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Cited by in F6Publishing
For: Silva N, Zhang D, Kulvicius T, Gail A, Barreiros C, Lindstaedt S, Kraft M, Bölte S, Poustka L, Nielsen-Saines K, Wörgötter F, Einspieler C, Marschik PB. The future of General Movement Assessment: The role of computer vision and machine learning - A scoping review. Res Dev Disabil 2021;110:103854. [PMID: 33571849 DOI: 10.1016/j.ridd.2021.103854] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
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2 Haffner DN, Sankovic A. A Neurologist's Guide to Neonatal Neurodevelopmental Assessments and Preterm Brain Injury. Seminars in Pediatric Neurology 2022. [DOI: 10.1016/j.spen.2022.100974] [Reference Citation Analysis]
3 Leo M, Bernava GM, Carcagnì P, Distante C. Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions. Sensors (Basel) 2022;22:866. [PMID: 35161612 DOI: 10.3390/s22030866] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Reich S, Zhang D, Kulvicius T, Bölte S, Nielsen-Saines K, Pokorny FB, Peharz R, Poustka L, Wörgötter F, Einspieler C, Marschik PB. Novel AI driven approach to classify infant motor functions. Sci Rep 2021;11:9888. [PMID: 33972661 DOI: 10.1038/s41598-021-89347-5] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
5 Redd CB, Karunanithi M, Boyd RN, Barber LA. Technology-assisted quantification of movement to predict infants at high risk of motor disability: A systematic review. Res Dev Disabil 2021;118:104071. [PMID: 34507051 DOI: 10.1016/j.ridd.2021.104071] [Reference Citation Analysis]
6 den Hartog D, van der Krogt MM, van der Burg S, Aleo I, Gijsbers J, Bonouvrié LA, Harlaar J, Buizer AI, Haberfehlner H. Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study. Sensors (Basel) 2022;22:4386. [PMID: 35746168 DOI: 10.3390/s22124386] [Reference Citation Analysis]
7 Shin HI, Shin HI, Bang MS, Kim DK, Shin SH, Kim EK, Kim YJ, Lee ES, Park SG, Ji HM, Lee WH. Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants. Sci Rep 2022;12:3138. [PMID: 35210507 DOI: 10.1038/s41598-022-07139-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
8 Cott R, Hagmann C, Etter R, Latal B. [Differences in the Distribution of the General Movements Classification Between Neonatal Risk Groups in the Children's Hospital Zurich: An Observational Study]. Z Geburtshilfe Neonatol 2022. [PMID: 35672004 DOI: 10.1055/a-1808-2843] [Reference Citation Analysis]
9 Esposito G, Marschik PB, Nordahl-Hansen A. Technological advancements in the assessment and intervention of developmental disabilities. Res Dev Disabil 2021;119:104088. [PMID: 34610524 DOI: 10.1016/j.ridd.2021.104088] [Reference Citation Analysis]
10 Gargot T, Archambault D, Chetouani M, Cohen D, Johal W, Anzalone SM. Automatic Assessment of Motor Impairments in Autism Spectrum Disorders: A Systematic Review. Cogn Comput. [DOI: 10.1007/s12559-021-09940-8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Groos D, Adde L, Aubert S, Boswell L, de Regnier RA, Fjørtoft T, Gaebler-Spira D, Haukeland A, Loennecken M, Msall M, Möinichen UI, Pascal A, Peyton C, Ramampiaro H, Schreiber MD, Silberg IE, Songstad NT, Thomas N, Van den Broeck C, Øberg GK, Ihlen EAF, Støen R. Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk. JAMA Netw Open 2022;5:e2221325. [PMID: 35816301 DOI: 10.1001/jamanetworkopen.2022.21325] [Reference Citation Analysis]