Early Detection of Hypothyroidism in Infants using Deep Learning
Deep Learning has been rapidly adopted in various spheres of healthcare for translating biomedical data into improved human healthcare. Deep learning based healthcare applications assist physicians to make faster, cheaper and more accurate diagnosis.
Amongst several successfully developed healthcare applications, in this case study, I will speak on “Early Detection of Hypothyroidism in Infants using Deep 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 the 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. Due to these complications, infant’s growth cannot be proceeded as healthy infants. To prevent such complications, we have designed and developed a novel approach to diagnose hypothyroidism in infants using deep learning during its early stage. To the best of our knowledge, ours is the first attempt to classify infant as either healthy infant or suffering from hypothyroidism based on only facial symptoms viz. puffy face, jaundice, swelling around eyes, protruding tongue and flat bridged nose with broad fleshy tip to diagnose hypothyroidism. The classification output is a probability of hypothyroidism to assist doctors to identify hypothyroidism only from the facial image of infant in the initial phase.
The discussion of this case study will include precise problem statement, the major steps involved in the solution, data set created consulting several Endocrinologists, experimental results and challenges faced & overcame to achieve this success. Finally, I will briefly discuss the possible extensions of our work and the future scope of research in healthcare sector.
Outline/Structure of the Case Study
- Hypothyroidism and its complications in Infants (5 mins)
- Issues in clinical screening of hypothyroidism (3 mins)
- Precise problem statement (2 mins)
- Proposed machine learning based solution (5 mins)
- Proposed deep learning based solution (5 mins)
- Discussion of dataset (5 mins)
- Empirical results and discussion (10 mins)
- Future research directions (5 mins)
- Q & A (5 mins)
- After attending this session, the attendees will ...
- understand how potential of machine learning and deep learning can be utilized to diagnose unexplored or rarely focused diseases.
- learn how to overcome healthcare challenges using deep learning.
Data Scientists, Machine Learning/Deep Learning Practitioners, Researchers, Doctors, Students & Faculty Members from sectors such as Engineering and Technology as well as Medical.
Prerequisites for Attendees
- Familiarity with fundamentals of machine learning and deep learning.