Embedded AI-Assisted Otoscopic Image Screening for Pediatric Otitis Media

OBJECTIVE: To evaluate an embedded artificial intelligence (AI) system for otoscopic image-based screening and prediagnostic triage of pediatric otitis media, with an emphasis on point-of-care deployment on a microcontroller. METHODS: We retrospectively analyzed 19 522 tympanic membrane images labeled by otolaryngologists as acute otitis media, otitis media with effusion, or normal. A lightweight convolutional neural network derived from AlexNet was trained using a patient-level split. Two posttraining INT8 variants (per-channel and per-tensor quantization) were deployed on an STM32H7S78-DK board using STM32Cube.AI. We evaluated accuracy on a held-out test set (n=600) and measured activation memory, total random access memory (RAM) usage, and average inference latency on the target device. RESULTS: The full-precision model achieved 97.67% test accuracy. Per-channel INT8 preserved accuracy (97.67%). Per-tensor INT8 showed a small decrease (97.50%) but reduced activation memory by 16%, total RAM by 8%, and latency by 13%. CONCLUSION: On-device otoscopic image analysis is feasible on an STM32H7-class microcontroller and may support screening and triage workflows (eg, repeat examination, tympanometry, monitoring, or referral). The tool is not intended to provide a final diagnosis or to direct treatment decisions; clinical diagnosis and management remain clinician-led.