ArticleScientific reports2023
Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
- LUCID: Intelligent Informative Frame Selection in Otoscopy for Enhanced Diagnostic Utility.Diagnostics (Basel, Switzerland) · 2026Article
- Deep Learning for Endoscopic Classification of Adenoid Hypertrophy.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Article
- Image-based deep learning in otology: a state-of-the-art review.Frontiers in neurology · 2026Review
- Article
- Eustachian Tube Dysfunction in Hearing Loss: Mechanistic Pathways to Targeted Interventions.Biomedicines · 2025Review
- Transforming ENT Healthcare: Advancements and Implications of Artificial Intelligence.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- Article
- Ear-Keeper: A cross-platform artificial intelligence system for rapid and accurate ear disease diagnosis.Digital healthArticle
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To evaluate the generalizability of artificial intelligence (AI) algorithms that use deep learning methods to identify middle ear disease from otoscopic images, between internal to external performance. 1842 otoscopic images were collected from three independent sources: (a) Van, Turkey, (b) Santiago, Chile, and (c) Ohio, USA. Diagnostic categories consisted of (i) normal or (ii) abnormal. Deep learning methods were used to develop models to evaluate internal and external performance, using area under the curve (AUC) estimates. A pooled assessment was performed by combining all cohorts together with fivefold cross validation. AI-otoscopy algorithms achieved high internal performance (mean AUC: 0.95, 95%CI: 0.80-1.00). However, performance was reduced when tested on external otoscopic images not used for training (mean AUC: 0.76, 95%CI: 0.61-0.91). Overall, external performance was significantly lower than internal performance (mean difference in AUC: -0.19, p ≤ 0.04). Combining cohorts achieved a substantial pooled performance (AUC: 0.96, standard error: 0.01). Internally applied algorithms for otoscopy performed well to identify middle ear disease from otoscopy images. However, external performance was reduced when applied to new test cohorts. Further efforts are required to explore data augmentation and pre-processing techniques that might improve external performance and develop a robust, generalizable algorithm for real-world clinical applications.
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