Infantile spasms are a type of seizure linked to developmental issues. Unfortunately, they are often misdiagnosed, causing delays in treatment. The purpose of this study is to develop a computer program that can reliably differentiate infantile spasms from similar, yet benign movements in videos. This computer program will learn from videos taken by parents of study participants. Quickly recognizing and treating infantile spasms is crucial for ensuring the best developmental outcomes.
Study Type
OBSERVATIONAL
Enrollment
200
Machine learning software developed to analyze videos and accurately distinguish infantile spasms from visually similar movements.
Johns Hopkins Hospital
Baltimore, Maryland, United States
RECRUITINGModel Sensitivity (Recall)
Proportion of true positives which the model classified correctly in the test dataset.
Time frame: 2 years
Model Specificity
Proportion of true negatives which the model classified correctly in the test dataset.
Time frame: 2 years
Model Positive Predictive Value (Precision)
Proportion of positive classifications which were correct in the test dataset.
Time frame: 2 years
Model Negative Predictive Value
Proportion of negative classifications which were correct in the test dataset.
Time frame: 2 years
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