Kamini Raghuram1, Silvia Orlandi2, Vibhuti Shah1,3,4, Tom Chau2, Maureen Luther5, Rudaina Banihani1,5, Paige Church6,7. 1. Division of Neonatology, Department of Pediatrics, University of Toronto, Toronto, ON, Canada. 2. Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada. 3. Department of Paediatrics, Mount Sinai Hospital, Toronto, ON, Canada. 4. Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada. 5. Neonatology and Neonatal Follow-up, Women and Babies Program, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. 6. Division of Neonatology, Department of Pediatrics, University of Toronto, Toronto, ON, Canada. paige.church@sunnybrook.ca. 7. Neonatology and Neonatal Follow-up, Women and Babies Program, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. paige.church@sunnybrook.ca.
Abstract
OBJECTIVE: To apply automated movement analysis to the general movements assessment (GMA) to build a predictive model for motor impairment (MI). STUDY DESIGN: A retrospective cohort study including infants ≤306/7 weeks GA or BW ≤1500 g seen at 3-5 months was conducted. Automated video analysis was used to develop a multivariable model to identify MI, defined as Bayley motor composite score <85 or cerebral palsy (CP). RESULTS: One hundred and fifty two videos were analyzed. Median GA and BW were 275/7 weeks and 955 g, respectively. MI and CP rates were 22% (N = 33) and 14% (N = 22). Minimum, mean, and mean vertical velocity of the infant's silhouette correlated significantly with MI. Sensitivity, specificity, positive and negative predictive values, and accuracy of automated GMA were 79%, 63%, 37%, 91%, and 66%, respectively. C-statistic indicated good fit (C = 0.77). CONCLUSIONS: Automated movement analysis predicts MI in preterm infants. Further refinement of this technology is required for clinical application.
OBJECTIVE: To apply automated movement analysis to the general movements assessment (GMA) to build a predictive model for motor impairment (MI). STUDY DESIGN: A retrospective cohort study including infants ≤306/7 weeks GA or BW ≤1500 g seen at 3-5 months was conducted. Automated video analysis was used to develop a multivariable model to identify MI, defined as Bayley motor composite score <85 or cerebral palsy (CP). RESULTS: One hundred and fifty two videos were analyzed. Median GA and BW were 275/7 weeks and 955 g, respectively. MI and CP rates were 22% (N = 33) and 14% (N = 22). Minimum, mean, and mean vertical velocity of the infant's silhouette correlated significantly with MI. Sensitivity, specificity, positive and negative predictive values, and accuracy of automated GMA were 79%, 63%, 37%, 91%, and 66%, respectively. C-statistic indicated good fit (C = 0.77). CONCLUSIONS: Automated movement analysis predicts MI in preterm infants. Further refinement of this technology is required for clinical application.
Authors: Simon Reich; Dajie Zhang; Tomas Kulvicius; Sven Bölte; Karin Nielsen-Saines; Florian B Pokorny; Robert Peharz; Luise Poustka; Florentin Wörgötter; Christa Einspieler; Peter B Marschik Journal: Sci Rep Date: 2021-05-10 Impact factor: 4.379
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