Artificial Intelligence-Based Diagnosis of Kaposi Sarcoma Using Digital Photographs in Dark-Skinned Patients in Uganda.
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PURPOSE: This study sought to evaluate the accuracy of artificial intelligence (AI)-based interpretation of digital surface images of skin lesions to diagnose Kaposi sarcoma (KS) among dark-skinned patients in Uganda. METHODS: Patients were evaluated at skin biopsy services in Uganda because of clinical suspicion of KS. In a cross-sectional design, 482 consecutive participants were enrolled. Lesions were photographed using a digital camera, and punch biopsies were obtained. Histopathologic interpretation was considered the gold standard. Using training (approximately 70% of images) and validation (approximately 10% of images) sets, we developed a prediction model using a rule-based combination of You Only Look Once version 5 and 8 object detection classifiers. We determined sensitivity, specificity, and positive and negative predictive values of the AI-based prediction model in a test set (approximately 20% of images) and compared these with the accuracy of a dermatologist's visual interpretation of images. RESULTS: Four hundred seventy-two participants (1,385 images) were evaluable. Of these, 36% was female; the median age was 34 years; and 94% had HIV, 332 had KS, and 140 had no KS by histopathology. In the test set, the AI-derived prediction model achieved 89% sensitivity (85%-94%) and 51% specificity (40%-61%) for diagnosing KS; the positive predictive value was 81% (75%-86%), and the negative predictive value was 67% (55%-78%). The area under the receiver operating characteristic curve was 0.72. A dermatologist evaluating the same images, with emphasis on sensitivity, achieved a sensitivity of 93% (89%-96%) and a specificity of 19% (11%-28%). CONCLUSION: Among dark-skinned patients in Uganda with lesions suspicious for KS, evaluation of digital surface images by an AI-based prediction model produced moderate accuracy for diagnosing KS. While currently inadequate for clinical use, this inaugural assessment is sufficiently promising to justify future evaluation of larger data sets and evolving technologies.