RDFace: A Benchmark Dataset for Rare Disease Facial Image Analysis under Extreme Data Scarcity and Phenotype-Aware Synthetic Generation

TL;DR

RDFace is a benchmark dataset of 456 pediatric facial images with rare genetic conditions, designed to facilitate AI diagnosis under extreme data scarcity and to evaluate synthetic data augmentation methods.

Contribution

The paper introduces RDFace, a curated dataset with standardized metadata, and demonstrates synthetic augmentation techniques to improve AI diagnostic accuracy in low-data scenarios.

Findings

Synthetic augmentation with DreamBooth and FastGAN improves diagnostic accuracy by up to 13.7%.

Generated images maintain phenotype fidelity through landmark similarity filtering.

Phenotype descriptions from real and synthetic images achieve a report similarity score of 0.84.

Abstract

Rare diseases often manifest with distinctive facial phenotypes in children, offering valuable diagnostic cues for clinicians and AI-assisted screening systems. However, progress in this field is severely limited by the scarcity of curated, ethically sourced facial data and the high similarity among phenotypes across different conditions. To address these challenges, we introduce RDFace, a curated benchmark dataset comprising 456 pediatric facial images spanning 103 rare genetic conditions (average 4.4 samples per condition). Each ethically verified image is paired with standardized metadata. RDFace enables the development and evaluation of data-efficient AI models for rare disease diagnosis under real-world low-data constraints. We benchmark multiple pretrained vision backbones using cross-validation and explore synthetic augmentation with DreamBooth and FastGAN. Generated images are filtered via facial landmark similarity to maintain phenotype fidelity and merged with real data, improving diagnostic accuracy by up to 13.7% in ultra-low-data regimes. To assess semantic validity, phenotype descriptions generated by a vision-language model from real and synthetic images achieve a report similarity score of 0.84. RDFace establishes a transparent, benchmark-ready dataset for equitable rare disease AI research and presents a scalable framework for evaluating both diagnostic performance and the integrity of synthetic medical imagery.