TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Hüsers, Jens
A1  - Moelleken, Maurice
A1  - Richter, Mats L.
A1  - Przysucha, Mareike
A1  - Malihi, Leila
A1  - Busch, Dorothee
A1  - Götz, Nina-Alexandra
A1  - Heggemann, Jan
A1  - Hafer, Guido
A1  - Wiemeyer, Stefan
A1  - Babitsch, Birgit
A1  - Heidemann, Gunther
A1  - Dissemond, Joachim
A1  - Erfurt-Berge, Cornelia
A1  - Hübner, Ursula Hertha
T1  - An Image Based Object Recognition System for Wound Detection and Classification of Diabetic Foot and Venous Leg Ulcers
T2  - Challenges of Trustable AI and Added-Value on Health
N2  - 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.
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:959-opus-35738
SN  - 978-1-64368-284-6
SB  - 978-1-64368-284-6
SN  - 978-1-64368-285-3
SB  - 978-1-64368-285-3
U6  - https://doi.org/10.3233/SHTI220397
DO  - https://doi.org/10.3233/SHTI220397
N1  - 32nd Medical Informatics Europe Conference (MIE2022), 27.05. - 30.05.2022, Nice (France)
SP  - 63
EP  - 67
PB  - IOS Press
CY  - Amsterdam, Berlin, Washington (DC)
ER  -