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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
The incorporation of telemedicine and artificial intelligence for early screening and assessment of severity of life-style disorders has a great potential for better assessment in a busy outpatient clinic and thereby curtail down the related morbidities. A computer based algorithm based upon standardized questionnaire (from established assessment tools) is designed to assess the risk of obstructive sleep apnoea syndrome (OSAS). In addition the incorporation of basic screening questions of anamnesis help in suggesting a probable diagnosis of sleep related disorder as well. The overall data at our center has been analyzed to establish the existing pattern of sleep related disorders. Of 850 healthy subjects screened, prevalence of snoring was 20.47% while OSAS was seen in 4.20% (N = 25) in males and 2.64% (N = 8) in females. The parasomnia was most prevalent (14.71%), followed by insomnia (10.24%), periodic leg movement (6.59%), bruxism (1.65%) and narcolepsy (0.59%). Hypertension, laryngopharyngeal reflux and obesity were the common co-morbidities in OSAS while family history of hypertension and diabetes were common in snorers. A significant association with OSA was seen with diabetes mellitus, neck circumference and nasal obstruction, while, obesity and apnoeic episodes were more significantly associated with OSA than snorers. Increased waist to hip ratio was appreciated in both the OSAS and snorers. The algorithm based online assessment is likely to diagnose the occult clinical cases as well as assess the risk of OSAS. In routine outpatient clinic, a clinician may better assess the patient morbidity with a comprehensive availability of symptoms and moreover enhance the post-treatment compliance. In addition a smartphone based computerized assessment for general population may be designed for other lifestyle disorders as well.
