Abstract
This study utilized AIML (artificial intelligence & machine learning) techniques to analyze 115 images of central perforation of tympanic membrane obtained from Telemyringoscopy through Borescope in order to establish a facilitation-model for the community ear diagnosis. The Modified VGG19 with batch normalization revealed the highest training accuracy of 85 as compared to other CNN techniques. The training accuracy started to saturate around mid-70% and the Test accuracy was around 50%. Although AIML did not reveal a high predictive value, its potential based on our observations cannot be underestimated considering many limitations (sample size, image-quality, associated pathologies, illumination-factor) in this study. Such limitations if resolved may revolutionize community ear care through a better cost effective tele-myringoscopy with innovations in AIML/ telemedicine.
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AK, PM, AK worked under supervision of DMK who was the overall authority for analyzing the image data. All the images were collected by AM who conceptualized the study and planned it with DMK. All authors read and approved the final manuscript.
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All the authors declare that there is no conflict of interest and no funding was obtained. Although no informed consent was taken as this distant method of assessment was in fact a necessity in covid phase but still an ethical clearance was applied for. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
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Katyayan, A., Mishra, P., Katyayan, A. et al. Augmenting Community Diagnosis of Safe Ear Disease Through Tele-Myringoscopy with Borescope Using AIML Techniques. Indian J Otolaryngol Head Neck Surg 75, 1864–1869 (2023). https://doi.org/10.1007/s12070-023-03769-3
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DOI: https://doi.org/10.1007/s12070-023-03769-3