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.

 
 

Outline/Structure of the Talk

  • Hypothyroidism: symptoms and complications in Infants (2 mins)
  • Motivation and problem statement (3 mins)
  • ML based solution (5 mins)
  • Dataset and empirical results (5 mins)
  • Possible future extensions of this work and research directions in healthcare (3 mins)
  • Q & A (2 mins)

Learning Outcome

  • After attending this session, the attendees will ...
    • understand how machine learning can be utilized to diagnose unexplored or rarely focused diseases.
    • understand how machine learning is useful to make faster, cheaper, and more accurate disease diagnosis.
    • be familiar with future research possibilities in healthcare sector.

Target Audience

Data Scientists, Machine Learning/Deep Learning Practitioners, Industry Professionals from Healthcare Sector, Researchers, Doctors, Students & Faculty Members from Engineering and Technology as well as Medical.

Prerequisites for Attendees

  • Familiarity with fundamentals of machine learning.
schedule Submitted 1 year ago

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