Diagnosis of Mechanical Ear Pathologies Using Structure-Based Modeling and Machine Learning Techniques
Masud, Salwa Fatima
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CitationMasud, Salwa Fatima. 2020. Diagnosis of Mechanical Ear Pathologies Using Structure-Based Modeling and Machine Learning Techniques. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractAbnormal macro-mechanics of the ear such as otosclerosis, ossicular discontinuity and superior canal dehiscence (SCD) can result in a variety of debilitating auditory and vestibular symptoms such as hearing loss, blocked sensation of the ear, hyperacusis and dizziness. The mechanisms by which some mechanical pathologies cause such symptoms are not well understood, and these pathologies are difficult to diagnose and treat. To address these challenges, we set out to improve our knowledge of the mechano-acoustic mechanisms in the normal and abnormal ear, enabling us to develop advanced diagnostic methods. We focus on improving the diagnostic capability of wideband acoustic immittance (WAI), an acoustic measurement in the ear-canal that assess the transfer of stimulus sound energy into the middle ear. WAI is a non-invasive, inexpensive approach with the potential to differentiate the various middle ear and inner ear pathologies that interfere with sound-energy transfer. To automatically detect abnormal macro-mechanics with WAI, we develop a structure-based ear model that simulates pathological WAI patterns in individual ears and utilize machine learning methods to automatically detect mechanical differences between normal and pathological ears. In the first computational modeling study, we evaluate the utility of a structure-based model to simulate changes in mechanics of an SCD ear before and after surgical repair. Next, we modify the same structure-based model to simulate other pathologies of the ear and develop a classifier to differentiate among various pathologies including SCD, ossicular fixation and ossicular discontinuity. Finally, by using partial least squares discriminant analysis to select important WAI features, and a Random Forest classifier to categorize normal-hearing and SCD ears, we develop a systematic method to improve classification accuracy for future larger sample sizes. The application of pattern recognition methods to WAI measurements offers the potential for automatic detection of various mechanical pathologies of the ear. Our developments aim to improve patient care through improved diagnosis to enable proper and fast treatment, better preparedness for surgery, and monitoring of mechanical changes postoperatively.
Citable link to this pagehttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37365115
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