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Assessing infant risk of cerebral palsy with video-based motion tracking | medRxiv
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This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/" /> <meta name="DC.AccessRights" content="restricted" /> <meta name="DC.Description" content="Cerebral Palsy (CP) is a common (about 1 in 500 children) health condition caused by abnormal brain development that affects the ability to control movement. Early risk assessment happens through the General Movements Assessment (GMA), a test administered by trained clinicians at 3-4 months of age that has high predictive value for CP. With recent improvements in video-based motion tracking, automated risk assessment for CP based on the GMA is being explored. However, studies generally have used small datasets or were limited in terms of methodological rigor. Here we acquired a large dataset (1060 infants) of videos from a clinical population with elevated CP risk. In a preregistered pipeline using a lock-box set that was not used before algorithm submission we find that our machine learning predictions are highly predictive of the clinician-assessed GMA (AUC=0.79). Given its low cost, our video-based approach may be useful for clinical screening applications, particularly in low-resource settings. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by NIH-NICHD and the clinical early detection trial data collection was supported in part by the Cerebral Palsy Foundation ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of The University of Pennsylvania (Penn) gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data that is consented for sharing (movement features and trained model) are available online at the OSF pre-registration site (<https://osf.io/sd6fa>) <https://osf.io/sd6fa> <https://doi.org/10.5281/zenodo.14042732>" /> <meta name="DC.Contributor" content="Melanie Segado" /> <meta name="DC.Contributor" content="Laura Prosser" /> <meta name="DC.Contributor" content="Andrea F. Duncan" /> <meta name="DC.Contributor" content="Michelle J. Johnson" /> <meta name="DC.Contributor" content="Konrad P. Kording" /> <meta name="article:published_time" content="2024-11-07" /> <meta name="citation_title" content="Assessing infant risk of cerebral palsy with video-based motion tracking" /> <meta name="citation_abstract" lang="en" content="<p>Cerebral Palsy (CP) is a common (about 1 in 500 children) health condition caused by abnormal brain development that affects the ability to control movement. Early risk assessment happens through the General Movements Assessment (GMA), a test administered by trained clinicians at 3-4 months of age that has high predictive value for CP. With recent improvements in video-based motion tracking, automated risk assessment for CP based on the GMA is being explored. However, studies generally have used small datasets or were limited in terms of methodological rigor. Here we acquired a large dataset (1060 infants) of videos from a clinical population with elevated CP risk. In a preregistered pipeline using a lock-box set that was not used before algorithm submission we find that our machine learning predictions are highly predictive of the clinician-assessed GMA (AUC=0.79). Given its low cost, our video-based approach may be useful for clinical screening applications, particularly in low-resource settings.</p>" /> <meta name="citation_journal_title" content="medRxiv" /> <meta name="citation_publisher" content="Cold Spring Harbor Laboratory Press" /> <meta name="citation_publication_date" content="2024/01/01" /> <meta name="citation_mjid" content="medrxiv;2024.11.06.24316844v1" /> <meta name="citation_id" content="2024.11.06.24316844v1" /> <meta name="citation_public_url" content="https://www.medrxiv.org/content/10.1101/2024.11.06.24316844v1" /> <meta name="citation_abstract_html_url" content="https://www.medrxiv.org/content/10.1101/2024.11.06.24316844v1.abstract" /> <meta name="citation_full_html_url" content="https://www.medrxiv.org/content/10.1101/2024.11.06.24316844v1.full" /> <meta name="citation_pdf_url" content="https://www.medrxiv.org/content/medrxiv/early/2024/11/07/2024.11.06.24316844.full.pdf" /> <meta name="citation_doi" content="10.1101/2024.11.06.24316844" /> <meta name="citation_num_pages" content="16" /> <meta name="citation_article_type" content="Article" /> <meta name="citation_firstpage" content="2024.11.06.24316844" /> <meta name="citation_author" content="Melanie Segado" /> <meta name="citation_author_institution" content="Department of Bioengineering, University of Pennsylvania" /> <meta name="citation_author_institution" content="Department of Neuroscience, University of Pennsylvania" /> <meta name="citation_author_institution" content="Division of Rehabilitation Medicine, The Children’s Hospital of Philadelphia" /> <meta