{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T14:10:21Z","timestamp":1784815821024,"version":"3.55.0"},"reference-count":44,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Huntington's Disease"],"abstract":"<jats:p>Understanding the trajectory of Huntington's disease (HD) is critical for patient stratification and the development of targeted interventions. Traditionally, studies relied on age\u2013CAG models to estimate disease onset and progression, based on the well-established relationship between CAG repeat length and age at onset. However, additional genetic, environmental, and clinical factors can cause substantial variability. Recent machine learning approaches integrate clinical, imaging, and molecular data for more precise prediction of disease progression. Following PRISMA guidelines, we systematically reviewed studies on HD onset and progression. Using Web of Science, PubMed, and IEEE Xplore, 20 studies published between 2003 and 2024 met the inclusion criteria. We analyzed the machine learning approaches and input features used, assessed methodological quality, and evaluated risk of bias using the PROBAST tool. Overall, machine learning models, particularly support vector machines and ensemble approaches, consistently outperformed traditional age\u2013CAG models. Several studies predicted conversion from premanifest to manifest HD within 5\u201310 years with high accuracy (88\u201398%). Beyond predicting onset, machine learning models have also been used to model dis-ease progression using clinical scores assessing motor, cognitive, and functional impairment. Performance was higher in studies incorporating structural and functional MRI biomarkers, and improved further with longitudinal clinical integration, enabling pre-diction of decline years before symptoms onset. Overall, machine learning shows strong potential to improve prognostic modeling in HD, especially through multimodal and longitudinal data. However, common methodological weaknesses and bias highlight the need for larger, externally validated studies using objective biomarkers.<\/jats:p>","DOI":"10.1177\/18796397261468824","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T11:58:59Z","timestamp":1784635139000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning applications in Huntington's disease prognosis: A review"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7686-5364","authenticated-orcid":false,"given":"Lubna M.","family":"Abu Zohair","sequence":"first","affiliation":[{"name":"Heriot-Watt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6337-0952","authenticated-orcid":false,"given":"Ruben","family":"Andriessen","sequence":"additional","affiliation":[{"name":"Maastricht University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9622-928X","authenticated-orcid":false,"given":"Noor","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Al Basheer Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7674-0825","authenticated-orcid":false,"given":"Moram","family":"Khalid","sequence":"additional","affiliation":[{"name":"Heriot-Watt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3804-0513","authenticated-orcid":false,"given":"Hind","family":"Zantout","sequence":"additional","affiliation":[{"name":"Heriot-Watt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9957-954X","authenticated-orcid":false,"given":"Marta","family":"Vallejo","sequence":"additional","affiliation":[{"name":"Heriot-Watt University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0924-9338(01)00604-6"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.3233\/JHD-170270"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/mds.29228"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.14802\/jmd.11016"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/mdc3.13148"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/psb.1824"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1399-0004.2004.00241.x"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2022.1034269"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2014.00078"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1002\/mdc3.12646"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1002\/mds.27227"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00415-024-12771-w"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2022.874208"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.21037\/jmai-25-13"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13233592"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.7326\/M18-1377"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1388-2457(03)00074-9"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.06.003"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.04.273"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1002\/mds.26364"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12343"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11682-016-9660-0"},{"key":"e_1_3_3_24_2","doi-asserted-by":"crossref","unstructured":"Eirola E Akusok A Bj\u00f6rk K-M et al. 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