OTOLens: Development and validation of a mobile application for assessment of tympanic membrane and middle ear...

Diagnosing tympanic membrane and middle ear pathology in primary care is challenging due to anatomical complexity, overlapping presentations, and limited access to specialized equipment. We developed and externally validated OTOLens, a smartphone-based mobile application classifying otoscopic images using deep learning.

In this prospective development and validation study, OTOLens combined three ResNet-based image classifiers (OME, TMP, and MYR) with a parallel gradient-boosted tabular classifier operating on PCA-reduced image features, integrated by a per-condition maximum and a 0.5 decision threshold (Apple Core ML/Create ML); the system was trained on 1011 annotated otoscopic images across five categories: normal tympanic membrane (n = 251), otitis media with effusion (OME; n = 227), myringitis (MYR; n = 210), other pathology (n = 197), and tympanic membrane perforation (TMP; n = 126), labeled by three fellowship-trained otolaryngologists via majority consensus. External validation used 180 prospectively acquired images reviewed independently by three otolaryngologists. Performance metrics included sensitivity, specificity, positive predictive value, F1 score, and AUROC; inter-rater reliability was assessed by Fleiss' κ; application agreement by Cohen's κ.

Internal testing demonstrated class-wise accuracy of 58-81% with AUROC up to 0.89. External validation achieved 52.8% overall accuracy (Cohen's κ = 0.417; moderate agreement). Class-specific sensitivity was 86.7% for TMP, 48.9% for OME, 40.0% for MYR, and 35.6% for normal tympanic membrane; specificity was 100% for normal tympanic membrane and MYR. Expert inter-rater reliability was substantial (Fleiss' κ = 0.681, 95% CI: 0.619-0.738).

OTOLens achieved high sensitivity for TMP, supporting its potential as a primary care triage aid. This single stronger result does not constitute broad clinical utility; the low sensitivities for normal TM, OME, and MYR and the modest OME PPV indicate that the application cannot reliably assess these categories and should not be used in isolation. Generalizability was constrained by domain shift, class imbalance, and limited TMP representation; multicenter validation with expanded datasets is required prior to clinical deployment.