Literature DB >> 23343036

Identification of fidgety movements and prediction of CP by the use of computer-based video analysis is more accurate when based on two video recordings.

Lars Adde1, Jorunn Helbostad, Alexander R Jensenius, Mette Langaas, Ragnhild Støen.   

Abstract

This study evaluates the role of postterm age at assessment and the use of one or two video recordings for the detection of fidgety movements (FMs) and prediction of cerebral palsy (CP) using computer vision software. Recordings between 9 and 17 weeks postterm age from 52 preterm and term infants (24 boys, 28 girls; 26 born preterm) were used. Recordings were analyzed using computer vision software. Movement variables, derived from differences between subsequent video frames, were used for quantitative analysis. Sensitivities, specificities, and area under curve were estimated for the first and second recording, or a mean of both. FMs were classified based on the Prechtl approach of general movement assessment. CP status was reported at 2 years. Nine children developed CP of whom all recordings had absent FMs. The mean variability of the centroid of motion (CSD) from two recordings was more accurate than using only one recording, and identified all children who were diagnosed with CP at 2 years. Age at assessment did not influence the detection of FMs or prediction of CP. The accuracy of computer vision techniques in identifying FMs and predicting CP based on two recordings should be confirmed in future studies.

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Year:  2013        PMID: 23343036     DOI: 10.3109/09593985.2012.757404

Source DB:  PubMed          Journal:  Physiother Theory Pract        ISSN: 0959-3985            Impact factor:   2.279


  9 in total

1.  Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk.

Authors:  Daniel Groos; Lars Adde; Sindre Aubert; Lynn Boswell; Raye-Ann de Regnier; Toril Fjørtoft; Deborah Gaebler-Spira; Andreas Haukeland; Marianne Loennecken; Michael Msall; Unn Inger Möinichen; Aurelie Pascal; Colleen Peyton; Heri Ramampiaro; Michael D Schreiber; Inger Elisabeth Silberg; Nils Thomas Songstad; Niranjan Thomas; Christine Van den Broeck; Gunn Kristin Øberg; Espen A F Ihlen; Ragnhild Støen
Journal:  JAMA Netw Open       Date:  2022-07-01

2.  Computer Vision to Automatically Assess Infant Neuromotor Risk.

Authors:  Claire Chambers; Nidhi Seethapathi; Rachit Saluja; Helen Loeb; Samuel R Pierce; Daniel K Bogen; Laura Prosser; Michelle J Johnson; Konrad P Kording
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2020-11-06       Impact factor: 3.802

3.  Novel AI driven approach to classify infant motor functions.

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

Review 4.  Technology-aided assessment of sensorimotor function in early infancy.

Authors:  Alessandro G Allievi; Tomoki Arichi; Anne L Gordon; Etienne Burdet
Journal:  Front Neurol       Date:  2014-10-01       Impact factor: 4.003

5.  Concurrent Validity Between Live and Home Video Observations Using the Alberta Infant Motor Scale.

Authors:  Marike Boonzaaijer; Ellen van Dam; Ingrid C van Haastert; Jacqueline Nuysink
Journal:  Pediatr Phys Ther       Date:  2017-04       Impact factor: 3.049

6.  Cerebral Palsy: Early Markers of Clinical Phenotype and Functional Outcome.

Authors:  Christa Einspieler; Arend F Bos; Magdalena Krieber-Tomantschger; Elsa Alvarado; Vanessa M Barbosa; Natascia Bertoncelli; Marlette Burger; Olena Chorna; Sabrina Del Secco; Raye-Ann DeRegnier; Britta Hüning; Jooyeon Ko; Laura Lucaccioni; Tomoki Maeda; Viviana Marchi; Erika Martín; Catherine Morgan; Akmer Mutlu; Alice Nogolová; Jasmin Pansy; Colleen Peyton; Florian B Pokorny; Lucia R Prinsloo; Eileen Ricci; Lokesh Saini; Anna Scheuchenegger; Cinthia R D Silva; Marina Soloveichick; Alicia J Spittle; Moreno Toldo; Fabiana Utsch; Jeanetta van Zyl; Carlos Viñals; Jun Wang; Hong Yang; Bilge N Yardımcı-Lokmanoğlu; Giovanni Cioni; Fabrizio Ferrari; Andrea Guzzetta; Peter B Marschik
Journal:  J Clin Med       Date:  2019-10-04       Impact factor: 4.241

7.  Temporal and spatial localisation of general movement complexity and variation-Why Gestalt assessment requires experience.

Authors:  Ying-Chin Wu; Ilse M van Rijssen; Maria T Buurman; Linze-Jaap Dijkstra; Elisa G Hamer; Mijna Hadders-Algra
Journal:  Acta Paediatr       Date:  2020-06-22       Impact factor: 2.299

8.  Automated Movement Analysis to Predict Cerebral Palsy in Very Preterm Infants: An Ambispective Cohort Study.

Authors:  Kamini Raghuram; Silvia Orlandi; Paige Church; Maureen Luther; Alex Kiss; Vibhuti Shah
Journal:  Children (Basel)       Date:  2022-06-07

9.  Early prediction of typical outcome and mild developmental delay for prioritisation of service delivery for very preterm and very low birthweight infants: a study protocol.

Authors:  Rebecca Caesar; Roslyn N Boyd; Paul Colditz; Giovani Cioni; Robert S Ware; Kaye Salthouse; Julie Doherty; Maxine Jackson; Leanne Matthews; Tom Hurley; Anthony Morosini; Clare Thomas; Laxmi Camadoo; Erica Baer
Journal:  BMJ Open       Date:  2016-07-04       Impact factor: 2.692

  9 in total

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