Cerebral Palsy (CP) is the most common motor dysfunction in childhood. Traditionally, diagnosis is set between 12 and 24 months of age. This study will evaluate feasibility of a new screening procedure for early detection of CP in high-risk infants and investigate how such a procedure can be implemented in the Central Norwegian Regional Health Authority (CNRHA). The most accurate method to detect and predict CP at an early age is the General Movement Assessment (GMA). GMA is based upon expert observations of infant spontaneous movements in a video. In Central-Norway such expertise is today only present at St. Olavs Hospital, Trondheim University Hospital. Video recordings by health personnel and parents will be used in follow-up programs within CNRHA for remote expert based GMA. In addition, machine learning models will be applied for automatic detection of CP. Early identification of CP will lead to improved function and increased possibility to direct health care resources to the patients who need it most, independent of geographical and expert based constraints.
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Scorable video for remote GMA
Timeframe: 1 week
Correlation of computer software data between standard video set-up and handheld smartphone video.
Timeframe: 1 week
Predictability of GMA and computer-based assessment for development of CP
Timeframe: 2 years
Predictability of GMA and computer-based assessment for development of CP
Timeframe: 5 years