Early Detection of Hypothyroidism in Infants using Machine Learning
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)
- 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.
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 10 months ago
People who liked this proposal, also liked:
Dr. Mayuri Mehta - How AI is Transforming HealthcareDr. Mayuri MehtaProfessor & PG In-ChargeDepartment of Computer Engineering, Sarvajanik College of Engineering and Technology
schedule 5 months agoSold Out!
Healthcare industry across the world has progressed very rapidly over the last two decades. However, the healthcare industry is behind other sectors in adopting the newer IT technologies. This talk primarily focuses on imbibing Artificial Intelligence (AI) in medical domain innovations. Like other revolutionary advances in medicine, AI is to be integrated into healthcare practices.
Healthcare using Artificial Intelligence is amongst the fastest growing research area across the globe. A massive amount of heterogeneous data generated in healthcare sector offers opportunities for big data analytics. Such analysis transforms big data into real and actionable insights to healthcare practices, thus provide new understanding and ways for better and quicker treatment and improve overall individual and population health.
Recent advancements in AI are proving beneficial in development of applications in various spheres of healthcare such as microbiological analysis, discovery of drug, disease diagnosis, Genomics, medical imaging and bioinformatics. Due to increasing availability of electronic healthcare data (structured as well as unstructured data) and rapid progress of analytics techniques, a lot of research is being carried out in this area. Popular AI techniques include machine learning/deep learning for structured data and natural language processing for unstructured data. Guided by relevant clinical questions, automation using AI can unlock clinically relevant information hidden in the massive amount of structured/unstructured data, which in turn can assist clinical decision making.
The talk connects three contemporary areas of research: AI, Healthcare and Bigdata Analytics. It will provide attendees a collective update on developments in healthcare using AI, major challenges, opportunities and future research directions.
Dr. Mayuri Mehta - Tear Film Break Time based Dry Eye Disease Diagnosis using Deep LearningDr. Mayuri MehtaProfessor & PG In-ChargeDepartment of Computer Engineering, Sarvajanik College of Engineering and Technology
schedule 10 months agoSold Out!
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.