Artificial intelligence models for wound infection recognition and their comparison with human results

Abstract

Recognizing an infected wound based solely on a photograph can be a challenge and the aim of this work was to develop a machine learning model that would enable that. We selected 899 wound photographs taken at PODOS Wound Care Clinic (Warsaw, Poland). There were 445 photographs showing uninfected wounds, whereas 454 photographs showed infected wounds with positive microbiological test and antibiotic treatment. A test set was created by randomly selecting 82 photographs representing 42 uninfected and 40 infected wounds. From the remaining photographs, 154 were randomly selected for the validation set, and the remaining 663 formed the training set. Initially we used five pretrained YOLO models from generation 8 and five from generation 11. The 8th generation models performed better than 11th generation models and were then compared with the results of 6 experts and 6 nursing students. The post-hoc analysis revealed that AI models outperformed both specialists and students in terms of mean averaged precision (mAP), accuracy and F1 score, while the results of specialists and students did not differ significantly. For specialists, the medians of mAP, F1 score, and accuracy were 74.1 %, 76.4 %, and 74.4 %, respectively. For Students the medians were 68.4 %, 59.4 %, and 67.7 %, respectively; and for AI models the medians were 92.7 %, 92.9 %, and 92.7 %, respectively. The highest accuracy of 95.1 % of YOLOv8n model was significantly higher than the best specialist’s result of 84.1 %. These results suggest that artificial intelligence can significantly help caregivers recognize wound infection, so they can take appropriate action more quickly.