name="citation_author_orcid" content="http://orcid.org/0000-0001-7862-6749" /> <meta name="citation_author" content="Laura Prosser" /> <meta name="citation_author_institution" content="Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania" /> <meta name="citation_author_institution" content="Division of Rehabilitation Medicine, The Children’s Hospital of Philadelphia" /> <meta name="citation_author_email" content="prosserl@chop.edu" /> <meta name="citation_author" content="Andrea F. Duncan" /> <meta name="citation_author_institution" content="Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania" /> <meta name="citation_author_institution" content="Division of Neonatology and Department of Pediatrics, Children’s Hospital of Philadelphia" /> <meta name="citation_author" content="Michelle J. Johnson" /> <meta name="citation_author_institution" content="Department of Bioengineering, University of Pennsylvania" /> <meta name="citation_author_institution" content="Department of Physical Medicine and Rehabilitation, University of Pennsylvania" /> <meta name="citation_author_institution" content="Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania" /> <meta name="citation_author_institution" content="Rehabilitation Robotics Lab, Perelman School of Medicine, University of Pennsylvania" /> <meta name="citation_author_orcid" content="http://orcid.org/0000-0002-7964-0304" /> <meta name="citation_author" content="Konrad P. 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Published online October 4, 2022. doi:10.48550/arXiv.2007.04074" /> <meta name="citation_reference" content="citation_title=Advances in Neural Information Processing Systems;citation_volume=28;citation_year=2015" /> <meta name="citation_reference" content="Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011;12:2825–2830." /> <meta name="twitter:title" content="Assessing infant risk of cerebral palsy with video-based motion tracking" /> <meta name="twitter:site" content="@medrxivpreprint" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:image" content="https://www.medrxiv.org/sites/default/files/images/medrxiv_logo_homepage7-5-small-test-up.png" /> <meta name="twitter:description" content="Cerebral Palsy (CP) is a common (about 1 in 500 children) health condition caused by abnormal brain development that affects the ability to control movement. Early risk assessment happens through the General Movements Assessment (GMA), a test administered by trained clinicians at 3-4 months of age that has high predictive value for CP. With recent improvements in video-based motion tracking, automated risk assessment for CP based on the GMA is being explored. However, studies generally have used small datasets or were limited in terms of methodological rigor. Here we acquired a large dataset (1060 infants) of videos from a clinical population with elevated CP risk. In a preregistered pipeline using a lock-box set that was not used before algorithm submission we find that our machine learning predictions are highly predictive of the clinician-assessed GMA (AUC=0.79). Given its low cost, our video-based approach may be useful for clinical screening applications, particularly in low-resource settings. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by NIH-NICHD and the clinical early detection trial data collection was supported in part by the Cerebral Palsy Foundation ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of The University of Pennsylvania (Penn) gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data that is consented for sharing (movement features and trained model) are available online at the OSF pre-registration site (<https://osf.io/sd6fa>) <https://osf.io/sd6fa> <https://doi.org/10.5281/zenodo.14042732>" /> <meta name="og-title" property="og:title" content="Assessing infant risk of cerebral palsy with video-based motion tracking" /> <meta name="og-url" property="og:url" content="https://www.medrxiv.org/content/10.1101/2024.11.06.24316844v1" /> <meta name="og-site-name" property="og:site_name" content="medRxiv" /> <meta name="og-description" property="og:description" content="Cerebral Palsy (CP) is a common (about 1 in 500 children) health condition caused by abnormal brain development that affects the ability to control movement. Early risk assessment happens through the General Movements Assessment (GMA), a test administered by trained clinicians at 3-4 months of age that has high predictive value for CP. With recent improvements in video-based motion tracking, automated risk assessment for CP based on the GMA is being explored. However, studies generally have used small datasets or were limited in terms of methodological rigor. Here we acquired a large dataset (1060 infants) of videos from a clinical population with elevated CP risk. In a preregistered pipeline using a lock-box set that was not used before algorithm submission we find that our machine learning predictions are highly predictive of the clinician-assessed GMA (AUC=0.79). Given its low cost, our video-based approach may be useful for clinical screening applications, particularly in low-resource settings. