AI-Driven Diagnostic Innovations in Primary Care: Optimizing Otitis Media Diagnosis

Primary care clinicians may have a validated AI aid for ear exams on the horizon. According to new research in the Journal of Clinical Medicine, a CNN-based diagnostic pipeline automates otoscopic interpretation to classify normal tympanic membranes and three otitis media subtypes, reducing variability at the point of care.

The pipeline uses task‑optimized CNN modules that mirror clinical decision steps and was trained on a large clinical image set. Clinically, the tool provides on‑device triage and decision support to improve consistency and earlier identification in front‑line clinics.

The study used a primary‑care–representative otoscopic image dataset (n = 2,964) to validate a four‑way tympanic membrane classification endpoint and reported overall accuracy of 88.7%. Validation applied sequential quality filtering, segmentation, laterality assignment, and multi‑class disease classification with cross‑validation; class‑level F1‑scores for AOM, OME, and COM were reported alongside overall metrics.

The 88.7% result compares favorably with typical non‑specialist otoscopic performance and could narrow gaps in specialist access in routine primary‑care settings.