Computer Algorithms in Assessment of Obstructive Sleep Apnoea Syndrome and Its Application in Estimating Prevalence of Sleep Related Disorders in Population

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.