Presentation
13 March 2024 Prognostic value of optical coherence tomography (OCT) with machine learning for assessing clinical outcomes of otitis media
Author Affiliations +
Abstract
Optical Coherence Tomography (OCT) has shown its detection and diagnostic capabilities for otitis media (OM), enabling visualization through scattering tissues including the tympanic membrane and biofilms, and into the middle ear cavity. Preliminary results from an ongoing five-year 235-subject study at Children’s Wisconsin, Medical College of Wisconsin, are presented. A vision-language machine learning model was trained on OCT image features and clinical metadata to differentiate OM disease states and predict required interventions. This study demonstrates the prognostic value of OCT in assessing OM and offers the potential for improving the management of patients with OM.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alexander Ho, Qian Jiang, Katherine Peterson, Roxanne Link, Guillermo Monroy, Darold R. Spillman, Edita Aksamitiene, Joseph E. Kerschner, Stephen A. Boppart, and Minh Do "Prognostic value of optical coherence tomography (OCT) with machine learning for assessing clinical outcomes of otitis media", Proc. SPIE PC12818, Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2024, (13 March 2024); https://doi.org/10.1117/12.3003143
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KEYWORDS
Optical coherence tomography

Machine learning

Biofilms

Diseases and disorders

Tissues

Visual process modeling

Image resolution

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