Tympanic Membrane Image Classification for Otitis Media Diagnosis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for diagnosing otitis media, particularly distinguishing between acute otitis media (AOM), otitis media with effusion (OME), and no effusion (NOE), are unreliable, leading to over- and under-prescription of antibiotics, increased costs, and potential complications due to the difficulty in visual examination and classification of tympanic membrane images.
Innovation Solution
A diagnostic system and method that classify tympanic membrane images using a set of extracted features such as concavity, translucency, amber level, grayscale variance, bubble presence, and light features, processed by a computing device to accurately differentiate between AOM, OME, and NOE, reducing the need for expert interpretation and minimizing misdiagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual examination of the tympanic membrane is performed by non-expert otoscopists, then the diagnostic process is simple and quick, but the diagnostic accuracy is low leading to misdiagnosis
Solution Approach 1:
The patent introduces an automated classification system that acts as an intermediary between the otoscope image capture and the final diagnosis. The system processes images through multiple classification stages (first classifier for AOM/NOE/OME, second classifier for AOM severity) to provide objective diagnostic assistance, thereby improving accuracy while maintaining ease of use for non-expert otoscopists
Solution Approach 2:
The patent replaces the subjective mechanical visual examination process with an automated digital image classification system. The system uses computer-aided classification algorithms to objectively analyze tympanic membrane images, substituting human expert judgment with automated processing while preserving the simplicity of the overall diagnostic workflow
2Reliability
If antibiotics are prescribed for uncertain cases to avoid under-treatment, then patient outcomes are protected, but antibiotic over-prescription occurs leading to resistance
Solution Approach 1:
The patent implements a feedback mechanism where the automated classification system provides diagnostic confidence indicators and classification results to guide antibiotic prescribing decisions. The system analyzes image features and provides structured output that helps clinicians make evidence-based decisions about when antibiotics are truly necessary, reducing over-prescription while maintaining appropriate treatment coverage
Solution Approach 2:
The patent changes the diagnostic parameters from subjective visual assessment to objective image-based measurements (color, texture, shape features). This parameter transformation enables more precise differentiation between AOM cases requiring antibiotics and OME/NOE cases where antibiotics are unnecessary, thereby reducing antibiotic resistance while protecting patient outcomes
3Device complexity
If hand-held otoscopes are used for diagnosis, then the equipment is simple and portable, but image acquisition and recording capabilities are limited
Solution Approach 1:
The patent merges the simple hand-held otoscope with digital imaging and computer-aided classification capabilities. The system combines traditional otoscope functionality with image capture, processing, and automated classification in an integrated workflow, preserving portability and simplicity while adding valuable image recording and analysis capabilities
Solution Approach 2:
The patent creates digital copies of the tympanic membrane images captured by the otoscope. These digital copies can be stored, analyzed by the classification system, and used for documentation and follow-up, thereby preserving diagnostic information without requiring complex equipment while enabling comprehensive image acquisition and recording
Data Source
AI summary
A method of aiding the diagnosis of otitis media in a patient includes obtaining image data in a processor apparatus of a computing device, the image data being associated with at least one electronic image of a tympanic membrane of the patient, calculating, a plurality of image features, each image feature being calculated based on at least a portion of the image data, classifying the at least one electronic image as a particular type of otitis media using the plurality of image features, and outputting an indication of the particular type of otitis media. Also, a system for implementing such a method that includes an output device and a computing device.