Artificial Intelligence-based Diagnosis of Kaposi Sarcoma using Photographs in Dark-skinned Patients.

Abstract

Importance: Advanced-stage disease at the time of diagnosis, with resultant high mortality, is among the most urgent issues for HIV-related Kaposi sarcoma (KS) in sub-Saharan Africa. Lack of access to skilled clinical personnel and histopathologic technology in the region contribute to diagnostic delays and advanced stage at diagnosis. Accordingly, new paradigms for KS diagnosis are needed.

Objective: To evaluate the accuracy of artificial intelligence (AI)-based interpretation of digital surface images of skin lesions to diagnose KS among dark-skinned patients in Uganda.

Design: Cross-sectional study of consecutive participants referred to skin biopsy services in Uganda because of clinical suspicion of KS. Lesions were photographed using a digital camera, and punch biopsies were obtained. Histopathologic interpretation was considered the gold standard. Using training (∼70% of images) and validation (∼10% of images) sets, we developed a prediction model using a rule-based combination of YOLO (You Only Look Once) version 5 and 8 object detection classifiers.

Setting: Free-of-charge skin biopsy services.

Participants: Consecutive sample of 482 individuals were evaluated due to clinical suspicion of KS.

Main outcomes: Sensitivity, specificity, positive and negative predictive value (with accompanying 95% confidence intervals) of the AI-based prediction model in a test set (∼20% of images). The accuracy of a dermatologist's visual interpretation of images was also described.

Results: 472 participants (1385 images) were evaluable. Of these, 36% were female, 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; positive predictive value was 81% (75%-86%) and negative predictive value was 67% (55%-78%). A dermatologist evaluating the same images, with emphasis on sensitivity, achieved sensitivity of 93% (89%-96%) and specificity of 19% (11%-28%).

Conclusions and relevance: Among dark-skinned patients in Uganda with skin 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 evaluation of larger datasets and evolving technologies to determine if accuracy can be improved.

Key points: Question: Can an artificial intelligence (AI)-based prediction model be developed from digital images to accurately distinguish Kaposi sarcoma (KS) from non-KS in dark-skinned patients?Findings: Evaluation of digital images of skin lesions from patients in Uganda by an AI-based prediction model produced moderate accuracy for diagnosing KS.Meaning: In sub-Saharan Africa, where incidence and mortality from KS is high and delayed diagnosis is common due to limited specialized personnel and technical supplies, AI-based prediction models built on digital images taken of suspicious lesions may someday hasten KS diagnoses.