Deep Learning has been rapidly adopted in various spheres of healthcare for translating medical data into improved human healthcare. Deep learning based healthcare applications assist physicians to make faster, cheaper and more accurate diagnosis. This talk will focus on how commonly occurring Dry Eye Disease (DED) can be diagnosed accurately and speedily using deep learning based automated approach.

DED is one of the commonly occurring chronic disease in the world today. It causes severe discomfort in eye, visual disturbance and blurred vision impacting the quality of life of patient. Certain factors such as prolonged use of electronic gadgets, old age, environmental conditions, medication, smoking habits and use of contact lens can disturb the tear film balance and can lead to evaporation of moisture from tear film which causes dry eye disease. If DED is left untreated, it can cause infection, corneal ulcer or blindness. However, diagnosis of dry eye is a difficult task because it occurs due to different factors. An ophthalmologist sometimes requires multiple tests or repetitive tests for proper diagnosis. Moreover, the major drawbacks of clinical diagnosis are: 1) Higher time in clinical diagnosis as it is done manually. This has severe impact during mass screening in Civil Hospitals and Multispecialilty Hospitals 2) Diagnosis is subjective in nature 3) Accurate severity level of DED is not identified and 4) Medication may be prescribed for incorrect period on the basis of inaccurate severity level. To overcome these drawbacks, we have developed a deep learning based automated approach to diagnose DED considering Tear Film Breakup Time (TBUT) which is a standard diagnostic procedure. This automated approach is to assist ophthalmologist and optometrist to bring objectivity in diagnosis, to increase diagnosis accuracy and to make diagnosis faster so that ophthalmologist can devote more time in counselling of patients.

The talk will include motivation, precise problem statement, proposed solution, data set generated consulting ophthalmologists and experimental results. Finally, the possible extensions of our work and the future scope of research in healthcare sector will be discussed.

Machine Learning has been rapidly adopted in various spheres of healthcare for translating medical data into improved human healthcare. Deep learning based healthcare applications assist physicians to make faster, cheaper and more accurate diagnosis. This talk will focus on “Early Detection of Hypothyroidism in Infants using Machine Learning”.

Thyroid is a hormone secreting gland which influences all metabolic activities in our body. Hypothyroidism is a common disorder of thyroid that occurs when thyroid gland produces an insufficient amount of thyroid hormone. Deficiency of thyroid hormone at birth leads to hypothyroidism in babies. The common hypothyroidism symptoms in infants are prolong jaundice, protruding tongue, hoarse cry, puffy face, pain and swelling in joints, goiter and umbilical hernia. During early stage of hypothyroidism, babies may not have noticeable symptoms and hence, doctors (Physicians, Paediatricians and Paediatric Endocrinologists) face difficulty in recognizing hypothyroidism in infants. If hypothyroidism in infants isn’t treated during early stage, severe complications such as mental retardation, slower linear growth, loss of IQ, poor muscle tone, sensorinueral deafness, speech disorder and vision problem may arise. As a consequence, infant’s growth cannot be proceeded as healthy infants. To prevent such complications, we have developed a novel approach to diagnose hypothyroidism in infants during its early stage. To the best of our knowledge, this is the first attempt to detect hypothyroidism based on only facial symptoms viz. puffy face, jaundice, swelling around eyes, protruding tongue and flat bridged nose with broad fleshy tip.

This talk will include motivation for this work, precise problem statement and its solution, data set generated consulting Pediatric Endocrinologists and experimental results. Finally, possible extensions of this work and the future scope of research in healthcare sector will be discussed.