Recent trends in smartphone-based detection for biomedical applications: a review

Abstract

Smartphone-based imaging devices (SIDs) have shown to be versatile and have a wide range of biomedical applications. With the increasing demand for high-quality medical services, technological interventions such as portable devices that can be used in remote and resource-less conditions and have an impact on quantity and quality of care. Additionally, smartphone-based devices have shown their application in the field of teleimaging, food technology, education, etc. Depending on the application and imaging capability required, the optical arrangement of the SID varies which enables them to be used in multiple setups like bright-field, fluorescence, dark-field, and multiple arrays with certain changes in their optics and illumination. This comprehensive review discusses the numerous applications and development of SIDs towards histopathological examination, detection of bacteria and viruses, food technology, and routine diagnosis. Smartphone-based devices are complemented with deep learning methods to further increase the efficiency of the devices. Graphical Abstract.

Keywords: Deep learning; Diagnostics; Fluorescence imaging; Immunoassay; Optical microscopy; Smartphone.

Graphical Abstract

Fig. 1

Smartphone-based imaging system. a The BLIPS lens-based smartphone microscopic system along with the acquired images of human liver tissue section obtained and comparative image of the bright-field microscope. Reproduced from Cesaretti et al. [20], with permission from Jhon Wiley and Sons (copyright 2016). b The real-time image acquisition of cancer cells. Reproduced from Skandarajah et al. [21], with permission from PLOS (copyright 2017)

Fig. 2

a The CellScope system with 3D-printed parts for fluorescence imaging. The fluorescence (green) and bright-field (red) images of TB pathogen, respectively. Reproduced from Chang et al. [33], with permission from Springer Nature (copyright 2012). b The PRODIGI system and its components for real-time autofluorescence imaging of wound, collagen (green), and bacteria (red). Reproduced from Wu et al. [34], with permission from Society of Photo-Optical Instrumentation Engineers (SPIE) (copyright 2014)

Fig. 3

a The smartphone-based immunoassay platform for the virus detection integrated with the microfluidic cassette containing the preloaded enzymes and antibodies. Reproduced from Laksanasopin et al. [43], with permission from American Association for the Advancement of Science (copyright 2015). b The schematic of the smartphone-based RT-LAMP device along with the smartphone application for real-time monitoring of the reaction. Reproduced from Priye et al. [44], with permission from Springer Nature (copyright 2012). c The schematic representation of smartphone-based fluorescence platform for DNA imaging and sizing. The image shows the fluorescence image of the DNA acquired with the device. Reproduced from Wei et al. [45], with permission from Springer Nature (copyright 2017)

Fig. 4

a Smartphone-based protein microarray with 3D-printed attachment and respective schematic of the detection platform. Reproduced from Ludwig et al. [54], with permission from Springer Nature (copyright 2014). b The schematic representation of the fluorescence colorimetric assay platform based on smartphone. Reproduced from Masawat et al. [55], with permission from Elsevier (copyright 2015). c The prototype of NIR smartphone-based tool utilizing Mie scattering for detection of bacteria in beef sample. The small box at the bottom shows the different angles utilized by the device for detection. Reproduced from Liang et al. [56], with permission from Springer Nature (copyright 2014). d The smartphone-based iTube platform for performing colorimetric assay. The enlarged region shows the optical arrangement and the detection module of the device. Reproduced from Coskun et al. [57], with permission from Royal Society of Chemistry (copyright 2012)

Fig. 5

The smartphone-based microscopic imaging system complimented with image processing utilizing deep leaning–based CNN. The images marked as a and b shows the comparison of deep learning–enhanced image to that of benchtop microscopic image. Reproduced from Rivenson et al. [65], with permission from American Chemical Society (copyright 2018)

Fig. 6

a Prototype of smartphone-based imaging platform showing the attachments for intraoral imaging and whole cavity imaging. The first column of images is white light–based, whereas the second column is autofluorescence-based. The third column shows the green intensity map of the respective images with the mean subtracted. Reproduced from Utoff et al. [68], with permission from PLOS (copyright 2018). b Identification and quantification of particulate matter with the help of the smartphone base c-Air device. Reproduced from Wu et al. [73], with permission from Springer Nature (copyright 2017)