Applications of smartphone-based near-infrared (NIR) imaging, measurement, and spectroscopy technologies to point-of-care (POC) diagnostics

Applications of smartphone-based near-infrared (NIR) imaging, measurement, and spectroscopy technologies to point-of-care (POC) diagnostics 基于智能手机的近红外成像、测量和光谱技术在护理点诊断中的应用 1 2 equal first-author 3 4 equal 5 equal 1 6 corresp Sheng ZHANG, szhang1984@nit.zju.edu.cn Sheng ZHANG, szhang1984@nit.zju.edu.cn szhang1984@nit.zju.edu.cn https://orcid.org/0000-0001-5599-6261 Ningbo Research Institute, Zhejiang University, Ningbo315100, China Ningbo Research Institute, Zhejiang University, Ningbo315100, China Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo113-8656, Japan Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo113-8656, Japan Institute of Combined Injury, State Key Laboratory of Trauma, Burn and Combined Injury, College of Preventive Medicine, Third Military Medical University, Chongqing400038, China Institute of Combined Injury, State Key Laboratory of Trauma, Burn and Combined Injury, College of Preventive Medicine, Third Military Medical University, Chongqing400038, China Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown02129, USA Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown02129, USA Division of Biomedical Engineering, Renal Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Cambridge02139, USA Division of Biomedical Engineering, Renal Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Cambridge02139, USA State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou310027, China State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou310027, China The role of point-of-care (POC) diagnostics is important in public health. With the support of smartphones, POC diagnostic technologies can be greatly improved. This opportunity has arisen from not only the large number and fast spread of cell-phones across the world but also their improved imaging/diagnostic functions. As a tool, the smartphone is regarded as part of a compact, portable, and low-cost system for real-time POC, even in areas with few resources. By combining near-infrared (NIR) imaging, measurement, and spectroscopy techniques, pathogens can be detected with high sensitivity. The whole process is rapid, accurate, and low-cost, and will set the future trend for POC diagnostics. In this review, the development of smartphone-based NIR fluorescent imaging technology was described, and the quality and potential of POC applications were discussed. 护理点（POC）诊断在公共卫生中起到非常重要的作用。在智能手机的支持下，POC诊断技术得到极大的改进。不仅仅是因为手机在世界范围内的大量使用，而且还得益于其日益增强的成像/拍照功能。智能手机结合近红外成像、测量和光谱技术，可以高灵敏度地检测病原体。整个过程快速、准确、成本低廉，将成为POC诊断的未来趋势。本文综述了基于智能手机的近红外荧光成像技术的发展，并对其应用和潜力进行了讨论。 Point-of-care (POC) diagnostics Near-infrared (NIR) fluorescent imaging Aggregation-induced emission (AIE) Smartphone-based imaging Fluorescent probe 护理点（POC）诊断 近红外成像 光谱技术 病毒检测 智能手机 荧光成像 1 Introduction 1 The timeline of smartphone usage in diagnostics was included in a recent review paper ( Ventola, 2014 Ventola, 2014 ). In 2002, the Blackberry was introduced as the first mobile device with the capacity for communication and computing ( Yoo, 2013 Yoo, 2013 ). Subsequently, the first-generation iPhone (Apple) was launched in January 2007, and smartphones with the Android operating system (Google) in October 2008 ( Yoo, 2013 Yoo, 2013 ). Smartphones are readily available and their use has become pervasive across the world. According to data from the International Telecommunication Union (ITU), by the end of June 2017, the average number of mobile cellular subscriptions per 100 people was over 100 globally, 249 in Hong Kong Special Administrative Region, China, and 12 in South Sudan, Africa. In the medical field, it was reported that 87% of doctors use smartphones or tablet devices in their workplace, and 85% of medical school staff, 90% of medical residents, and 85% of medical school students use mobile devices in clinical settings at hospitals or in the classroom ( Ventola, 2014 Ventola, 2014 ). Smartphones and other mobile devices are valuable at the point-of-care (POC) because of their communication capabilities, information resources, and clinical software applications. The opportunity to apply smartphones to the POC has resulted from not only their large number and fast spread across the world but also their improved imaging/diagnostic functions ( Breslauer et al., 2009 Breslauer et al., 2009 ; Smith et al., 2011 Smith et al., 2011 ; Rateni et al., 2017 Rateni et al., 2017 ). As a tool, the smartphone is regarded as a part of a compact, portable, and low-cost system for real-time POC, even in locations with few resources, such as a remote mountain village. With the help of the smartphone networks, community health workers at POC can share information on initial diagnostic tests and treatment with expert providers located far away. Improved functions of smartphones also promote their usage at POC. Smartphones provide various types of software applications with diverse functions including information and time management using notes, health record maintenance and access, reference and information gathering using textbooks, and even patient monitoring using collected clinical data, for use at the POC ( Ventola, 2014 Ventola, 2014 ). Recently, smartphones have been applied as an imaging modality to visualize tissues or even cells for diagnosing disease using visible light, green fluorescent proteins (GFPs), and near-infrared (NIR) light ( Zhu et al., 2011b Zhu et al., 2011b , 2012 2012 ; Chung et al., 2018 Chung et al., 2018 ). This demonstrates that the usage of smartphones at the POC has entered “the deep-water area.” However, no previous papers have systematically evaluated the effectiveness of smartphones in the field of diagnostics through the imaging techniques which have become possible by the incorporation of newly developed hardware such as smartphone spectrometers, or the smart usage of the original smartphone imaging tools. In this review, we pointed out that there is a growing trend in the development of smartphone-based NIR imaging, measurement, and spectroscopy to improve the quality and applicability of POC. The techniques of NIR imaging, measurement, and spectroscopy present exciting opportunities for the following reasons. Smartphones are widespread, even in poor areas of the world, and are an indispensable platform for the promotion of POC. As a hand-held device, the size of any associated opto-mechanical attachments should be comparable to that of the smartphone and provide advantages in manipulating test samples in microstructures (microfluidic devices). Therefore, attachments to the smartphone are always very compact, with some being only a few hundred micrometers having been fabricated as a “lab on a smartphone” using microfabrication techniques. There is one problem arising from the use of microfluidic devices: viruses are transparent compared to the