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Prototype of a multimodal AI system for vitiligo detection and mental health monitoring
Attila Biró1,2,3,4, László Barna Iantovics4, László Fekete5
1Physiological Controls Research Center, Obuda University, Budapest, Hungary.
Background:
Vitiligo is a chronic autoimmune disorder with profound psychosocial implications.
Methods:
The paper propose a multimodal artificial intelligence (AI) framework that combines and integrates YOLOv11 for the detection of dermatological lesion and a BERT-based sentiment classifier for the monitoring of mental health, supported by questionnaire data sets (DLQI, RSE).
Results:
YOLOv11 achieved mAP = 98.8%, precision = 95.6%, recall = 97.0%; the mental health module uses a BERT-based sentiment classifier, fine-tuned in the GoEmotions corpus, reaching F1 = 0.83. A simulated fusion score that integrates the Dermatology Life Quality Index (DLQI) and Rosenberg Self-Esteem (RSE) scores, resulting in an area under the ROC curve (AUC) of 0.82 for the identification of high-risk patients.
Conclusion:
The implemented prototype establishes the feasibility of AI-assisted psychodermatology, allowing early diagnosis, emotional monitoring, and real-time alerting by physicians.