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by NIH-NICHD and the clinical early detection trial data collection was supported in part by the Cerebral Palsy Foundation ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of The University of Pennsylvania (Penn) gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data that is consented for sharing (movement features and trained model) are available online at the OSF pre-registration site (<https://osf.io/sd6fa>) <https://osf.io/sd6fa> <https://doi.org/10.5281/zenodo.14042732>" /> <meta name="og-type" property="og:type" content="article" /> <meta name="og-image" property="og:image" content="https://www.medrxiv.org/sites/default/files/images/medrxiv_logo_homepage7-5-small-test-up.png" /> <meta name="citation_date" content="2024-11-07" /> <link rel="alternate" type="text/plain" title="Full Text (Plain)" href="/content/10.1101/2024.11.06.24316844v1.full.txt" /> <link rel="alternate" type="application/vnd.ms-powerpoint" title="Powerpoint" href="/content/10.1101/2024.11.06.24316844v1.ppt" /> <link rel="alternate" type="application/pdf" title="Full Text (PDF)" href="/content/10.1101/2024.11.06.24316844v1.full.pdf" /> <meta name="description" content="medRxiv - The Preprint Server for Health Sciences" /> <meta name="generator" content="Drupal 7 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data-apath="/medrxiv/early/2024/11/07/2024.11.06.24316844.atom" data-hw-author-tooltip-instance=""><div class="highwire-cite highwire-cite-highwire-article highwire-citation-medrxiv-article-top clearfix has-author-tooltip" > <h1 class="highwire-cite-title" id="page-title">Assessing infant risk of cerebral palsy with video-based motion tracking</h1> <div class="highwire-cite-authors" ><span class="highwire-citation-authors"><span class="highwire-citation-author first hw-author-orcid-logo-wrapper" data-delta="0"><a href="http://orcid.org/0000-0001-7862-6749" target="_blank" class="hw-author-orcid-logo link-icon-only link-icon"><span class="hw-icon-orcid hw-icon-color-orcid"></span> <span class="title element-invisible">View ORCID Profile</span></a><span class="nlm-given-names">Melanie</span> <span class="nlm-surname">Segado</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Laura</span> <span class="nlm-surname">Prosser</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Andrea F.</span> <span class="nlm-surname">Duncan</span></span>, <span class="highwire-citation-author hw-author-orcid-logo-wrapper" data-delta="3"><a href="http://orcid.org/0000-0002-7964-0304" target="_blank" class="hw-author-orcid-logo link-icon-only link-icon"><span class="hw-icon-orcid hw-icon-color-orcid"></span> <span class="title element-invisible">View ORCID Profile</span></a><span class="nlm-given-names">Michelle J.</span> <span class="nlm-surname">Johnson</span></span>, <span class="highwire-citation-author hw-author-orcid-logo-wrapper" data-delta="4"><a href="http://orcid.org/0000-0001-8408-4499" target="_blank" class="hw-author-orcid-logo link-icon-only link-icon"><span class="hw-icon-orcid hw-icon-color-orcid"></span> <span class="title element-invisible">View ORCID Profile</span></a><span class="nlm-given-names">Konrad P.</span> <span class="nlm-surname">Kording</span></span></span></div> <div class="highwire-cite-metadata" ><span class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">doi:</span> https://doi.org/10.1101/2024.11.06.24316844 </span></div> </div> <div id="hw-article-author-popups-node-895565--21314513859" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Melanie Segado </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Department of Bioengineering, University of Pennsylvania</span>, Philadelphia, PA, <span class='nlm-country'>United States</span></div><div class='author-affiliation'><span class='nlm-sup'>2</span><span class='nlm-institution'>Department of Neuroscience, University of Pennsylvania</span>, Philadelphia, PA, <span class='nlm-country'>United State</span></div><div class='author-affiliation'><span class='nlm-sup'>5</span><span class='nlm-institution'>Division of Rehabilitation Medicine, The Children’s Hospital of Philadelphia</span>, Philadelphia, PA, <span class='nlm-country'>USA</span></div>PhD</span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Melanie%2BSegado%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Segado%20M&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AMelanie%2BSegado%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="http://orcid.org/0000-0001-7862-6749" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Melanie Segado</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Laura