A deep learning algorithm was developed to identify skin lesions caused by the mpox virus and was then implemented in a web-based app designed for patient use ogevaert Stanford
, A personalized recommendation computed from the information provided and the MPXV-CNN classification of the skin lesion image., Simplified decision tree for MPXV infection risk stratification derived from WHO case definitions with the addition of an AI-assisted case definition based on predictions of the MPXV-CNN. An IDE was used to create and update the survey for risk stratification based on these questions , logical expressions and the MPXV-CNN .
We performed detailed analyses and identified several parameters that impacted the performance, including the body region of the skin lesion, skin tones and non-MPXV diagnoses. The TPR for skin lesions at the head was lower compared to other body locations. This might be related to the complex facial anatomy and the presence of hair. MPXV-CNN’s best performance was achieved in the anogenital and lower extremities regions with TPR of 100% and 85.7% and FPRs of 3.6% and 3.
The main limitation of our study is related to the current scarcity of MPXV photographic images. Due to a lack of public datasets with MPXV images, we created a new dataset from publications of the scientific literature, encyclopedia articles, news articles, social media and a prospective cohort. This approach, however, is prone to biases. Authors might report pictures not of typical, but of extraordinary cases, such as patients with a generalized exanthem or superinfected lesions.
As pointed out by the WHO, AI has great potential for neglected tropical infections such as MPXV, but ethical and privacy considerations for AI tools have to be carefully taken into account, such as where user data are stored and data stewardship
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