{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T06:04:59Z","timestamp":1789365899545,"version":"build-2803163510"},"reference-count":40,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T00:00:00Z","timestamp":1789257600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T00:00:00Z","timestamp":1789257600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003032","name":"Association Nationale de la Recherche et de la Technologie","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003032","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011004","name":"Minist\u00e8re de l'Agriculture et de l'Alimentation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011004","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["beva.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Equine Veterinary Journal"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>In clinical practice, lameness evaluation often relies on visual assessment under different examination conditions, including ground and figures, and remains subjective. Although objective symmetry indices provide quantitative measures of gait asymmetry, their interpretation across multiple conditions remains challenging because several complementary indices are required and their thresholds vary with context.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To assess the ability of a context\u2010aware machine learning approach to predict lameness side and severity at the trot using trunk\u2010mounted IMUs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Retrospective observational study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A neural network incorporating contextual parameters (ground, figure) via feature\u2010wise linear modulation (FiLM) was trained on symmetry indices derived from head, withers and pelvis motion in 625 horses examined during routine clinical evaluations, after exclusion of bilateral lameness. Reference lameness grades were extracted from clinical examination reports for each recording and aggregated into four ordinal severity levels. The model generated a predicted lameness class and an associated confidence score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Exact accuracy reached 46% (43%\u201353% across contexts) for forelimbs and 47% (45%\u201352%) for hindlimbs. Most errors occurred between neighbouring ordinal categories, while left\u2013right limb inversion remained below 3%. A limited subset of high\u2010confidence predictions showed improved accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main Limitations<\/jats:title>\n                    <jats:p>Reference labels were based on subjective clinical grading, without systematic confirmation by diagnostic analgesia or repeated objective measurements. Additional limitations include the single\u2010clinic dataset, limited number of evaluators, and separate forelimb and hindlimb models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This proof\u2010of\u2010concept study shows that a context\u2010aware approach can integrate multiple IMU\u2010derived indices across examination conditions to predict lameness side and severity. Agreement with subjective clinical grades was moderate, and most errors occurred between adjacent severity categories, whereas left\u2013right limb inversion was uncommon. Further validation using stronger reference standards and independent datasets is required before clinical relevance can be assessed.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/evj.70326","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T05:56:07Z","timestamp":1789365367000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Context\u2010aware machine learning for lameness side and severity prediction in trotting horses: A proof\u2010of\u2010concept study"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9616-0918","authenticated-orcid":false,"given":"Mahaut","family":"G\u00e9rard","sequence":"first","affiliation":[{"name":"Ecole Nationale V\u00e9t\u00e9rinaire d'Alfort ACAP3, CHUV\u2010Eq  Goustranville France"},{"name":"Applied artificial intelligence Institut (I2A) TELUQ University  Qu\u00e9bec Canada"},{"name":"LIM Group  Nontron 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