Special Issue "Mobile Diagnosis"

A special issue of Diagnostics (ISSN 2075-4418).

Deadline for manuscript submissions: closed (31 July 2016)

Special Issue Editor

Guest Editor
Prof. Dr. Aydogan Ozcan

Electrical Engineering and Bioengineering Departments, University of California, Los Angeles, USA
Website | E-Mail
Interests: computational optical imaging and sensing, mobile health, telemedicine, global health

Special Issue Information

Dear Colleagues,

Mobile sensing and diagnostic capabilities are becoming extremely important for a wide range of emerging applications and fields, spanning mobile health, telemedicine, point-of-care diagnostics, global health, field medicine, democratization of sensing and diagnostics tools, environmental monitoring, and citizen science, among many others. This Special Issue will focus on these application areas, and provide a timely summary of cutting edge results and emerging technologies in these interdisciplinary fields.

Dr. Aydogan Ozcan
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 papers will be 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. Diagnostics is an international peer-reviewed open access quarterly 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 350 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

  • mobile sensing
  • mobile diagnostics
  • mobile health
  • telemedicine
  • point-of-care diagnostics
  • global health
  • field medicine
  • sensing and diagnostics tools
  • citizen science

Published Papers (4 papers)

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Research

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Open AccessArticle Mobile Diagnostics Based on Motion? A Close Look at Motility Patterns in the Schistosome Life Cycle
Diagnostics 2016, 6(2), 24; doi:10.3390/diagnostics6020024
Received: 2 February 2016 / Revised: 8 April 2016 / Accepted: 23 May 2016 / Published: 17 June 2016
PDF Full-text (6117 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Imaging at high resolution and subsequent image analysis with modified mobile phones have the potential to solve problems related to microscopy-based diagnostics of parasitic infections in many endemic regions. Diagnostics using the computing power of “smartphones” is not restricted by limited expertise or
[...] Read more.
Imaging at high resolution and subsequent image analysis with modified mobile phones have the potential to solve problems related to microscopy-based diagnostics of parasitic infections in many endemic regions. Diagnostics using the computing power of “smartphones” is not restricted by limited expertise or limitations set by visual perception of a microscopist. Thus diagnostics currently almost exclusively dependent on recognition of morphological features of pathogenic organisms could be based on additional properties, such as motility characteristics recognizable by computer vision. Of special interest are infectious larval stages and “micro swimmers” of e.g., the schistosome life cycle, which infect the intermediate and definitive hosts, respectively. The ciliated miracidium, emerges from the excreted egg upon its contact with water. This means that for diagnostics, recognition of a swimming miracidium is equivalent to recognition of an egg. The motility pattern of miracidia could be defined by computer vision and used as a diagnostic criterion. To develop motility pattern-based diagnostics of schistosomiasis using simple imaging devices, we analyzed Paramecium as a model for the schistosome miracidium. As a model for invasive nematodes, such as strongyloids and filaria, we examined a different type of motility in the apathogenic nematode Turbatrix, the “vinegar eel.” The results of motion time and frequency analysis suggest that target motility may be expressed as specific spectrograms serving as “diagnostic fingerprints.” Full article
(This article belongs to the Special Issue Mobile Diagnosis)
Open AccessArticle Real-Time Monitoring and Evaluation of a Visual-Based Cervical Cancer Screening Program Using a Decision Support Job Aid
Diagnostics 2016, 6(2), 20; doi:10.3390/diagnostics6020020
Received: 8 February 2016 / Revised: 26 April 2016 / Accepted: 6 May 2016 / Published: 16 May 2016
Cited by 3 | PDF Full-text (1626 KB) | HTML Full-text | XML Full-text
Abstract
In many developing nations, cervical cancer screening is done by visual inspection with acetic acid (VIA). Monitoring and evaluation (M&E) of such screening programs is challenging. An enhanced visual assessment (EVA) system was developed to augment VIA procedures in low-resource settings. The EVA
[...] Read more.
In many developing nations, cervical cancer screening is done by visual inspection with acetic acid (VIA). Monitoring and evaluation (M&E) of such screening programs is challenging. An enhanced visual assessment (EVA) system was developed to augment VIA procedures in low-resource settings. The EVA System consists of a mobile colposcope built around a smartphone, and an online image portal for storing and annotating images. A smartphone app is used to control the mobile colposcope, and upload pictures to the image portal. In this paper, a new app feature that documents clinical decisions using an integrated job aid was deployed in a cervical cancer screening camp in Kenya. Six organizations conducting VIA used the EVA System to screen 824 patients over the course of a week, and providers recorded their diagnoses and treatments in the application. Real-time aggregated statistics were broadcast on a public website. Screening organizations were able to assess the number of patients screened, alongside treatment rates, and the patients who tested positive and required treatment in real time, which allowed them to make adjustments as needed. The real-time M&E enabled by “smart” diagnostic medical devices holds promise for broader use in screening programs in low-resource settings. Full article
(This article belongs to the Special Issue Mobile Diagnosis)
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Open AccessArticle Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology
Diagnostics 2016, 6(2), 17; doi:10.3390/diagnostics6020017
Received: 29 December 2015 / Revised: 4 March 2016 / Accepted: 27 April 2016 / Published: 5 May 2016
Cited by 1 | PDF Full-text (5640 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a
[...] Read more.
Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al.), we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, and HepG2, HeLa, and MCF7 cells. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings. Full article
(This article belongs to the Special Issue Mobile Diagnosis)
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Review

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Open AccessReview Improving the Sensitivity and Functionality of Mobile Webcam-Based Fluorescence Detectors for Point-of-Care Diagnostics in Global Health
Diagnostics 2016, 6(2), 19; doi:10.3390/diagnostics6020019
Received: 2 February 2016 / Revised: 19 April 2016 / Accepted: 6 May 2016 / Published: 17 May 2016
Cited by 2 | PDF Full-text (5862 KB) | HTML Full-text | XML Full-text
Abstract
Resource-poor countries and regions require effective, low-cost diagnostic devices for accurate identification and diagnosis of health conditions. Optical detection technologies used for many types of biological and clinical analysis can play a significant role in addressing this need, but must be sufficiently affordable
[...] Read more.
Resource-poor countries and regions require effective, low-cost diagnostic devices for accurate identification and diagnosis of health conditions. Optical detection technologies used for many types of biological and clinical analysis can play a significant role in addressing this need, but must be sufficiently affordable and portable for use in global health settings. Most current clinical optical imaging technologies are accurate and sensitive, but also expensive and difficult to adapt for use in these settings. These challenges can be mitigated by taking advantage of affordable consumer electronics mobile devices such as webcams, mobile phones, charge-coupled device (CCD) cameras, lasers, and LEDs. Low-cost, portable multi-wavelength fluorescence plate readers have been developed for many applications including detection of microbial toxins such as C. Botulinum A neurotoxin, Shiga toxin, and S. aureus enterotoxin B (SEB), and flow cytometry has been used to detect very low cell concentrations. However, the relatively low sensitivities of these devices limit their clinical utility. We have developed several approaches to improve their sensitivity presented here for webcam based fluorescence detectors, including (1) image stacking to improve signal-to-noise ratios; (2) lasers to enable fluorescence excitation for flow cytometry; and (3) streak imaging to capture the trajectory of a single cell, enabling imaging sensors with high noise levels to detect rare cell events. These approaches can also help to overcome some of the limitations of other low-cost optical detection technologies such as CCD or phone-based detectors (like high noise levels or low sensitivities), and provide for their use in low-cost medical diagnostics in resource-poor settings. Full article
(This article belongs to the Special Issue Mobile Diagnosis)
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