Using Neural Networks to Detect Developmental Delays

Team: 1

School: Academy For Tech & Classics

Area of Science: Medicine and Health

Proposal: According to the Journal of Clinical & Diagnostic Research, 68% of children presenting signs of developmental delays had visible abnormalities in their MRI brain scans, with children with known genetic disorders being excluded from this data. With the ability to quickly determine brain abnormalities from MRI’s, children who have not been recognized as having a developmental delays could be diagnosed in a timely manner and the amount of cases that go undiagnosed would dramatically decrease. We plan to accomplish this by implementing a normal brain scan into our code, and then comparing the scans with possible abnormalities to it. This would allow for much more efficient recognition of developmental delays, drastically decreasing the amount of personnel dedicated to this. In addition, the code could ideally be easily adapted to recognize other brain abnormalities not related to developmental delays.

Team Members:

  Etta Pope
  Odin Frostad
  Eamon BenSlama-McKinley

Sponsoring Teacher: Jenifer Hooten

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