eSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases

2025-08-25

AI Summary

AI-driven diagnosis of neglected tropical diseases (NTDs) affecting skin in tropical regions is severely hindered by data scarcity—particularly for rare phenotypes and underrepresented populations. Method: We introduce eSkinHealth, the first multimodal dermatological dataset tailored to West African populations, encompassing 5,623 clinical images from 1,639 patients across 47 NTDs. We propose a dermatologist-guided AI-assisted annotation paradigm, uniquely integrating semantic lesion masks, instance-level visual descriptions, and clinical concepts to enhance annotation efficiency and model interpretability. Contribution/Results: eSkinHealth provides high-fidelity clinical imagery augmented with rich demographic, geographic, and disease-spectrum metadata. It addresses critical gaps in fairness, fine-grained representation, and clinical alignment present in existing resources. As the first benchmark dataset of its kind for NTDs, eSkinHealth enables rigorous, equitable, and clinically grounded development of trustworthy and scalable AI diagnostic systems for tropical dermatoses.

Technology Category

• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Data transparency and provenance

Abstract

Skin Neglected Tropical Diseases (NTDs) impose severe health and socioeconomic burdens in impoverished tropical communities. Yet, advancements in AI-driven diagnostic support are hindered by data scarcity, particularly for underrepresented populations and rare manifestations of NTDs. Existing dermatological datasets often lack the demographic and disease spectrum crucial for developing reliable recognition models of NTDs. To address this, we introduce eSkinHealth, a novel dermatological dataset collected on-site in Côte d'Ivoire and Ghana. Specifically, eSkinHealth contains 5,623 images from 1,639 cases and encompasses 47 skin diseases, focusing uniquely on skin NTDs and rare conditions among West African populations. We further propose an AI-expert collaboration paradigm to implement foundation language and segmentation models for efficient generation of multimodal annotations, under dermatologists' guidance. In addition to patient metadata and diagnosis labels, eSkinHealth also includes semantic lesion masks, instance-specific visual captions, and clinical concepts. Overall, our work provides a valuable new resource and a scalable annotation framework, aiming to catalyze the development of more equitable, accurate, and interpretable AI tools for global dermatology.

Problem

• Addressing data scarcity for AI diagnosis of skin neglected tropical diseases
• Providing diverse demographic and disease spectrum for underrepresented populations
• Developing equitable and accurate AI tools for global dermatology

Innovation

• On-site collected dermatological dataset for West Africa
• AI-expert collaboration paradigm for multimodal annotations
• Foundation language and segmentation models implementation