many materials such as polydimethylsiloxane (PDMS) commonly used for such devices ( Männik et al., 2012 Männik et al., 2012 ; Wu et al., 2015b Wu et al., 2015b ). Thus, it was difficult to distinguish viruses from the background because of interference between the boundaries of the microfluidic device and bacteria. In this case, phase-contrast imaging or fluorescent imaging with auto-fluorescence may not provide sufficient contrast, and the NIR spectral range provides a reliable tool for clear imaging ( Männik et al., 2012 Männik et al., 2012 ; Wu et al., 2015a Wu et al., 2015a ). The wavelength ranges of 700‒900 nm (NIR-I) and 1000‒1700 nm (NIR-II) have been acknowledged as biological windows for NIR fluorescence imaging ( Fig. 1 Fig. 1 ). Owing to the extremely low autofluorescence and absorption of water, oxygenated/deoxygenated blood, fatty tissue, and organisms, NIR can minimize background interference, and improve tissue depth penetration and image sensitivity without relying on invasive ionizing radiation ( Frangioni, 2003 Frangioni, 2003 ). 1;2; A hardware system for NIR imaging may be realized by the application of fluorescent smartphones, which have been well established as a low-cost and more compact platform. The imaging quality or performance of NIR-based cell-phones will be greatly enhanced, because NIR imaging has advantages such as low auto-fluorescence (low background) and a low absorption rate (deeper tissue penetration) compared to visible light or fluorescence ( Zhou et al., 2016 Zhou et al., 2016 ). If probes are available, bacteria can be observed using a smartphone simply by inserting a test sample into the light path of the smartphone-based microscope system. There have been tens of such systems developed for POC diagnosis through white light or visible fluorescence. There is thought to be almost no technical barrier between the application of visible fluorescent imaging and NIR imaging. In a lab-based system, manufacturers need only to change the blue/green light-emitting diodes (LEDs) to LEDs-emitting light at the NIR spectral range. Cheap LEDs, thin-film filter membranes, externally applied lenses for magnification or light collimation, a sample holder, and so on, could be purchased from online shops, and the total cost could be only tens of US dollars. Indocyanine green (ICG) was the first NIR fluorescent dye approved for use in the human clinical field by the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) ( Zhou et al., 2016 Zhou et al., 2016 ). Since then, more than 17 types of NIR organic and inorganic (nanoscale) fluorophores have been developed for preclinical or clinical research in the field of biomedical engineering using lab-based imaging systems ( Hong et al., 2017 Hong et al., 2017 ). If a smartphone-based platform for NIR fluorescent imaging is established, it can be regarded as a lab on a smartphone, so there is no technical barrier to applying the well-developed NIR fluorophores for biomedical engineering on the platform. The application of NIR fluorophores to a smartphone-based platform for the observation of an extracted sample should be a non-invasive method. Therefore, the application of such a platform should greatly promote the use of different types of NIR fluorophores in specific testing fields, such as the observation of bacteria and viruses ( Qi et al., 2016 Qi et al., 2016 ). Since antimicrobial resistance is increasing, more accurate methods are needed to diagnose bacterial infections and for use as tools for preclinical studies ( Heuker et al., 2016 Heuker et al., 2016 ). The development of a smartphone-based system with the application of NIR fluorophores should further promote research on the observation of bacteria or viruses, even at the nanoscale level ( Wei et al., 2013 Wei et al., 2013 ), and eventually improve the performance of the platform as a precise diagnostic tool at the POC. Here, we systematically describe the development flow and building blocks of smartphone-based NIR fluorescent imaging and its application to POC, from the basic techniques to the final goals ( Fig. 2 Fig. 2 ). To state the flow and trends clearly, we will cover basic research such as fundamental studies on visible/NIR fluorophores, the development of smartphone systems for observation, promising techniques that will increase the usage of smartphone-based NIR imaging and establish its applicability, and applied research in the biomedical field which will eventually open the door to its widespread adoption. 2 Development of the techniques of NIR fluorescent imaging 1 2.1 Lab-based systems for NIR imaging 2 NIR imaging techniques have been used in the field of biomedical engineering with or without NIR fluorophores ( Troyan et al., 2009 Troyan et al., 2009 ; Chen et al., 2014 Chen et al., 2014 ; Dias, 2015 Dias, 2015 ; Zhu and Sevick-Muraca, 2015 Zhu and Sevick-Muraca, 2015 ; Hong et al., 2017 Hong et al., 2017 ). Many kinds of commercial NIR imaging devices have emerged. In systems without NIR fluorophores, because the interaction of NIR lights with deoxyhemoglobin and oxyhemoglobin is stronger than that with water, lipids, and so on, there is also a contrast between the deoxyhemoglobin and oxyhemoglobin. NIR spectroscopy (NIRS) systems were conventionally used for the measurement of physiological parameters such as tissue oxygenation changes ( Vaithianathan et al., 2004 Vaithianathan et al., 2004 ; Kim et al., 2011 Kim et al., 2011 ). The historical background of NIRS, NIR light propagation in tissues, the basic law for measurements, kinds of NIRS systems, NIRS treatment algorithms, and NIRS instrumentation have been summarized by Dias (2015) Dias (2015) . LEDs or laser diodes (LDs) have been used extensively as light sources, and LDs may have high tissue penetration ( Rolfe, 2000 Rolfe, 2000 ). For a higher contrast between hemoglobin and oxyhemoglobin, it was recommended to choose a combination of light sources with wavelength of 760 or 690 nm paired with 830 nm ( Strangman et al., 2003 Strangman et al., 2003 ). There are various kinds of detectors of reflections, such as the large-sized photomultiplier tube (PMT), the avalanche photodiode (APD), the silicon photodiode (SPD), and the small-sized photodiode OPT101 ( Achigui et al., 2008 Achigui et al., 2008 ; Zhang et al., 2009 Zhang et al., 2009 ; Safaie et al., 2013 Safaie et al., 2013 ). NIR fluorescence imaging is regarded as an "emerging biomedical imaging modality" with high spatial resolution and increasing tissue penetration depths using fluorescent labels of molecular or functional reporters ( Hong et al., 2017 Hong et al., 2017 ). NIR light excites the fluorescent labels within the tissues, and the generated fluorescence is imaged to determine the distribution of the imaging reagent ( Zhu and Sevick-Muraca, 2015 Zhu and Sevick-Muraca, 2015 ). The components of lab-based NIR fluorescence imaging devices for ICG imaging