An Image Based Object Recognition System for Wound Detection and Classification of Diabetic Foot and Venous Leg Ulcers
Autor(en): | Hüsers, J. Moelleken, M. Richter, M.L. Przysucha, M. Malihi, L. Busch, D. Götz, N.-A. Heggemann, J. Hafer, G. Wiemeyer, S. Babitsch, B. Heidemann, G. Dissemond, J. Erfurt-Berge, C. Hübner, U. |
Herausgeber: | Seroussi, B. Weber, P. Dhombres, F. Grouin, C. Liebe, J.-D. Pelayo, S. Pinna, A. Rance, B. Sacchi, L. Ugon, A. Benis, A. Gallos, P. |
Stichwörter: | Artificial Intelligence; Classification (of information); Clinical Decision Support System; Decision support systems; Diabetic Foot; Diabetic Foot Ulcer; diagnostic imaging; Diseases; Health Information Technology; Health informations; human, Diabetes Mellitus; Humans; Image based object recognition; Image Classification; Images classification; Medical informatics; Object recognition systems; Object recognition, Clinical decision support systems; Venous Leg Ulcer; Venous leg ulcers; Wound Care; Wound care, Image classification, diabetes mellitus | Erscheinungsdatum: | 2022 | Herausgeber: | IOS Press BV | Journal: | Studies in Health Technology and Informatics | Volumen: | 294 | Startseite: | 63 | Seitenende: | 67 | Zusammenfassung: | Venous leg ulcers and diabetic foot ulcers are the most common chronic wounds. Their prevalence has been increasing significantly over the last years, consuming scarce care resources. This study aimed to explore the performance of detection and classification algorithms for these types of wounds in images. To this end, algorithms of the YoloV5 family of pre-trained models were applied to 885 images containing at least one of the two wound types. The YoloV5m6 model provided the highest precision (0.942) and a high recall value (0.837). Its mAP_0.5:0.95 was 0.642. While the latter value is comparable to the ones reported in the literature, precision and recall were considerably higher. In conclusion, our results on good wound detection and classification may reveal a path towards (semi-) automated entry of wound information in patient records. To strengthen the trust of clinicians, we are currently incorporating a dashboard where clinicians can check the validity of the predictions against their expertise. © 2022 European Federation for Medical Informatics (EFMI) and IOS Press. |
Beschreibung: | Conference of 32nd Medical Informatics Europe Conference, MIE 2022 ; Conference Date: 27 May 2022 Through 30 May 2022; Conference Code:179490 |
ISBN: | 9781643682846 | ISSN: | 0926-9630 | DOI: | 10.3233/SHTI220397 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131106854&doi=10.3233%2fSHTI220397&partnerID=40&md5=d4a65a2a22d8eea71cc166fe29647dc5 |
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geprüft am 29.05.2024