Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Trajectories of sepsis survivors in Australia: A scoping review.

Journal of the Intensive Care Society·2026
Same author

Non-Indigenous women's experiences of obstetric and gynaecological inequity in rural and remote South Australia: A feminist post-structuralist analysis.

Women's health (London, England)·2026
Same author

Hearing loss among forcibly displaced children: a scoping review on the critical areas of concern.

International journal of audiology·2026
Same author

Effect of parent-focused interventions for screen use on developmental outcomes in young children: a systematic review and meta-analysis.

The international journal of behavioral nutrition and physical activity·2026
Same author

Sweet dreams at home: A review of paediatric domiciliary sleep studies.

Sleep medicine·2026
Same author

Epidemiological time trends in acute pancreatitis - A 16-year experience from South Australia.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]·2026

Related Experiment Video

Updated: May 27, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.4K

A multimodal machine learning algorithm improved diagnostic accuracy for otitis media in a school aged Aboriginal

Jacqueline H Stephens1, Phong Phu Nguyen2, Amanda Machell3

  • 1Flinders University, College of Medicine and Public Health, Flinders Health and Medical Research Institute, Adelaide, Australia.

Journal of Biomedical Informatics
|February 19, 2025
PubMed
Summary

Combining ear infection diagnostic data improves accuracy. Integrating otoscopy and tympanometry in machine learning models enhances early detection of otitis media (OM) in children, aiding timely treatment.

Keywords:
Australian Aboriginal and Torres Strait Islander Peoples [MeSH]Diagnosis [MeSH]Machine Learning [MeSH]Otitis media [MeSH]

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K

Related Experiment Videos

Last Updated: May 27, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.4K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K

Area of Science:

  • Medical diagnostics
  • Artificial Intelligence in Healthcare
  • Pediatric Otolaryngology

Background:

  • Otitis Media (OM), a common ear infection, can lead to significant hearing loss and developmental delays in children.
  • Accurate diagnosis of OM and its subgroups is challenging, even for experienced clinicians.
  • Current AI diagnostic tools for OM often focus on single data types, limiting their comprehensive diagnostic power.

Purpose of the Study:

  • To determine if combining otoscopic and tympanometry data enhances the diagnostic accuracy of a machine learning (ML) algorithm for Otitis Media.
  • To evaluate the ML algorithm's effectiveness in diagnosing various subgroups of OM.

Main Methods:

  • Utilized a dataset of 15,057 matched video otoscopy and tympanometry samples from 813 school-aged children in remote South Australia.
  • Employed Support Vector Machine models to develop the ML diagnostic system.
  • Compared diagnostic accuracy using otoscopy data alone versus combined otoscopy and tympanometry data.

Main Results:

  • The ML algorithm's diagnostic accuracy increased from 78% using only otoscopy data to 82% when incorporating tympanometry data.
  • Combining tympanometry data with otoscopy probability predictions yielded improved diagnostic performance.

Conclusions:

  • Integrating otoscopy and tympanometry data significantly improves the diagnostic accuracy of ML algorithms for Otitis Media.
  • This combined data approach offers a promising tool to support accurate OM diagnosis in children.
  • The findings are particularly relevant for improving timely diagnosis and treatment in rural and remote settings.