An Image Based Object Recognition System for Wound Detection and Classification of Diabetic Foot and Venous Leg Ulcers...32nd Medical Informatics Europe Conference (MIE2022), 27-30 May, 2022, Nice, France.
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...
| Published in: | Studies in Health Technology & Informatics Vol. 294; pp. 63 - 68 |
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| Main Authors: | , , , , , , , , , , , , , , |
| Format: | pictorial proceedings research tables/charts Journal Article |
| Published: |
Sage Publications Inc.
2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157268367&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157268367 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2022 vid: 294 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 157268367 157268367 157268367 10.3233/SHTI220397 157268367 ppf: 63 ppct: 5 formats: tig: atl: An Image Based Object Recognition System for Wound Detection and Classification of Diabetic Foot and Venous Leg Ulcers...32nd Medical Informatics Europe Conference (MIE2022), 27-30 May, 2022, Nice, France. aug: au: HÜSERS, Jens MOELLEKEN, Maurice RICHTER, Mats L. PRZYSUCHA, Mareike MALIHI, Leila BUSCH, Dorothee GÖTZ, Nina-Alexandra HEGGEMANN, Jan HAFER, Guido WIEMEYER, Stefan BABITSCH, Birgit HEIDEMANN, Gunther DISSEMOND, Joachim ERFURT-BERGE, Cornelia HÜBNER, Ursula affil: Health Informatics Research Group, Osnabrück University of AS, Germany. sug: subj: Venous Ulcer Diagnosis Diabetic Foot Diagnosis Venous Ulcer Classification Diabetic Foot Classification Algorithms Evaluation Image Interpretation, Computer Assisted Artificial Intelligence Decision Support Systems, Clinical Human Congresses and Conferences France France Funding Source Wound Assessment Germany Precision Medical Informatics Academic Medical Centers Random Assignment Descriptive Statistics Image Processing, Computer Assisted Prediction Models ab: 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. pubtype: Academic Journal doctype: pictorial proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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