The diagnosis of cerebral palsy (CP) is difficult before 2 years of age. The general movements assessment (GMA) is a method for predicting CP from the spontaneous movements of infants in the first months of life. This assessment has shown high accuracy in predicting CP, but its use is limited by a lack of trained clinicians and its subjective nature. An objective and cost-effective alternative is the automatic videobased assessment of infant movements. Retrospective videos with clinical GMA outcomes were evaluated against eligibility criteria for the automatic analysis consisting of a skin model for segmentation and large displacement optical flow (LDOF) for motion tracking. Kinematic features were extracted to classify the movements as typical or atypical using different classification algorithms. Preliminary classification results obtained from the analysis of 127 videos of preterm infants showed up to 92% of accuracy in predicting CP. A computerbased assessment would provide clinicians with an objective tool for early diagnosis of CP, to facilitate early intervention and improve functional outcomes.
Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis / Orlandi S.; Raghuram K.; Smith C.R.; Mansueto D.; Church P.; Shah V.; Luther M.; Chau T.. - STAMPA. - 2018-:(2018), pp. 8513078.3598-8513078.3601. (Intervento presentato al convegno 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 tenutosi a Hawaii Convention Center, usa nel 2018) [10.1109/EMBC.2018.8513078].
Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis
Orlandi S.
Primo
Writing – Original Draft Preparation
;
2018
Abstract
The diagnosis of cerebral palsy (CP) is difficult before 2 years of age. The general movements assessment (GMA) is a method for predicting CP from the spontaneous movements of infants in the first months of life. This assessment has shown high accuracy in predicting CP, but its use is limited by a lack of trained clinicians and its subjective nature. An objective and cost-effective alternative is the automatic videobased assessment of infant movements. Retrospective videos with clinical GMA outcomes were evaluated against eligibility criteria for the automatic analysis consisting of a skin model for segmentation and large displacement optical flow (LDOF) for motion tracking. Kinematic features were extracted to classify the movements as typical or atypical using different classification algorithms. Preliminary classification results obtained from the analysis of 127 videos of preterm infants showed up to 92% of accuracy in predicting CP. A computerbased assessment would provide clinicians with an objective tool for early diagnosis of CP, to facilitate early intervention and improve functional outcomes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
