{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T04:19:44Z","timestamp":1792815584092,"version":"4.5.0"},"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T00:00:00Z","timestamp":1712620800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Instrumented gait analysis (IGA) has been around for a long time but has never been shown to be useful for improving patient outcomes. In this study we demonstrate the potential utility of IGA by showing that machine learning models are better able to estimate treatment outcomes when they include both IGA and clinical (CLI) features compared to when they include CLI features alone.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>We carried out a retrospective analysis of data from ambulatory children diagnosed with cerebral palsy who were seen at least twice at our gait analysis center. Individuals underwent a variety of treatments (including no treatment) between sequential gait analyses. We fit Bayesian Additive Regression Tree (BART) models that estimated outcomes for mean stance foot progression to demonstrate the approach. We built two models: one using CLI features only, and one using CLI and IGA features. We then compared the models\u2019 performance in detail. We performed similar, but less detailed, analyses for a number of other outcomes. All results were based on independent test data from a 70%\/30% training\/testing split.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The IGA model was more accurate than the CLI model for mean stance-phase foot progression outcomes (RMSE\n                      <jats:sub>IGA<\/jats:sub>\n                      = 11\n                      <jats:sup>\u2218<\/jats:sup>\n                      , RMSE\n                      <jats:sub>CLI<\/jats:sub>\n                      = 13\n                      <jats:sup>\u2218<\/jats:sup>\n                      ) and explained more than 1.5 \u00d7 as much of the variance (R\n                      <jats:sup>2<\/jats:sup>\n                      <jats:sub>\n                        <jats:italic>IGA<\/jats:italic>\n                      <\/jats:sub>\n                      = .45, R\n                      <jats:sup>2<\/jats:sup>\n                      <jats:sub>\n                        <jats:italic>CLI<\/jats:italic>\n                      <\/jats:sub>\n                      = .28). The IGA model outperformed the CLI model for every level of treatment complexity, as measured by number of simultaneous surgeries. The IGA model also exhibited superior performance for estimating outcomes of mean stance-phase knee flexion, mean stance-phase ankle dorsiflexion, maximum swing-phase knee flexion, gait deviation index (GDI), and dimensionless speed.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>\n                      The results show that IGA has the\n                      <jats:italic>potential<\/jats:italic>\n                      to be useful in the treatment planning process for ambulatory children diagnosed with cerebral palsy. We propose that the results of machine learning outcome estimators\u2014including estimates of uncertainty\u2014become the primary IGA tool utilized in the clinical process, complementing the standard medical practice of conducting a through patient history and physical exam, eliciting patient goals, reviewing relevant imaging data, and so on.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0301230","type":"journal-article","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T13:48:59Z","timestamp":1712670539000},"page":"e0301230","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":21,"title":["Demonstrating the utility of Instrumented Gait Analysis in the treatment of children with cerebral palsy"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3388-7413","authenticated-orcid":true,"given":"Michael H.","family":"Schwartz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1235-3714","authenticated-orcid":true,"given":"Andrew J.","family":"Ries","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3624-8273","authenticated-orcid":true,"given":"Andrew G.","family":"Georgiadis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5296-6040","authenticated-orcid":true,"given":"Hans","family":"Kainz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"key":"pone.0301230.ref001","first-page":"225","author":"P.E. 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