eSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases

TL;DR

The paper introduces eSkinHealth, a comprehensive multimodal dermatological dataset from West Africa, designed to improve AI diagnostics for neglected tropical skin diseases by including diverse images, metadata, and annotations.

Contribution

It provides a new large-scale, multimodal dataset focusing on neglected tropical skin diseases and proposes an AI-expert collaboration framework for scalable annotation.

Findings

Dataset includes 5,623 images from 1,639 cases covering 47 skin diseases.

Includes semantic lesion masks, captions, and clinical concepts for rich annotations.

Aims to foster equitable and accurate AI tools for dermatology in underserved populations.

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 C\^ote 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.