A Smart Telemedicine System with Deep Learning to Manage Diabetic Retinopathy and Foot Ulcers

Lim

et al. 2021

Sensors

In this study, two-photon laser scanning microscopy (TPLSM) based on the internet of things (IoT) is proposed as a remote research equipment sharing system, which enables the remote sharing economy. IoT modules, where data are transmitted to and received from the remote users in the web service via IoT, instead of a data acquisition (DAQ) system embedded in the conventional TPLSM, are installed in the IoT-based TPLSM (IoT-TPLSM). The performance for each IoT module is evaluated independently, and it is confirmed that it works well even in a personal computer-free environment. In addition, a message queuing telemetry transport (MQTT) protocol is applied to the DAQ interface in the web service, and a graphic user interface for enabling the remote users to operate IoT-TPLSM remotely is also designed and implemented. For the image acquisition demonstration, the stained cellular images and the autofluorescent tissue images are obtained in IoT-TPLSM. Lastly, it is confirmed that the comparable performance is provided with the conventional TPLSM by evaluating the imaging conditions and qualities of the three-dimensional image stacks processed in IoT-TPLSM.

Section: Discussionmentioning

confidence: 99%

IoT-Based Research Equipment Sharing System for Remotely Controlled Two-Photon Laser Scanning Microscopy

Lim

et al. 2021

Sensors

International Journal of Technology and Human Interaction

“…The subject of our research is user cognitive reactions to the performance of the test by the AI algorithm, and to the Virtual Assistant's communication of this result. This approach is similar to one presented by Wijesinghe, Gamage, Perera, and Chitraranjan (2019) for smart telemedicine system to manage diabetic retinopathy. Empirically, this paper is focused on the Polish health system because it has high hopes for the implementation of telemedicine in order to improve its condition (Domagała & Klich, 2018).…”

Section: Introductionmentioning

confidence: 92%

Is AI Augmenting or Substituting Humans?

Gaczek

Leszczyński

Zieliński

2021

In this paper, the authors focus on Artificial Intelligence as a tangible technology that is designed to sense, comprehend, act, and learn. There are two manifestations of AI in the medical service: an algorithm that analyzes and interprets the test result and a virtual assistant that communicates the result to the patient. The aim of this paper is to consider how AI can substitute a doctor in measuring human health and how the interaction with virtual assistant impacts one’s visual attention processes. Theoretically, the article refers to the following research strands: Human-Computer Interaction, technology in services, implementation of AI in the medical sector, and behavioral economy. By conducting an eye-tracking experimental study, it is demonstrated that the perception of medical diagnosis does not differ across experimental groups (human vs. AI). However, it is observed that participants exposed to AI-based assistant focused more on button allowing to contact a real doctor.

“…Eight studies focussed on the segmentation of colour images [3], [17], [40], [42], [43], [45], [46], [48], and four studies focussed on the segmentation of thermal images [28], [49]- [51]. Nine studies utilised ML to conduct both segmentation and classification [31], [33]- [35], [39], [41], [44], [52], [55]. One study looked at risk analysis via regression [22].…”

Section: A Study Characteristicsmentioning

confidence: 99%

“…Two studies focussed on neural networks to classify DFU images into six categories, based on the Wagner diabetic foot ulcer grade classification system [33], [55]. In Wijesinghe et al [55] a D-CNN was utilised for both the process of wound detection and segmentation, as well as wound classification, achieving an accuracy of 97.5% on 400 test images.…”

Section: Segmentation and Classification Algorithmsmentioning

confidence: 99%

Machine Learning in the Prevention, Diagnosis and Management of Diabetic Foot Ulcers: A Systematic Review

2020

Diabetic foot ulcers (DFUs) are a serious complication for people with diabetes. They result in increased morbidity and pressures on health system resources. Developments in machine learning (ML) offer an opportunity for improved care of individuals at risk of DFUs, to identify and synthesise evidence about the current uses and accuracy of ML in the interventional care and management of DFUs, and, to provide a reference for areas of future research. PubMed, Google Scholar, Web of Science and Scopus were searched using the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines for papers involving ML and DFUs. In order to be included, studies needed to mention ML, DFUs, and report relevant outcome measures regarding ML algorithm accuracy. Bias in included studies was assessed using the quality assessment tool for diagnostic accuracy (QUADAS-2). 37 out of 3769 papers were included after applying eligibility criteria. Included papers reported accuracy measures for multiple types of ML algorithms in DFU studies. Whilst varying across the ML algorithm used, all studies reported at least 90% accuracy compared to gold standards using a minimum of one reported ML algorithm for processing or recording data. Applications where ML had positive effects on DFU data analysis and outcomes include image segmentation and classification, raw data analysis and risk assessment. ML offers an effective and accurate solution to guide analysis and procurement of data from interventions which are designed for the care of DFUs in small samples and study conditions. Current research is limited, and, for the development of more applicable ML algorithms, future research should address the following: direct comparison of ML applications with current standards of care, health economic analyses and large scale data collection. There is currently no evidence to confidently suggest that ML methods in DFU diagnosis are ready for implementation and use in healthcare settings.