were reviewed by Zhu and Sevick-Muraca (2015) Zhu and Sevick-Muraca (2015) . The core components include light sources for the excitation of fluorescent agents, optical filters, and detectors. There are more than ten types of devices for ICG imaging, and light sources may be LDs, LEDs, or xenon (the filtered lamp sources), with excitation wavelengths ranging from 760 to 806 nm ( Zhu and Sevick-Muraca, 2015 Zhu and Sevick-Muraca, 2015 ). Interference filters are used to cut out noise such as weak fluorescent signals and ambient light ( Zhu et al., 2010 Zhu et al., 2010 ). In previous studies, filters such as an 800-nm long-pass filter (FF01-800/LP-25, Semrock, Inc., USA) have been applied ( Michael, 2010 Michael, 2010 ). The light incident angle is critical for maximal spectral performance and should be close to the angle of normal incidence ( Michael, 2010 Michael, 2010 ). Collimation optics have been designed for reducing excitation light leakage ( Zhu et al., 2010 Zhu et al., 2010 ). Charge-coupled device (CCD) detectors are mostly used for fluorescence imaging ( Zhu and Sevick-Muraca, 2015 Zhu and Sevick-Muraca, 2015 ). 2.2 Development of NIR fluorophores for lab/clinical use 2 The developed NIR fluorophores include cyanine dyes, porphyrin derivatives, phthalocyanines, squaraine, and boron dipyrromethane analogues, as reviewed by Luo et al. (2011) Luo et al. (2011) . The FDA approved the first NIR fluorophore, ICG, for clinical use more than half a century ago ( Fig. 1 Fig. 1 e), followed by methylene blue (MB; emission about 700 nm). ICG binds to cell plasma protein and has absorption and emission peaks of around 807 and 822 nm, respectively ( Schaafsma et al., 2011 Schaafsma et al., 2011 ). IRDye800CW has been approved for NIR imaging in clinical trials. Luo et al. (2011) Luo et al. (2011) introduced a cyclohexenyl substitution and two amphipathic N -alkyl side chains into ICG. The modified NIR fluorophore can be used not only for NIR imaging but also for targeting cancer-cell mitochondria, chemotherapy, photodynamic therapy (PDT), and photo-thermal therapy (PTT) (IR-780, IR-808DB, IR-7) ( Tan et al., 2017 Tan et al., 2017 ). Similar to cyanine dye IR-780, another NIR dye Pz-247 from porphyrin derivatives has also been identified with tumor-preferential accumulation, which is very useful for cancer-selective detection and early diagnosis ( McAuliffe et al., 2017 McAuliffe et al., 2017 ). Tang et al. (2015) Tang et al. (2015) reported a series of interesting NIR probes with the novel property of aggregation-induced emission (AIE) ( Ding et al., 2013 Ding et al., 2013 ). They emit NIR fluorescence only when aggregated (e.g., molecule 2-(4-bromophenyl)-3-(4'-(dimethylamino)-biphenyl-4-yl) fumaronitrile (BDABFN)). Hong et al. (2017) Hong et al. (2017) presented a list of fluorophores, including 17 common fluorescent reagents for NIR-I and NIR-II. As mentioned before, NIR-I refers to the traditional NIR window (700‍‒‍900 nm), and NIR-II is the NIR window ranging from 1000 to 1700 nm ( Zhao et al., 2018 Zhao et al., 2018 ). CH1055-PEG and IR1061 are examples of NIR-II fluorophores. Apart from small organic molecules, different kinds of elaborate inorganic nanomaterials were also used as NIR fluorophores, such as quantum dots (QDs), single-walled carbon nanotubes, and rare-earth nanoparticles. Both organic and inorganic fluorophores have been applied to target specific tissues such as cartilage or bone, liver, and tumors ( Shu et al., 2009 Shu et al., 2009 ; Pansare et al., 2012 Pansare et al., 2012 ; Hyun et al., 2014 Hyun et al., 2014 , 2015 2015 ; Antaris et al., 2016 Antaris et al., 2016 ; Shcherbakova et al., 2016 Shcherbakova et al., 2016 ). The use of tissue-specific fluorophores is regarded as a key technique to distinguish diseased from vital tissues ( Chen et al., 2008 Chen et al., 2008 ; Neuman et al., 2015 Neuman et al., 2015 ). Owens et al. (2016) Owens et al. (2016) discussed the principle of the use of two distinct imaging channels of NIR light from 650 to 900 nm to achieve tissue-specific fluorescent imaging. Optical filters can be applied to detect two NIR wavelengths (700 nm for normal, and 800 nm for diseased tissues) excited by light sources or visible light separately or simultaneously. Based on these considerations, various methods have been proposed to achieve tissue-specific NIR imaging. For example, the vasculature may be leaky in tumor tissues but non-permeable in normal tissues, and a contrast agent can enter the tumor tissue selectively ( Owens et al., 2016 Owens et al., 2016 ). Specific expression of surface biomarkers of cancer cells is also applicable for selectively imaging tumor tissues, as these markers have been investigated extensively ( Dinjaski et al., 2014 Dinjaski et al., 2014 ; Tang et al., 2015 Tang et al., 2015 ). A method called "activatable targeting" has also been considered ( Owens et al., 2016 Owens et al., 2016 ). The fluorescence of the NIR fluorophore is quenched using methods such as pH-sensitive moieties when the agents pass through the body, and becomes activated by specific microenvironmental factors such as pH, only after arrival in the diseased tissues ( Urano et al., 2008 Urano et al., 2008 ). The recombination of NIR fluorescent proteins with viruses has been tested for tumor targeting. Sakuda et al. (2019) Sakuda et al. (2019) developed a bioimaging system by combining the vesicular stomatitis virus (VSV) with an NIR fluorescent protein called Katushka, to form the recombinant VSV-Katushka. VSV is bound to osteosarcoma cells specifically because tumor cells do not have the antiviral interferon signaling pathways ( Sakuda et al., 2019 Sakuda et al., 2019 ). Using the osteosarcoma cell-specific agent, the tumor margins were demonstrated as a fluorescent area for resection ( Sakuda et al., 2019 Sakuda et al., 2019 ). 3 Smartphones as an imaging tool for POC: from white light to fluorescence 1 As an interface device, smartphones have been used as platforms to control portable diagnostic systems such as NIRS systems ( Watanabe et al., 2016 Watanabe et al., 2016 ). In such systems, the smartphone is connected to a system containing three built-in NIR LEDs and a single silicon p-type/intrinsic/n-type (PIN) photodiode. The system (PocketNIRS Duo and PocketNIRS HM) was used as a monitor for brain and arm muscle hemodynamics and oxygenation based on the modified Beer-Lambert law ( Watanabe et al., 2016 Watanabe et al., 2016 ). The smartphone is a promising diagnostic tool at POC based on the abovementioned advantages. In contrast to systems using the smartphone in a control function, other techniques have been developed for the efficient use of smartphone cameras. Smartphones equipped with complementary metal oxide semiconductor (CMOS) detectors can collect images using either visible or NIR light ( Ghassemi et al., 2017 Ghassemi et al., 2017 ). Imaging using white-light is the most basic imaging modality of a smartphone-based microscope system. It is generally used together with a combination of blue, green, or red color imaging. The mobile phone is opto-mechanically attached to the microscopy system. For bright-field imaging, a cheap microscope eyepiece, an objective, and a white LED for illumination are necessary. A field-of-view of about 180-μm diameter with a spatial resolution of about 1.2 μm was realized using a 0.85 numerical aperture (NA) 60× Achromat objective and a 20× eyepiece ( Breslauer et al., 2009 Breslauer et al., 2009 ). Smartphone-based imaging techniques based on visible light may afford lessons for the development of the device for NIR fluorescent imaging. Applications of the cameras involved in white-light, GFP, and NIR light were discussed below. 