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Biosensors
Special Issues
Smartphone, Wearable, or Hand-Held Diagnostic Bioimaging Sensors/Devices
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Smartphone, Wearable, or Hand-Held Diagnostic Bioimaging Sensors/Devices

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensors and Healthcare".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 23254

Special Issue Editor

Dr. Anuradha Godavarty

E-Mail Website
Guest Editor
Dept of Biomedical Engineering, 10555 W. Flagler St., EC 2675, Florida International University, Miami, FL 33174, USA
Interests: near-infrared imaging; diffuse optical imaging; hand-held devices; smartphone imaging devices; diagnostic imaging; wound imaging; cancer therapeutic monitoring; image coregistration and segmentation

Special Issue Information

Dear Colleagues,

Global health and healthcare technologies are focused on low-cost, portable, hand-held, and/or wearable sensors for diagnostic imaging applications. The enormous potential for portable low-cost technologies to transform healthcare, personal health management, and basic health research has led to the rapid development of new health-related applications, sensors, and devices. These applications include wound imaging, cancer diagnostics, brain mapping, glucose monitoring, ophthalmological applications, early detection of disorders, and many more. This Special Issue calls for any research focused on low-cost diagnostic bioimaging sensors or devices that are wearable, hand-held, and/or smartphone (mobile) technology-based in any biomedical application. The research papers can relate to the device development and preclinical and/or clinical applications of these sensors/devices in diagnostic bioimaging (anywhere from early to advanced stages).

Prof. Anuradha Godavarty
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smartphone devices/sensors
  • Wearable devices/sensors
  • Mobile technology for healthcare
  • Hand-held devices/sensors
  • Diagnostic bioimaging application

Published Papers (4 papers)

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Research

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9 pages, 1324 KiB  
Communication
Machine Learning-Guided Prediction of Central Anterior Chamber Depth Using Slit Lamp Images from a Portable Smartphone Device
by David Chen, Yvonne Ho, Yuki Sasa, Jieying Lee, Ching Chiuan Yen and Clement Tan
Biosensors 2021, 11(6), 182; https://doi.org/10.3390/bios11060182 - 05 Jun 2021
Cited by 7 | Viewed by 3461
Abstract
There is currently no objective portable screening modality for narrow angles in the community. In this prospective, single-centre image validation study, we used machine learning on slit lamp images taken with a portable smartphone device (MIDAS) to predict the central anterior chamber depth [...] Read more.
There is currently no objective portable screening modality for narrow angles in the community. In this prospective, single-centre image validation study, we used machine learning on slit lamp images taken with a portable smartphone device (MIDAS) to predict the central anterior chamber depth (ACD) of phakic patients with undilated pupils. Patients 60 years or older with no history of laser or intraocular surgery were recruited. Slit lamp images were taken with MIDAS, followed by anterior segment optical coherence tomography (ASOCT; Casia SS-1000, Tomey, Nagoya, Japan). After manual annotation of the anatomical landmarks of the slit lamp photos, machine learning was applied after image processing and feature extraction to predict the ACD. These values were then compared with those acquired from the ASOCT. Sixty-six eyes (right = 39, 59.1%) were included for analysis. The predicted ACD values formed a strong positive correlation with the measured ACD values from ASOCT (R2 = 0.91 for training data and R2 = 0.73 for test data). This study suggests the possibility of estimating central ACD using slit lamp images taken from portable devices. Full article
(This article belongs to the Special Issue Smartphone, Wearable, or Hand-Held Diagnostic Bioimaging Sensors/Devices)
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14 pages, 4296 KiB  
Article
Development of a Smartphone-Based Optical Device to Measure Hemoglobin Concentration Changes for Remote Monitoring of Wounds
by Kacie Kaile, Christian Fernandez and Anuradha Godavarty
Biosensors 2021, 11(6), 165; https://doi.org/10.3390/bios11060165 - 21 May 2021
Cited by 8 | Viewed by 2704
Abstract
Telemedicine (TM) can revolutionize the impact of diabetic wound care management, along with tools for remote patient monitoring (RPM). There are no low-cost mobile RPM devices for TM technology to provide comprehensive (visual and physiological) clinical assessments. Here, a novel low-cost smartphone-based optical [...] Read more.
Telemedicine (TM) can revolutionize the impact of diabetic wound care management, along with tools for remote patient monitoring (RPM). There are no low-cost mobile RPM devices for TM technology to provide comprehensive (visual and physiological) clinical assessments. Here, a novel low-cost smartphone-based optical imaging device has been developed to provide physiological measurements of tissues in terms of hemoglobin concentration maps. The device (SmartPhone Oxygenation Tool—SPOT) constitutes an add-on optical module, a smartphone, and a custom app to automate data acquisition while syncing a multi-wavelength near-infrared light-emitting diode (LED) light source (690, 810, 830 nm). The optimal imaging conditions of the SPOT device were determined from signal-to-noise maps. A standard vascular occlusion test was performed in three control subjects to observe changes in hemoglobin concentration maps between rest, occlusion, and release time points on the dorsal of the hand. Hemoglobin concentration maps were compared with and without applying an image de-noising algorithm, single value decomposition. Statistical analysis demonstrated that the hemoglobin concentrations changed significantly across the three-time stamps. Ongoing efforts are in imaging diabetic foot ulcers using the SPOT device to assess its potential as a smart health device for physiological monitoring of wounds remotely. Full article
(This article belongs to the Special Issue Smartphone, Wearable, or Hand-Held Diagnostic Bioimaging Sensors/Devices)
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14 pages, 2659 KiB  
Article
Handheld Briefcase Optical Coherence Tomography with Real-Time Machine Learning Classifier for Middle Ear Infections
by Jungeun Won, Guillermo L. Monroy, Roshan I. Dsouza, Darold R. Spillman, Jr., Jonathan McJunkin, Ryan G. Porter, Jindou Shi, Edita Aksamitiene, MaryEllen Sherwood, Lindsay Stiger and Stephen A. Boppart
Biosensors 2021, 11(5), 143; https://doi.org/10.3390/bios11050143 - 03 May 2021
Cited by 14 | Viewed by 3662
Abstract
A middle ear infection is a prevalent inflammatory disease most common in the pediatric population, and its financial burden remains substantial. Current diagnostic methods are highly subjective, relying on visual cues gathered by an otoscope. To address this shortcoming, optical coherence tomography (OCT) [...] Read more.
A middle ear infection is a prevalent inflammatory disease most common in the pediatric population, and its financial burden remains substantial. Current diagnostic methods are highly subjective, relying on visual cues gathered by an otoscope. To address this shortcoming, optical coherence tomography (OCT) has been integrated into a handheld imaging probe. This system can non-invasively and quantitatively assess middle ear effusions and identify the presence of bacterial biofilms in the middle ear cavity during ear infections. Furthermore, the complete OCT system is housed in a standard briefcase to maximize its portability as a diagnostic device. Nonetheless, interpreting OCT images of the middle ear more often requires expertise in OCT as well as middle ear infections, making it difficult for an untrained user to operate the system as an accurate stand-alone diagnostic tool in clinical settings. Here, we present a briefcase OCT system implemented with a real-time machine learning platform for middle ear infections. A random forest-based classifier can categorize images based on the presence of middle ear effusions and biofilms. This study demonstrates that our briefcase OCT system coupled with machine learning can provide user-invariant classification results of middle ear conditions, which may greatly improve the utility of this technology for the diagnosis and management of middle ear infections. Full article
(This article belongs to the Special Issue Smartphone, Wearable, or Hand-Held Diagnostic Bioimaging Sensors/Devices)
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Review

