{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T12:23:54Z","timestamp":1777551834703,"version":"3.51.4"},"reference-count":45,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Interdisciplinary Project of Dalian University","award":["DLUXK-2024-QN-014"],"award-info":[{"award-number":["DLUXK-2024-QN-014"]}]},{"name":"Interdisciplinary Project of Dalian University","award":["DLUXK-2025- FX-001"],"award-info":[{"award-number":["DLUXK-2025- FX-001"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives:<\/jats:title>\n                    <jats:p>To develop and validate a smartphone video-based framework using deep learning for quantifying smooth-pursuit abnormalities in Parkinson\u2019s disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      Smartphone videos (\n                      <jats:italic toggle=\"yes\">N<\/jats:italic>\n                      = 54) from 18 patients with confirmed Parkinson\u2019s disease were rigorously annotated to identify 1767 event-level samples (2-second windows), comprising 941 normal and 826 abnormal smooth-pursuit events. Ocular landmarks were extracted using MediaPipe FaceLandmarker. Preprocessing steps included canthus-referenced spatial normalization, Kalman smoothing, and blink filtering. Event samples were encoded as kinematic feature sequences and classified using DP-MDLA Net, a dual-path multi-scale dilated-LSTM attention architecture that fuses convolutional and recurrent representations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Under a random split regimen for event samples, the framework achieved 96.59% accuracy, 97.50% precision, 95.12% recall, 96.03% F1-score, and an AUC of 0.9939 on the test set (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      = 176). Five-fold cross-validation yielded a mean accuracy of 93.04% (SD 1.86%) and a mean AUC of 0.9735 (SD 0.0102). Subject-independent validation (disjoint split by patient) produced an accuracy of 93.57% and an AUC of 0.9693. Ablation without normalization decreased accuracy to 84.09% and AUC to 0.9323, indicating the critical role of landmark-based spatial alignment.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The framework enables robust event-level quantification of smooth-pursuit abnormalities from smartphone video, supporting portable bedside assessment and standardized longitudinal monitoring of Parkinson\u2019s disease without specialized equipment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261421158","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:23:11Z","timestamp":1770834191000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["DP-MDLA Net: Detection of smooth pursuit abnormalities in Parkinson\u2019s disease"],"prefix":"10.1177","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3693-9798","authenticated-orcid":false,"given":"Zhiyuan","family":"Tan","sequence":"first","affiliation":[{"name":"Dalian University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhao","sequence":"additional","affiliation":[{"name":"Affiliated Zhongshan Hospital of Dalian University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liwen","family":"Kong","sequence":"additional","affiliation":[{"name":"Dalian University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinfen","family":"Song","sequence":"additional","affiliation":[{"name":"Dalian University Affiliated Xinhua Hospital"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qijie","family":"Zou","sequence":"additional","affiliation":[{"name":"Dalian University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yana","family":"Lv","sequence":"additional","affiliation":[{"name":"Dalian University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanshan","family":"Liang","sequence":"additional","affiliation":[{"name":"Affiliated Zhongshan Hospital of Dalian University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Tao","sequence":"additional","affiliation":[{"name":"Dalian University"},{"name":"Affiliated Zhongshan Hospital of Dalian University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"Parkinson\u2019s Foundation. 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