DeepIH: A deep learning-based near-patient system for treatment recommendation in infantile hemangiomas

Abstract

Infantile hemangiomas (IH) are a common pediatric condition that, if not diagnosed and treated early, can lead to functional impairments or permanent disfigurement. However, accurate diagnosis and timely treatment recommendations often depend on the expertise of clinicians and expensive medical imaging, which presents significant challenges in resource-limited settings, especially in low- and middle-income countries. While existing computer-aided diagnosis (CAD) methods have been developed for IH, they mainly assist clinicians rather than offering direct decision-making support, which limits their impact on patient care. To address these challenges, we propose DeepIH, the first near-patient system designed for treatment recommendation of IH based on deep learning. DeepIH is methodologically innovative in two key ways: (1) it accepts camera-shot images as input, enabling patients to conveniently access treatment recommendations through accessible edge devices like smartphones or laptops; (2) it directly generates treatment recommendations, reducing reliance on clinician oversight and enabling faster, more accessible care. Through evaluation on our established dataset, DeepIH achieves an impressive 95.8% accuracy in detecting lesion regions and 84.9% top-3 accuracy in recommending treatments, which even surpasses a fine-tuned foundation model by 1.7%. These findings, for the first time, validate the viability of near-patient diagnosis for IH, highlighting its potential significance in clinical applications as it allows patients to receive treatment recommendations through everyday devices like smartphones or laptops.