aceClinically Interpretable Deep Learning for Differentiating Vitiligo and Postinflammatory Hypopigmentation: Diagnostic Accuracy Study
Distinguishing vitiligo from postinflammatory hypopigmentation (PIH) is clinically challenging because both conditions may present with similar depigmented lesions. Although deep learning has shown strong potential for dermatologic image classification, limited interpretability remains a barrier to clinical adoption. This study aimed to develop an interpretable deep learning framework for accurate differentiation between vitiligo and PIH using a lightweight convolutional neural network and an ensemble of explain…
A fine-tuned MobileNetV2 model differentiated vitiligo from postinflammatory hypopigmentation with 94.88% accuracy and 0.9885 AUC while providing clinically meaningful ensemble explanations.
Evidence
- Peer-reviewedJMIR Medical Informatics2026-07-24
How should this claim be treated?
Truvace Impact Record TRV-2026-0563, v1: “Clinically Interpretable Deep Learning for Differentiating Vitiligo and Postinflammatory Hypopigmentation: Diagnostic Accuracy Study.” Truvace, 2026-07-25. /record/TRV-2026-0563 (accessed at citation time). sha256 a865ffb6b74dc92e…
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