3.1 Smartphone-based imaging using visible-wavelength fluorescence 2 Smartphone-based fluorescent imaging of cells, viruses, and molecules has been realized using a combination of external and built-in lenses. Normally, LEDs or LDs are added to the white LEDs, and filter membranes are used to cut out noise or for a dark field. Using a three-dimensional (3D) printing technique, Kühnemund et al. (2017) Kühnemund et al. (2017) established a system which is light-weight and capable of being attached opto-mechanically to a mobile phone. To obtain fluorescence and bright-field images, the optical attachment is equipped with laser diodes at 532 and 638 nm, and a white LED. The sample holder consists of motorized stages for x -, y -, and z -movement for focus adjustment ( Kühnemund et al., 2017 Kühnemund et al., 2017 ). Zhu et al. (2012) Zhu et al. (2012) developed a method to detect Escherichia coli using a smartphone. A filter and an external lens were placed between the sample and the built-in lens of the smartphone. E. coli in contaminated water and food was captured by anti- E. coli O157:H7 antibodies immobilized onto a capillary tube with an inner diameter of about 100 μm, and streptavidin-conjugated QDs with fluorescence emission were introduced as a fluorescent reporter. Ultra-violet (UV) LEDs were butt-coupled to the capillary tube, and the liquid sample in the tube acted as a light waveguide along the tube. Because the light is perpendicular to the detection path, an inexpensive long-pass glass filter can be used to remove the scattered light and obtain a dark-field background ( Zhu et al., 2012 Zhu et al., 2012 ). Wide-field fluorescent imaging (about 81 mm 2 ) on a Sony-Erickson U10i Aino smartphone with a spatial resolution of about 20 μm was also established by Zhu et al. (2011a Zhu et al. (2011a , 2011b) 2011b) . Imaging of a large sample area and volume can be realized using such a system. In this system, the excitation light is also perpendicular to the detection path, and therefore, an absorption filter is sufficient for the creation of a dark-field background. To achieve wide field, they placed a lens directly above the built-in smartphone lens to create a de-magnification of 3.2 for imaging of the fluorescent samples ( Zhu et al., 2011c Zhu et al., 2011c ). Zhu et al. (2011a) Zhu et al. (2011a) developed fluorescent imaging cytometry for bodily fluids and water samples on a Sony-Erickson U10i Aino smartphone. The optofluidic attachment consists of a flat microfluid chip, LEDs directly butt-coupled to the chip, an absorption filter, and an external lens for magnification located right above the built-in lens of the smartphone. Fluorescently labeled samples were injected into the microfluid chip by an external syringe pump. Videos of the labeled cells/particles were obtained at a frame rate of about 7 fps (frames per second), and the number of targets was counted through the videos. Based on the principle of optofluidic wave-guiding, the excitation light is confined inside the microchannel, and a simple plastic absorption filter can be used to cut out the scattered pump photons because the fluorescent light is perpendicular to the detection path ( Zhu et al., 2011b Zhu et al., 2011b ). Infection with human cytomegaloviruses (HCMV), which range from 150 to 300 nm in diameter, may lead to virus-associated birth defects ( Huang and Johnson, 2000 Huang and Johnson, 2000 ; Haspot et al., 2012 Haspot et al., 2012 ). To detect single nanoparticles or viruses using a smartphone-based microscope, Wei et al. (2013) Wei et al. (2013) applied a high-power laser diode (75 mW) with an excitation center at 450 nm to strengthen the fluorescence arising from nanoparticles (sizes ranging from 10 μm to 100 nm) or labeled HCMV. The incidence angle of the excitation light was 75°. A long-pass filter was used to cut out the scattered excitation light. Focus adjustment was realized by the application of a miniature dovetail stage (DT12, Thorlabs). Fluorescent images with 2× magnification were obtained by using a combination of the external lens and the lens of the smartphone ( Wei et al., 2013 Wei et al., 2013 ). 3.2 Smartphone-based diagnosis using NIR spectral analysis 2 Many smartphone-based diagnoses using NIR light are now emerging ( Fig. 3 Fig. 3 ). There is even a miniaturized NIR grating spectrometer developed specifically as a light source for smartphone applications at Fraunhofer intelligent pain management system (IPMS), covering wavelengths from 950 to 1900 nm ( Fig. 3 Fig. 3 a) ( Pügner et al., 2016 Pügner et al., 2016 ). A smartphone-based system with an NIR illuminant has been used in pulse oximetry to monitor blood oxygenation levels ( Kanva et al., 2014 Kanva et al., 2014 ; Scott et al., 2014 Scott et al., 2014 ; Bui et al., 2017 Bui et al., 2017 ; Holz and Ofek, 2018 Holz and Ofek, 2018 ; Vanegas et al., 2018 Vanegas et al., 2018 ). Haemoglobin is an important component of blood. Oxygen binds with haemoglobin to form oxyhaemoglobin, making it soluble in blood. To measure oxygenation levels, smartphone camera oximetry must be capable of distinguishing reflections from oxyhaemoglobin and deoxyhemoglobin. NIR illuminants based on Beer-Lambert law typically are used to detect reflections from deoxyhemoglobin ( Chance et al., 1988 Chance et al., 1988 ; Kim and Liu, 2007 Kim and Liu, 2007 ). Hussain et al. (2018) Hussain et al. (2018) designed an optical device attached to a Sony Xperia E3 smartphone (Android 4.4.3 lollipop) for the detection of light from the visible to near infra-red spectroscopic regions ( Fig. 3 Fig. 3 b). The ambient light sensor of the smartphone was used to sense the spectra in the range of 350‒1000 nm, instead of applying the smartphone CMOS camera. The optical system contains two types of LEDs as light sources, with peak emission wavelengths of 510 and 880 nm, respectively. Light filters were not used because the cost is higher than that of LEDs with a specific wavelength. The two external LEDs were chosen based on the results of spectrophotometric analysis of the two samples, iron (II) and phosphate solutions, using