Augmenting Community Diagnosis of Safe Ear Disease Through Tele-Myringoscopy with Borescope Using AIML Tecniques

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.