RESUMEN
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.
RESUMEN
This pilot observation intends to stress on web-based hearing assessment (WBHA) as somewhat parallel to clinical pure tone audiometry. While WBHA was comparable with PTA in context of severity of deafness particularly in symmetrical hearing loss, it was inconclusive for a gross asymmetry despite multiple trials. With increasing COVID transmission, more need for social distancing and lack of audiologists in developing countries, the self-participation by patients in WBHA model will prove to be a very safe model of deafness-screening.
RESUMEN
Computerization of health care is the only model to sustain safe health care in this COVID era particularly in overpopulated nations with limited health care providers/systems like India. Accordingly incorporation of computer-based algorithms and artificial intelligence seems very robust and practical models to assist the physician. The advantages of Computerized algorithms to facilitate better screening, diagnosis or follow-up and use of Artificial Intelligence (AI) to aid in medical diagnosis are discussed.