Non-invasive detection of diabetic complications via pattern analysis of temporal facial colour variations. (November 2020)

• Record Type: Journal Article
• Authors: Majtner, Tomáš; Nadimi, Esmaeil S.; Yderstræde, Knud B.; Blanes-Vidal, Victoria
• Is Part Of: Computer methods and programs in biomedicine. Volume 196(2020)
• Publication Date: 2020-11
• Subjects: Pattern recognition -- Machine learning -- Skin patch analysis -- Feature extraction -- Skin redness; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105619
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A non-invasive detection framework for patients with diabetic complications. Automatic examination of rubeosis faciei in a room with a non-controlled lighting conditions. Image processing and pattern recognition techniques to analyse short video recordings. Evaluation on 174 video files with 92.86% accuracy, 100% sensitivity, and 80% specificity. Abstract: Background and Objective: Diabetes mellitus is a common disorder amounting to 400 million patients worldwide. It is often accompanied by a number of complications, including neuropathy, nephropathy, and cardiovascular diseases. For example, peripheral neuropathy is present among 20–30% of diabetics before the diagnosis is substantiated. For this reason, a reliable detection method for diabetic complications is crucial and attracts a lot of research attention. Methods: In this paper, we introduce a non-invasive detection framework for patients with diabetic complications that only requires short video recordings of faces from a standard commercial camera. We employed multiple image processing and pattern recognition techniques to process video frames, extract relevant information, and predict the health status. To evaluate our framework, we collected a dataset of 114 video files from diabetic patients, who were diagnosed with diabetes for years and 60 video files from the control group. Extracted features from videos were tested using two conceptually different classifiers. Results: We found that our proposed framework correctly identifies patients with diabetic complications with 92.86% accuracy, 100% sensitivity, and 80% specificity. Conclusions: Our study brings a novel perspective on diagnosis procedures in this field. We used multiple techniques from image processing, pattern recognition, and machine learning to robustly process video frames and predict the health status of our subjects with high efficiency.