Prosser </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>4</span><span class='nlm-institution'>Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania</span>, Philadelphia, PA, <span class='nlm-country'>USA</span></div><div class='author-affiliation'><span class='nlm-sup'>5</span><span class='nlm-institution'>Division of Rehabilitation Medicine, The Children’s Hospital of Philadelphia</span>, Philadelphia, PA, <span class='nlm-country'>USA</span></div>PT, PhD</span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Laura%2BProsser%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Prosser%20L&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3ALaura%2BProsser%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-corresp-email-link last"><span>For correspondence: <a href="mailto:prosserl@chop.edu" class="" data-icon-position="" data-hide-link-title="0">prosserl@chop.edu</a></span></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">Andrea F. Duncan </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>4</span><span class='nlm-institution'>Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania</span>, Philadelphia, PA, <span class='nlm-country'>USA</span></div><div class='author-affiliation'><span class='nlm-sup'>6</span><span class='nlm-institution'>Division of Neonatology and Department of Pediatrics, Children’s Hospital of Philadelphia</span></div>MD, MS</span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Andrea%2BF.%2BDuncan%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Duncan%20AF&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AAndrea%2BF.%2BDuncan%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">Michelle J. Johnson </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Department of Bioengineering, University of Pennsylvania</span>, Philadelphia, PA, <span class='nlm-country'>United States</span></div><div class='author-affiliation'><span class='nlm-sup'>7</span><span class='nlm-institution'>Department of Physical Medicine and Rehabilitation, University of Pennsylvania</span>, Philadelphia, PA, <span class='nlm-country'>USA</span></div><div class='author-affiliation'><span class='nlm-sup'>8</span><span class='nlm-institution'>Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania</span>, Philadelphia, PA, <span class='nlm-country'>USA</span></div><div class='author-affiliation'><span class='nlm-sup'>9</span><span class='nlm-institution'>Rehabilitation Robotics Lab, Perelman School of Medicine, University of Pennsylvania</span>, Philadelphia, PA, <span class='nlm-country'>USA</span></div>PhD</span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Michelle%2BJ.%2BJohnson%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Johnson%20MJ&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AMichelle%2BJ.%2BJohnson%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="http://orcid.org/0000-0002-7964-0304" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Michelle J. Johnson</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">Konrad P. Kording </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Department of Bioengineering, University of Pennsylvania</span>, Philadelphia, PA, <span class='nlm-country'>United States</span></div><div class='author-affiliation'><span class='nlm-sup'>2</span><span class='nlm-institution'>Department of Neuroscience, University of Pennsylvania</span>, Philadelphia, PA, <span class='nlm-country'>United State</span></div>PhD</span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Konrad%2BP.%2BKording%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Kording%20KP&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AKonrad%2BP.%2BKording%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="http://orcid.org/0000-0001-8408-4499" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Konrad P. Kording</a></li></ul></div></div></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-highwire-panel-tabs pane-panels-ajax-tab-tabs" > <div class="pane-content"> <div class="item-list"><ul class="tabs inline panels-ajax-tab"><li class="first"><a href="/content/10.1101/2024.11.06.24316844v1" class="panels-ajax-tab-tab" data-panel-name="biorxiv_tab_art" data-target-id="highwire_article_tabs" data-entity-context="node:895565" data-trigger="" data-url-enabled="1">Abstract</a><a href="/panels_ajax_tab/biorxiv_tab_art/node:895565/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/2024.11.06.24316844v1.full-text" class="panels-ajax-tab-tab" data-panel-name="article_tab_full_text" data-target-id="highwire_article_tabs" data-entity-context="node:895565" data-trigger="full-text" data-url-enabled="1">Full Text</a><a href="/panels_ajax_tab/article_tab_full_text/node:895565/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/2024.11.06.24316844v1.article-info" class="panels-ajax-tab-tab" data-panel-name="biorxiv_tab_info" data-target-id="highwire_article_tabs" data-entity-context="node:895565" data-trigger="article-info" data-url-enabled="1">Info/History</a><a