Synthetic Data Alone is Enough? Rethinking Data Scarcity in Pediatric Rare Disease Recognition

TL;DR

This study investigates whether synthetic facial images alone can effectively train computer vision models for pediatric rare disease recognition, showing promising results at sufficient scale.

Contribution

It demonstrates that high-fidelity synthetic data can match real data performance in pediatric rare disease classification, enabling privacy-preserving applications.

Findings

Synthetic-only training achieves comparable performance to real-data baselines.

Models trained on synthetic data perform well at sufficient scale across multiple architectures.

Synthetic data can serve as a privacy-preserving resource for genetic education and counseling.

Abstract

Children with rare genetic diseases often exhibit distinctive facial phenotypes, yet developing computer vision systems for early diagnosis remains challenging due to extreme data scarcity, privacy constraints, and limited data sharing in pediatric settings. These challenges not only hinder automated diagnosis but also restrict the availability of visual resources for clinical genetic counseling. While prior work has shown that synthetic data can augment real datasets and preserve phenotype-level semantics, it remains unclear whether synthetic data alone is sufficient for learning in ultra-low-resource pediatric settings. In this work, we study the synthetic-only regime for pediatric rare disease recognition. Under a controlled experimental setup, models are trained exclusively on phenotype-aware synthetic facial images at increasing scales. We find that synthetic-only training achieves performance comparable to real-data-only baselines at sufficient scale across multiple backbones, suggesting that high-fidelity synthetic data can approximate clinically meaningful distributions. These findings together further enable the use of synthetic pediatric facial images as privacy-preserving resources for genetic education and counseling, supporting clinician training and patient communication. Our results highlight the potential of computer vision to improve data efficiency and expand accessible visual tools in children's healthcare.