a standard laboratory-grade spectrophotometer (UV-3600 Plus UV-Vis-NIR Spectrophotometer, Shimadzu, Japan). The LEDs were powered by the internal battery of the smartphone through a USB-OTG cable. The LED light beam was collimated by an external collimating lens and propagated through the sample solution. The ambient light sensor then detected the intensity of the transmitted light beam via a focusing lens, and the sample concentration was measured based on the Beer-Lambert law of absorption. Using this smartphone-based optical system, samples with various concentrations were measured correctly according to device calibration by Hussain et al. (2018) Hussain et al. (2018) . There are also commercially available smartphones for food scanning, in which an NIR camera or spectrometer has been installed ( Fig. 3 Fig. 3 c). Liang et al. (2014) Liang et al. (2014) used a smartphone to detect microbial spoilage on ground beef under an 880-nm NIR LED. The specific scatter signals at various angles were associated with the E. coli concentration on the meat ( Fig. 3 Fig. 3 d). Antibodies were not applied in the study. Instead, Mie scattering was used as a parameter to demonstrate the bacterial concentration. E. coli tends to bind to fat cells. The averaged refractive index of pure lipids is 1.460 and the refractive index of the bacteria is 1.388. Both indexes are higher than that of water (1.327). Although the difference is small, pure lipids could be distinguished from the flat surface with the colonies of E. coli . Mie scattering is dependent on the angles of scatter detection and bacterial concentrations. Long et al. (2017) Long et al. (2017) established the system based on these principles. The LED was irradiated perpendicular to the meat surface and the camera of an iPhone 4S detected the signal angled at 15°‒60° from the incident light at different bacterial concentrations. Kaile and Godavarty (2019) Kaile and Godavarty (2019) developed an NIR smartphone-based imaging system to measure in vivo the hemoglobin-related oxygenation changes beneath the surface of the skin ( Fig. 3 Fig. 3 e). 3.3 Smartphone-based NIR fluorescent imaging 2 Compared to systems without the use of NIR fluorophores, smartphone-based fluorescence microscope systems are an emerging technology. Currently, only a small number of groups are conducting research in this field. The CMOS detector of a smartphone is said to be sensitive to light in the visible and NIR regions, but manufacturers usually use a short-pass filter to cut off NIR wavelengths ( Ghassemi et al., 2017 Ghassemi et al., 2017 ). Ghassemi et al. (2017) Ghassemi et al. (2017) removed the short-pass filter and evaluated the effectiveness of a smartphone-based system using NIR fluorescence imaging ( Fig. 4 Fig. 4 a). The external part of the system contained an LED (M780L3, Thorlabs, Inc., Newton, NJ, USA) with a 780-nm center wavelength as an external light source, an 800-nm short-pass filter (84-729, Edmund Optics, Barrington, NJ, USA) as an excitation filter, and a convex lens and diffuser to obtain uniform illumination. A long-pass emission filter was attached to the smartphone camera following the removal of the NIR blocking filter. ICG (Pulsion Medical Inc., Powell, OH, USA) was used as the primary fluorophore. It was injected into the channels of a 3D-printed tissue phantom or a Lewis rat model to determine the spectral sensitivity of the smartphone camera in the range of 700‒‍1100 nm. The quality of smartphone-based NIR fluorescence imaging was almost as good as that of a scientific CCD in the 3D-printed tissue phantom or the ICG-filled rodent femoral vasculature (Figs. 4 4 b and 4 4 c, a femoral artery bifurcation) ( Ghassemi et al., 2017 Ghassemi et al., 2017 ). This may have been the first experiment involving removal of the built-in filter of a smartphone. The study demonstrated that it is effective for NIR fluorescent imaging of the 3D-printed phantom and the ex vivo animal model. However, it is unclear whether the method and its application to in vivo imaging represent a strong trend in smartphone-based imaging as a POC task. Conservatively, it may be used only in a few high-end devices. Chen et al. (2014) Chen et al. (2014) recently compared the fluorescence imaging capabilities of NIR-enabled mobile phones with or without CCD, using phantom-based test methods, and found certain aspects of performance comparable to a scientific-grade CCD (Figs. 4 4 d and 4 4 e). They also verified the in vivo fluorescence imaging and tumor visualization capabilities of mobile phones ( Fig. 4 Fig. 4 f). Cost-effective, compact, and portable NIR mobile phones capable of wireless data transfer will inevitably fuel up the application of NIR imaging technologies in our daily life and healthcare systems ( Suresh et al., 2018 Suresh et al., 2018 ). Typical elements of lab-based and smartphone-based NIR fluorescent imaging are listed in Table 1 Table 1 . Components of smartphone-based fluorescent imaging are shown in Table 2 Table 2 . NIR: near-infrared; LED: light-emitting diode; CCD: charge-coupled device; CMOS: complementary metal oxide semiconductor; MEMS: micro-electro-mechanical system. 2 Element 2 Lab-based 2 Smartphone-based Representative item Reference Representative item Reference 3 Light source Laser light source Zhu and Sevick-Muraca, 2015 Zhu and Sevick-Muraca, 2015 LED (wavelength=780 nm) Ghassemi et al., 2017 Ghassemi et al., 2017 ; Wang et al., 2017 Wang et al., 2017 LED Zhu and Sevick-Muraca, 2015 Zhu and Sevick-Muraca, 2015 2 MEMS-based NIR grating (wavelength=950‒1900 nm) 2 Pügner et al., 2016 Pügner et al., 2016 Xenon Zhu and Sevick-Muraca, 2015 Zhu and Sevick-Muraca, 2015 3 Optical filter for image collection Bandpass filter Marshall et al., 2010 Marshall et al., 2010 ; Sevick-Muraca et al., 2008 Sevick-Muraca et al., 2008 ; Troyan et al., 2009 Troyan et al., 2009 Short-pass excitation filter Ghassemi et al., 2017 Ghassemi et al., 2017 Notch filter Marshall et al., 2010 Marshall et al., 2010 ; Sevick-Muraca et al., 2008 Sevick-Muraca et al., 2008 2 Long-pass emission filter 2 Ghassemi et al., 2017 Ghassemi et al., 2017 Cut-on filter Handa et al., 2009 Handa et al., 2009 ; Marshall et al., 2010 Marshall et al., 2010 3 Detector for fluorescent signals CCD Zhu and Sevick-Muraca, 2015 Zhu and Sevick-Muraca, 2015 3 8-bit CMOS sensor (NIR blocking filter removal) 3 Ghassemi et al., 2017 Ghassemi et al., 2017 Electron-multiplying CCD Zhu and Sevick-Muraca, 2015 Zhu and Sevick-Muraca, 2015 Intensified CCD Zhu and Sevick-Muraca, 2015 Zhu and Sevick-Muraca, 2015 NIR: near-infrared; LED: light-emitting diode; UV: ultra-violet; LD: laser diode; 3D: t hree-dimensional; QD: quantum dot. System Source type/emission wavelength Sampling/preparation Visible fluorescence NIR Reference Sony-Erickson U10i Aino TM ; Kodak Wratten Color Filter 12 for dark-field Blue and white LED (Digikey Corp.) Fluorescent