AIMC Topic: Otoscopy
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Deep learning algorithm classification of tympanostomy tube images from a heterogenous pediatric population.
International journal of pediatric otorhinolaryngology
Mar 13, 2025
IMPORTANCE: The ability to augment routine post-operative tube check appointments with at-home digital otoscopes and deep learning AI could improve health care access as well as reduce financial and time burden on families.
A multimodal machine learning algorithm improved diagnostic accuracy for otitis media in a school aged Aboriginal population.
Journal of biomedical informatics
Feb 17, 2025
OBJECTIVE: Otitis Media (OM) - ear infection - can lead to hearing loss and associated developmental delay. There are several subgroups of OM which can be difficult to diagnose accurately, even for experienced clinicians. AI and machine learning algo...
Pediatric tympanostomy tube assessment via deep learning.
American journal of otolaryngology
Apr 28, 2024
PURPOSE: Tympanostomy tube (TT) placement is the most frequently performed ambulatory surgery in children under 15. After the procedure it is recommended that patients follow up regularly for "tube checks" until TT extrusion. Such visits incur direct...
Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks.
European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
Jan 10, 2024
PURPOSE: Patient-to-image registration is a preliminary step required in surgical navigation based on preoperative images. Human intervention and fiducial markers hamper this task as they are time-consuming and introduce potential errors. We aimed to...
AI Model Versus Clinician Otoscopy in the Operative Setting for Otitis Media Diagnosis.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Oct 11, 2023
Prior work has demonstrated improved accuracy in otitis media diagnosis based on otoscopy using artificial intelligence (AI)-based approaches compared to clinician evaluation. However, this difference in accuracy has not been shown in a setting resem...
Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy.
Scientific reports
Apr 1, 2023
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...
Automatic Prediction of Conductive Hearing Loss Using Video Pneumatic Otoscopy and Deep Learning Algorithm.
Ear and hearing
Mar 29, 2022
OBJECTIVES: Diseases of the middle ear can interfere with normal sound transmission, which results in conductive hearing loss. Since video pneumatic otoscopy (VPO) findings reveal not only the presence of middle ear effusions but also dynamic movemen...
Artificial intelligence to diagnose ear disease using otoscopic image analysis: a review.
Journal of investigative medicine : the official publication of the American Federation for Clinical Research
Sep 14, 2021
AI relates broadly to the science of developing computer systems to imitate human intelligence, thus allowing for the automation of tasks that would otherwise necessitate human cognition. Such technology has increasingly demonstrated capacity to outp...
Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach.
Scientific reports
Jun 15, 2021
Otitis media, a common disease marked by the presence of fluid within the middle ear space, imparts a significant global health and economic burden. Identifying an effusion through the tympanic membrane is critical to diagnostic success but remains c...
Machine Learning for Accurate Intraoperative Pediatric Middle Ear Effusion Diagnosis.
Pediatrics
Mar 17, 2021
OBJECTIVES: Misdiagnosis of acute and chronic otitis media in children can result in significant consequences from either undertreatment or overtreatment. Our objective was to develop and train an artificial intelligence algorithm to accurately predi...
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