{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T05:45:48Z","timestamp":1785735948245,"version":"3.56.0"},"reference-count":89,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2023JJ60302"],"award-info":[{"award-number":["2023JJ60302"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Deep learning (DL), a core branch of artificial intelligence (AI), has revolutionized medical image analysis. Driven by advances in computational power and access to large-scale datasets, DL excels at extracting hierarchical features from complex, unstructured data. Accurate interpretation of imaging features is essential for diagnosing and treating middle and inner ear diseases. This narrative review aims to summarize current literature on the application of DL in otological imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A narrative literature search was conducted using the PubMed and MEDLINE databases. Keywords related to AI or DL in otology were used to identify relevant articles published up to the time of writing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>DL models have achieved favorable results in classifying common middle ear diseases (e.g., otitis media) using otoscopic images and in segmenting major inner ear structures (e.g., cochlea, ossicular chain) in 3D volumetric data. While 2D CNNs are mature for otoscopic classification, 3D U-Net and UNETR architectures dominate CT and MRI analysis. Models also show value in low-dose CT reconstruction and multimodal diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>DL has demonstrated strong potential to improve clinical decision-making and healthcare efficiency in otology. However, the field faces challenges related to data scarcity for rare diseases, poor segmentation performance for tiny structures (e.g., stapes), and a lack of integration with clinical workflows. Future efforts should focus on standardizing data and optimizing network structures for specific modalities.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2026.1884951","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T05:23:46Z","timestamp":1785734626000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Image-based deep learning in otology: a state-of-the-art review"],"prefix":"10.3389","volume":"17","author":[{"given":"Sining","family":"Wu","sequence":"first","affiliation":[{"name":"Hunan Provincial People's Hospital","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yating","family":"Liao","sequence":"additional","affiliation":[{"name":"Hunan Provincial People's Hospital","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhua","family":"Li","sequence":"additional","affiliation":[{"name":"Hunan Provincial People's Hospital","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans Med 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