A multimodal machine learning algorithm improved diagnostic accuracy for otitis media in a school aged aboriginal population.

Abstract

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 algorithms for OM diagnosis are evolving but typically only focus on one defined diagnostic feature of OM. This study aimed to establish if combining otoscopic and tympanometry data improves the diagnostic accuracy of a ML algorithm for diagnosing OM and its various subgroups.

Methods: We used an existing dataset containing data from 813 school-aged children (aged five to eight years) from 10 Aboriginal communities in remote South Australia. Data were collected between 2009 and 2011. All children underwent video otoscopy and tympanometry assessment of both ears and diagnosis of OM was made by otorhinolaryngology (ENT) surgeons. After data augmentation and preprocessing, the database contained 15,057 samples with matched video otoscopy and tympanometry data (normal: n = 8,239; abnormal: n = 6,746). Support Vector Machine models were used to build the ML system.

Results: By combining tympanometry data with the probability prediction of the single otoscopy model, the accuracy of the system increased from 78 % (otoscopy data) to 82 % (otoscopy and tympanometry data).

Conclusion: Compared to diagnosis based solely on otoscopy data, combining otoscopy and tympanometry data increased the diagnostic accuracy of the ML algorithm. This approach could be used to support the accurate diagnosis of OM in children and can facilitate timely and appropriate treatment, especially in rural and remote areas.