bead or stained white blood cell/ Giardia lamblia Yes No Zhu et al., 2011a Zhu et al., 2011a Sony-Ericsson U10i Aino TM ; glass capillaries (inner diameter about 100 μm); long-pass glass filter for waveguide UV LEDs Fluorescent QD labeled Escherichia coli on the capillary surface Yes No Zhu et al., 2012 Zhu et al., 2012 Nokia smartphone PureView 808; long-pass filter FF01-500/LP-23.3-D, Semrock Blue LD centered at 450 nm (75° incidence angle) Green fluorescent polystyrene particle of 100 nm; fluorescently labeled human cytomegaloviruses Yes No Wei et al., 2013 Wei et al., 2013 Nokia Lumia 1020 smartphone; external lens; dual-band emission filter A white LED; LDs 532 nm at 75 mW and 638 nm at 180 mW, Mitsubishi Electric (about 75° incidence angle) Micron-sized DNA coils inside preserved cells and tissues Yes No Kühnemund et al., 2017 Kühnemund et al., 2017 Smartphone (no brand information); external lens; excitation filter; emission filter; Z -stage 8 LEDs Fluorescently labeled Giardia cysts Yes No Koydemir et al., 2015 Koydemir et al., 2015 Smartphone (no brand information); short-pass excitation filter; long-pass emission filter; zoom lens; convex lens; diffuser LED with 780 nm center wavelength 3D-printed phantoms; ex vivo rodent model No Yes Ghassemi et al., 2017 Ghassemi et al., 2017 4 Techniques for further promotion of smartphone-based measurement and spectroscopy 1 4.1 Optical fibers for smartphone-based spectral analysis 2 Long et al. (2017) Long et al. (2017) have developed an instrument, which used the rear-facing camera of a smartphone for investigations based on three biosensing modalities: light transmission for in vitro diagnostic (IVD) tests such as enzyme-linked immunosorbent assay (ELISA) assays, reflectance spectroscopy for IVD tests such as surface plasmon resonance (SPR) biosensing, and intensity spectroscopy for IVD tests such as fluorescence or chemiluminescent immunoassays (far-infrared ray (FIR), chemiluminescent immunoassay (CLIA)). Optical fibers, a photonic crystal biosensor (reflection mirror), lenses, and a green LED were assembled into a 3D-printed cradle (FormLabs Form 2 at a resolution of 50 μm) with precise optical alignment. One optical fiber was used to collect white light from the light source (the rear flash LED of a smartphone) for the transmission and reflectance modalities. For the intensity modality, the flash LED of the smartphone was turned off and a 532-nm laser diode (green light) was turned on. A collection lens was used to focus the light within the test sample for maximal fluorescence collection. After the light passed through the test sample, the other optical fiber was used to direct the light to the rear camera of the smartphone. In the study, the test samples were introduced to the light path using a microfluidic cartridge, and samples could be manipulated using an eight-channel multi-pipette ( Long et al., 2017 Long et al., 2017 ). 4.2 Cell separation/sorting techniques 2 To apply a smartphone-based system to NIR imaging, measurement, and spectroscopy at POC, there must be sophisticated supporting techniques such as methods for specific cell capture and cell sorting. Knowlton et al. (2017) Knowlton et al. (2017) developed a device capable of first separating cells based on magnetophoresis-based cytometry, and then imaging them. Cells can be separated according to their density in a paramagnetic medium using the method of magnetic focusing: cells are levitated at a height in the medium due to the resultant force of magnetic and buoyant forces. The buoyant forces are determined by the density of the cells relative to the density of the medium ( Mirica et al., 2009 Mirica et al., 2009 ; Nemiroski et al., 2016 Nemiroski et al., 2016 ; Knowlton et al., 2017 Knowlton et al., 2017 ). A 3D-printed microscopy system (FormLabs Form 1 at a resolution of 0.1 mm), containing a phone case, optical components, and emission filter holders, was applied. A Samsung Galaxy S6 was used because it offers manual focusing. Two nickel-plated neodymium-iron-boron (NdFeB) permanent magnets aligned along the longitudinal direction of the rear face of the smartphone and a glass capillary tube with the test sample were located between the two magnets. A gadolinium-based medium, Gadavist (Bayer, Whippany, NJ, USA), was used as a paramagnetic agent, and only cells levitating at the desired height, not those at the bottom of the micro-capillary or at a different height, were visible. Using this method, rare diseased cells could be separated from healthy cells within a large population. For example, ovarian cancer cells HeyA8 were distinguished from cells in diluted blood, because the two cell types had different densities and therefore were spatially separated at different heights with respect to the microscope focus in the magnetic field ( Knowlton et al., 2017 Knowlton et al., 2017 ). Koydemir et al. (2015) Koydemir et al. (2015) developed a smartphone-based fluorescent microscopy technique with a capturing method for the detection of cysts of the most common waterborne parasite, Giardia lamblia (Ceylan Koydemir and Ozcan, 2018). The waterborne transmission of parasitic protozoa can lead to outbreaks of human diseases ( Baldursson and Karanis, 2011 Baldursson and Karanis, 2011 ). Using porous filter membranes with a 5‍-μm pore size for capturing the cysts, Koydemir et al. (2015) Koydemir et al. (2015) designed a sample holder within the microscopy system to separate these bacteria from water samples of about 20 mL. Diluted stain (200 μL) was added to a water sample before the separation process. A Nokia Lumia 1020 smartphone was used ( Wei et al., 2013 Wei et al., 2013 , 2014 2014 ), and eight blue LEDs provided the external light source for even light distribution. A large field of view (FOV) of about 0.8 cm 2 was achievable. 4.3 Microfabrication techniques 2 Pügner et al. (2016) Pügner et al. (2016) developed an NIR grating spectrometer (micro-electro-mechanical system (MEMS) devices) for smartphone applications using sophisticated microfabrication techniques with lithography ( Fig. 3 Fig. 3 a). For applications in a mobile phone, the spectrometer should have a spectral range from 950 to 1900 nm with a resolution equal to or less than 10 µm. One nanometer is necessary for long-term wavelength stability. Further, the absorbance ( A ) value should be from 3.5 to 4.0 to ensure that the absorption measurements are suitable for smartphone analysis according to the Beer-Lambert law. As an inbuilt part of a smartphone, the device size should be smaller than 20 mm×20 mm×6 mm (thickness). A Czerny-Turner design was applied. The principle is as follows. A curved mirror is used to collimate the light passing through an entrance slit, and a rotatable grating is applied to diffract the collimated light. A second curved mirror is used to re-focus the diffracted light at an exit slit. Only light with a specific wavelength determined by the grating rotation angle can pass through the exit slit: lights with different wavelengths are eliminated because they are re-focused to a position other than the exit slit. MEMS devices were applied with a precisely defined layout. The entrance and exit slits were added to the MEMS chip. The two slits were located at defined positions with respect to a rotatable grating, taking into consideration the angles of incidence and diffraction. A resonant electrostatic-comb drive was used to actuate the MEMS devices ( Pügner et al., 2016 Pügner et al., 2016 ). 