Work

Prototype of a multimodal AI system for vitiligo detection and mental health monitoring

Year: 2025

Type: article

Abstract: 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.

Source: Frontiers in Medicine

DOI: 10.3389/fmed.2025.1709891

Authors: Attila Biró, László Barna Iantovics, László Fekete, Gyula László Fekete

Institutions: Obuda University, Universitatea de Medicină, Farmacie, Științe și Tehnologie „George Emil Palade” din Târgu Mureș, Instituto de Investigación Biomédica de Málaga, Universidad de Málaga

Language: English

Topic: Cutaneous Melanoma Detection and Management

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801