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36 pages, 2180 KiB  
Review
Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring
by Jesse Fine, Kimberly L. Branan, Andres J. Rodriguez, Tananant Boonya-ananta, Ajmal, Jessica C. Ramella-Roman, Michael J. McShane and Gerard L. Coté
Biosensors 2021, 11(4), 126; https://doi.org/10.3390/bios11040126 - 16 Apr 2021
Cited by 132 | Viewed by 12622
Abstract
Photoplethysmography (PPG) is a low-cost, noninvasive optical technique that uses change in light transmission with changes in blood volume within tissue to provide information for cardiovascular health and fitness. As remote health and wearable medical devices become more prevalent, PPG devices are being [...] Read more.
Photoplethysmography (PPG) is a low-cost, noninvasive optical technique that uses change in light transmission with changes in blood volume within tissue to provide information for cardiovascular health and fitness. As remote health and wearable medical devices become more prevalent, PPG devices are being developed as part of wearable systems to monitor parameters such as heart rate (HR) that do not require complex analysis of the PPG waveform. However, complex analyses of the PPG waveform yield valuable clinical information, such as: blood pressure, respiratory information, sympathetic nervous system activity, and heart rate variability. Systems aiming to derive such complex parameters do not always account for realistic sources of noise, as testing is performed within controlled parameter spaces. A wearable monitoring tool to be used beyond fitness and heart rate must account for noise sources originating from individual patient variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external perturbations of the device itself (e.g., motion artifact, ambient light, and applied pressure to the skin). Here, we present a comprehensive review of the literature that aims to summarize these noise sources for future PPG device development for use in health monitoring. Full article
(This article belongs to the Special Issue Smartphone, Wearable, or Hand-Held Diagnostic Bioimaging Sensors/Devices)
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