5 Promising fields of application towards POC and issues to be resolved 1 5.1 A potential application field: smartphone-based virus or bacteria detection 2 Smartphone-based detection of bacteria is regarded as a state-of-the-art method ( Gopinath et al., 2014 Gopinath et al., 2014 ). Lab-based methods have been developed to provide noninvasive visualization of virus infection in vivo, and NIR fluorescent imaging is effective for monitoring bacterial infections ( Foster et al., 2008 Foster et al., 2008 ; Dinjaski et al., 2014 Dinjaski et al., 2014 ; Pan et al., 2014 Pan et al., 2014 ). Here, NIR fluorophores used for virus imaging and related results are reviewed to evaluate the possible application of NIR fluorescent imaging to smartphone-based bacterial detection, although previous studies usually used the fluorophores for in vivo detection. Pan et al. (2014) Pan et al. (2014) labeled avian influenza H5N1 pseudotype virus (H5N1p) with NIR-emitting QDs to observe the distribution of this virus in the lungs of mice. Isomura et al. (2017) Isomura et al. (2017) have demonstrated that a recombinant rabies virus (RABV) expressing an NIR fluorescent protein iRFP720 (infra-red fluorescent protein 720) was suitable for NIR fluorescent imaging of RABV infection in nude mice. To monitor three bacterial species in the gastrointestinal tract (bacterial infection) in mice, Berlec et al. (2015) Berlec et al. (2015) constructed Lactococcus lactis , Lactobacillus plantarum , and E. coli expressing the NIR fluorescent protein iRFP713 ( Kleerebezem et al., 1997 Kleerebezem et al., 1997 ). The expression of iRFP713 in L. lactis and L. plantarum was constructed using the nisin-controlled gene expression (NICE) system. Take the construction of NIR fluorescent L. lactis as an example. The plasmid pNZ-iRFP713 in L. lactis NZ9000 (a host strain) contains copies of nisRK genes and enables binding of nisin (a 34-amino acid anti-microbial peptide). During the construction process, the nisA promoter (PnisA) (a promoter in the nisin gene cluster) on a plasmid was used to control the expression of the downstream gene ( iRFP713 ) ( Mierau and Kleerebezem, 2005 Mierau and Kleerebezem, 2005 ; Zhou et al., 2006 Zhou et al., 2006 ; Berlec et al., 2015 Berlec et al., 2015 ). A biliverdin assay developed by Berlec et al. (2015) Berlec et al. (2015) was used to construct the NIR fluorescent E. coli . A CP25 promoter in pGEM::CP25-iRFP713 plasmid, which was applied to control the transfection of iRFP713 ( Berlec and Štrukelj, 2014 Berlec and Štrukelj, 2014 ; Berlec et al., 2015 Berlec et al., 2015 ). As discussed above, NIR fluorophores have been used for the tracing of bacteria or viruses in vivo. However, a few studies have involved the application of smartphone-based NIR fluorescent imaging for bacteria or virus detection. The development of these types of fluorophores and investigation of the movement of viruses in vivo have been confirmed to be helpful in the elucidation of pathology. However, this may not have actual utility in smartphone-based NIR fluorescent imaging, which is used to survey extracted samples in the sample tray inserted into the compact microscope system ( Gopinath et al., 2014 Gopinath et al., 2014 ). Probes available for bacteria or virus imaging are listed in Table 3 Table 3 . NIR: near-infrared; VSV: vesicular stomatitis virus; QDs: quantum dots; iRFP: infra-red fluorescent protein. Type of bacteria/virus Probe Excitation wavelength (nm) Emission wavelength (nm) Condition for use Reference Lactococcus lactis , Lactobacillus plantarum , and Escherichia coli iRFP713 690 713 Stomach and small intestine in live mice Berlec et al., 2015 Berlec et al., 2015 Bacterial peptidoglycan of Mycobacterium smegmatis , Corynebacterium glutamicum , and Listeria monocytogenes NIR fluorogenic azide probe based on Si-rhodamines 655 668 No washing was necessary for staining Shieh et al., 2014 Shieh et al., 2014 E. coli eqFP670 605 670 Multi-color imaging using blue, orange, and NIR probes Wu et al., 2015a Wu et al., 2015a Avian influenza A virus (H5N1p) NIR-emitting QDs 750 Nuance FX multispectral imaging system (PerkinElmer) Pan et al., 2014 Pan et al., 2014 VSV Katsushka NIR fluorescent protein (recombinant VSV-K) 588 635 Evaluation by fluorescence microscopy (BZ-9000 microscope, Keyence) Sakuda et al.,2019 Sakuda et al.,2019 5.2 Development of fluorophores for virus-specific NIR fluorescent imaging 2 Some species of bacteria are harmful to human health, but some bacteria (or bacteria in the proper amount) are necessary for the normal physiological functions of human beings ( Zhao et al., 2015b Zhao et al., 2015b ). NIR probes that cannot specifically bind to one kind of bacteria may have no practical value for bacteria or virus detection in the field of POC. No NIR fluorophores have been developed for specific species of viruses. However, fluorescent imaging of a specific type of virus is possible according to a previous study, and this should become a trend for the testing of NIR fluorophores. Zhao et al. (2015a Zhao et al. (2015a , 2015b) 2015b) developed AIE luminogen (AIEgen) 4 for the evaluation of bacterial susceptibility and antibiotics screening. Only after the formation of micelles at high concentrations was fluorescence turned on. In other words, fluorescence of probes at low concentrations was not observed. Based on this phenomenon, different bacteria may be distinguished. For example, after the addition of the antibiotic kanamycin sulfate (KANA), the growth of E. coli was inhibited, but KANA-resistant E. coli still grew extensively. As a result, strong fluorescence of KANA-resistant E. coli but low fluorescence of E. coli was obtained ( Zhao et al., 2015b Zhao et al., 2015b ). Therefore, such an AIEgen could be used to distinguish the two types of bacteria. 