The study looked at more than 500 infants with a high risk of perinatal brain damage at hospitals across the U.S., Belgium, India and Norway using video recordings of their movements. Of these videos, 75 percent went through a deep learning algorithm analysis for predicting cerebral palsy and 25 percent of them went through an external validation process using medical professionals.
The results revealed that the machine learning algorithm had a sensitivity of 71.4 percent, meaning it predicted true cases at that rate. It also had a specificity of 94.1 percent, indicating that the model correctly predicts almost all negative cases. The authors concluded that “this study’s findings suggest that deep learning–based assessments could support early detection of CP in infants at high risk.”
At the Becker's 11th Annual IT + Revenue Cycle Conference: The Future of AI & Digital Health, taking place September 14–17 in Chicago, healthcare executives and digital leaders from across the country will come together to explore how AI, interoperability, cybersecurity, and revenue cycle innovation are transforming care delivery, strengthening financial performance, and driving the next era of digital health. Apply for complimentary registration now.
