A narrative review of the management of pars flaccida tympanic membrane retractions without cholesteatoma

Bateman, Laura; Borsetto, Daniele; Boscolo‐Rizzo, Paolo; Mochloulis, George; Vijendren, Ananth

doi:10.1111/coa.14085

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…The structure of the TM is important for normal hearing. Many diseases cause hearing loss by altering the TM, such as perforations [3][4][5][6][7], retraction pockets [8][9][10][11], and cholesteatoma [11][12][13]. Other ear diseases cause hearing loss by affecting the middle ear space, which lies directly behind the TM, such as ear infections, middle ear fluid, or tumors of the middle ear.…”

Section: Introductionmentioning

confidence: 99%

Automated Segmentation of Optical Coherence Tomography Images of the Human Tympanic Membrane Using Deep Learning

Oghalai,

Long,

Kim

et al. 2023

Algorithms

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Optical Coherence Tomography (OCT) is a light-based imaging modality that is used widely in the diagnosis and management of eye disease, and it is starting to become used to evaluate for ear disease. However, manual image analysis to interpret the anatomical and pathological findings in the images it provides is complicated and time-consuming. To streamline data analysis and image processing, we applied a machine learning algorithm to identify and segment the key anatomical structure of interest for medical diagnostics, the tympanic membrane. Using 3D volumes of the human tympanic membrane, we used thresholding and contour finding to locate a series of objects. We then applied TensorFlow deep learning algorithms to identify the tympanic membrane within the objects using a convolutional neural network. Finally, we reconstructed the 3D volume to selectively display the tympanic membrane. The algorithm was able to correctly identify the tympanic membrane properly with an accuracy of ~98% while removing most of the artifacts within the images, caused by reflections and signal saturations. Thus, the algorithm significantly improved visualization of the tympanic membrane, which was our primary objective. Machine learning approaches, such as this one, will be critical to allowing OCT medical imaging to become a convenient and viable diagnostic tool within the field of otolaryngology.

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