5.3 A potential application field: smartphone-based virus or bacteria detection 2 NIR fluorophores have proved to afford an attractive spectral choice for bacterial imaging. To provide guidelines for the engineering of fluorescent fusions in bacteria, Wu et al. (2015a) Wu et al. (2015a) studied the combination of NIR fluorescent proteins with blue and orange fluorescent proteins for the observation of the nucleoid and division proteins of bacteria in vitro, respectively. The nucleoid proteins of E. coli , HU and Lac I, and the cell division proteins, Min D and Fts Z, were fluorescently labeled to test brightness and photo-stability, respectively. An NIR protein, eqFP670 (excitation/emission wavelength: 605 nm/670 nm), was used for E. coli imaging, because eqFP670 was said to be the most red-shifted protein and has high photo-stability ( Shcherbo et al., 2010 Shcherbo et al., 2010 ). As a result, eqFP670 is highly applicable to multi-color imaging, because it shows high brightness and photo-stability for the NIR spectral range with little cell photo-damage ( Wu et al., 2015a Wu et al., 2015a ). Wu et al. (2015a) Wu et al. (2015a) , therefore, pointed out that NIR fluorescent proteins such as eqFP670 could be used as a third or fourth color other than blue or orange, etc. for the observation of bacteria inside microstructures or microfluidic devices such as PDMS devices ( Wang et al., 2010 Wang et al., 2010 ; Hol and Dekker, 2014 Hol and Dekker, 2014 ). 5.4 Development of wash-free NIR fluorophores 2 A common strategy for a target probe is to conjugate a probe to antibodies that are specific for a targeted analyte or biomarker of pathogens. However, the non-specific attachment or unbinding free probe would also cause a high background noise signal and decrease detection sensitivity. To avoid this, it is necessary to wash the samples before fluorescence imaging. The washing process is always necessary during the process of cell transfection, which increases the experimental procedures. Further, the washing process may lead to the loss of bacteria and thus decrease the accuracy of quantification analysis ( Zhao et al., 2015b Zhao et al., 2015b ). Some probes for fluorescent imaging with a visible wavelength developed using the strategy of AIE emit faintly when they are dissolved in water, but the fluorescence becomes strong when the probes are aggregated ( Luo et al., 2001 Luo et al., 2001 ; Mei et al., 2015 Mei et al., 2015 ). Therefore, the washing step is not necessary. Shieh et al. (2014) Shieh et al. (2014) developed a wash-free NIR fluorogenic azide probe based on biorthogonal chemistry ( Sletten and Bertozzi, 2009 Sletten and Bertozzi, 2009 ), and a new class of cyclooctyne-functionalized d-amino acids was incorporated into the probe (emission 670 nm) for the observation of peptidoglycan of the bacteria Mycobacterium smegmatis , Corynebacterium glutamicum , and Listeria monocytogenes . The development of NIR fluorogenic probes with emission wavelengths above 700 nm was thought to be challenging. The trigger for successful development was that Xiao's group found that emission in the NIR region could be achieved by replacing the oxygen atom in the xanthene moiety of tetramethylrhodamine with Si-rhodamine ( Fu et al., 2008 Fu et al., 2008 ; Koide et al., 2011 Koide et al., 2011 ). Shieh et al. (2014) Shieh et al. (2014) identified azide-functionalized Si-rhodamines that enhanced fluorescence quantum yield upon triazole formation using computational methods. The probe bound specifically to the terminal alkynes on the mammalian cell surface. Washing of the free probes after transfection was not necessary, because the excess probes did not fluoresce during nonspecific binding events ( Lukinavičius et al., 2013 Lukinavičius et al., 2013 ). 6 Conclusions 1 A smartphone-based microscope/imaging system has been developed as a platform for diagnosis at POC. Sooner or later, it is only natural that the techniques of NIR fluorescent imaging will be combined with smartphone technologies to afford rapid, accurate, and low-cost services related to telemedicine. In this paper, we analyzed several aspects of these trends. As a popular hand-held platform available on almost any occasion in daily life in developed and developing countries, smartphones draw a lot of attention, and the convenience they provide promotes their widespread use in research involving imaging and diagnostic techniques. Smartphone-based platforms have been developed for imaging cells, bacteria, and viruses through white light and/or fluorescence. Smartphone systems have also been used in NIR imaging to investigate the oxygenation levels in tissues. The establishment of smartphone-based microscope systems has benefited greatly from the design concepts of lab-based systems and their components such as LED light sources, light filters, collimation optics, magnifying lenses, sample trays, and XYZ stages. Researchers have paid attention to the layout due to the compact size of the smartphone, and the necessity of convenience for use at POC. For example, the introduction of cell capturing or sorting equipment. The development of smartphone-based imaging systems for POC provides a robust platform for the incorporation of NIR fluorescent imaging. Smartphone-based NIR fluorescent imaging is still an emerging technology, but the performance of the built-in camera has been confirmed to be effective in capturing NIR fluorescent images. Based on the source and the development trend of this technology, we demonstrated that smartphone-based non-invasive bacteria/virus/cell detection should be the shape of things to come. Therefore, in this review, several issues were discussed including the incorporation of microfabrication techniques, the development of brighter/species-specific/wash-free NIR fluorophores, and smartphone-based telemedicine ( Table 4 Table 4 ). All in all, based on the considerable progress in the development of NIR fluorophores, mobile phones, and other peripheral technologies, a smartphone-based microscope system should be a key technology that connects the functionality of NIR fluorescent imaging with POC. Technique Effectiveness Reference Grating for smartphone 1. Spectral coverage from the visible to near-infrared spectral regions; 2. High wavelength resolution; 3. Low cost Pügner et al., 2016 Pügner et al., 2016 ; McGonigle et al., 2018 McGonigle et al., 2018 Three-dimensional (3D) printing techniques 1. Low-cost fabrication for device housings; 2. 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All authors have read and approved the final manuscript and, therefore, have full access to all the data in the study and take responsibility for the integrity and security of the data. This work was supported by the National Natural Science Foundation of China (No. 81773352) and the China Scholarship Council (No. 201703170071). 10.1631/jzus.B2000388 1673-1581（2021）03-0171-19 2020-07-17 2020-11-05 2021-01-27 2021-03-15 2022-05-09 © Zhejiang University Press 2021 Wenjing HUANG, Shenglin LUO, Dong YANG, and Sheng ZHANG declare that they have no conflict of interest. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Informed consent was obtained from all patients for being included in the study. Additional informed consent was obtained from all patients for whom identifying information is included in this article. Compliance with ethics guidelines 浙江大学学报（英文版）（B辑：生物医学和生物技术） 3 22