href="/panels_ajax_tab/biorxiv_tab_info/node:895565/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/2024.11.06.24316844v1.article-metrics" class="panels-ajax-tab-tab" data-panel-name="article_tab_metrics" data-target-id="highwire_article_tabs" data-entity-context="node:895565" data-trigger="article-metrics" data-url-enabled="1">Metrics</a><a href="/panels_ajax_tab/article_tab_metrics/node:895565/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/2024.11.06.24316844v1.external-links" class="panels-ajax-tab-tab" data-panel-name="article_tab_data_code" data-target-id="highwire_article_tabs" data-entity-context="node:895565" data-trigger="external-links" data-url-enabled="1">Data/Code</a><a href="/panels_ajax_tab/article_tab_data_code/node:895565/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li class="last"><a href="/content/10.1101/2024.11.06.24316844v1.full.pdf+html" class="panels-ajax-tab-tab" data-panel-name="biorxiv_tab_pdf" data-target-id="highwire_article_tabs" data-entity-context="node:895565" data-trigger="full.pdf+html" data-url-enabled="1"><i class="icon-file-alt"></i> Preview PDF</a><a href="/panels_ajax_tab/biorxiv_tab_pdf/node:895565/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li></ul></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-highwire-panel-tabs-container" > <div class="pane-content"> <div data-panels-ajax-tab-preloaded="biorxiv_tab_art" id="panels-ajax-tab-container-highwire_article_tabs" class="panels-ajax-tab-container"><div class="panels-ajax-tab-loading" style ="display:none"><img class="loading" src="https://www.medrxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif" alt="Loading" title="Loading" /></div><div class="panels-ajax-tab-wrap-biorxiv_tab_art"><div class="panel-display panel-1col clearfix" > <div class="panel-panel panel-col"> <div><div class="panel-pane pane-highwire-markup" > <div class="pane-content"> <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" data-highwire-cite-ref-tooltip-instance="highwire_reflinks_tooltip" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div class="article abstract-view "><span class="highwire-journal-article-marker-start"></span><div class="section abstract" id="abstract-1"><h2 class="">Abstract</h2><p id="p-2">Cerebral Palsy (CP) is a common (about 1 in 500 children) health condition caused by abnormal brain development that affects the ability to control movement. Early risk assessment happens through the General Movements Assessment (GMA), a test administered by trained clinicians at 3-4 months of age that has high predictive value for CP. With recent improvements in video-based motion tracking, automated risk assessment for CP based on the GMA is being explored. However, studies generally have used small datasets or were limited in terms of methodological rigor. Here we acquired a large dataset (1060 infants) of videos from a clinical population with elevated CP risk. In a preregistered pipeline using a lock-box set that was not used before algorithm submission we find that our machine learning predictions are highly predictive of the clinician-assessed GMA (AUC=0.79). Given its low cost, our video-based approach may be useful for clinical screening applications, particularly in low-resource settings.</p></div><h3>Competing Interest Statement</h3><p id="p-3">The authors have declared no competing interest.</p><h3>Funding Statement</h3><p id="p-4">This work was funded by NIH-NICHD and the clinical early detection trial data collection was supported in part by the Cerebral Palsy Foundation</p><h3>Author Declarations</h3><p id="p-5">I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.</p><p id="p-6">Yes</p><p id="p-7">The details of the IRB/oversight body that provided approval or exemption for the research described are given below:</p><p id="p-8">Ethics committee/IRB of The University of Pennsylvania (Penn) gave ethical approval for this work</p><p id="p-9">I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.</p><p id="p-10">Yes</p><p id="p-11">I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).</p><p id="p-12">Yes</p><p id="p-13">I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.</p><p id="p-14">Yes</p><div class="section data-availability" id="sec-12"><h2 class="">Data Availability</h2><p id="p-45">All data that is consented for sharing (movement features and trained model) are available online at the OSF pre-registration site (<a href="https://osf.io/sd6fa">https://osf.io/sd6fa</a>)</p><p id="p-46"> <a href="https://osf.io/sd6fa">https://osf.io/sd6fa</a> </p><p id="p-47"> <a href="https://doi.org/10.5281/zenodo.14042732">https://doi.org/10.5281/zenodo.14042732</a> 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