{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T05:54:15Z","timestamp":1781848455932,"version":"3.54.5"},"reference-count":66,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Developmental dyslexia refers to a common neurodevelopmental disorder, which impairs the accuracy and fluency of reading, and early identification is vital for initiating timely intervention. Nonetheless, the traditional methods of formal assessment are time- and resource-intensive, which limits their scalability. Machine-learning approaches and eye-tracking technologies provide objective, data-driven solutions for dyslexia screening. This research integrates current evidence on eye-movement-based and emerging multimodal computational methods for dyslexia screening, risk identification, and algorithmic classification during reading tasks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>PubMed, Scopus, Web of Science, and CINAHL were searched systematically to identify studies published between January 2015 and March 2026. Eligible studies included analysis of eye-movement obtained via eye tracking or electrooculography (EOG), with or without predictive modeling. Methodological quality was assessed using JBI, PROBAST, ROBINS-I, and COSMIN tools.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Twenty-three articles were included out of 50 full-text articles screened comprising eye-movement biomarker\/observational studies (\n                      <jats:italic>n<\/jats:italic>\n                      = 5), machine-learning prediction-model studies (\n                      <jats:italic>n<\/jats:italic>\n                      = 14), intervention response studies (\n                      <jats:italic>n<\/jats:italic>\n                      = 2), and reliability\/feasibility studies (\n                      <jats:italic>n<\/jats:italic>\n                      = 2). The sample sizes ranged from small experimental cohorts (&amp;lt;20 participants) to larger datasets (&amp;gt;300 participants). In the literature, dyslexic readers were consistently found to exhibit longer fixation durations, increased regression behavior and reduced saccadic efficiency. Machine-learning algorithms using fixation, saccade, scan path, and signal-based features demonstrated classification accuracies ranging from approximately 80 to 95% with some studies reporting values approaching 99% under specific experimental conditions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Nevertheless, there was a significant heterogeneity in datasets, feature extraction methods, outcome definitions and validation schemes. Notably, numerous studies used proxy diagnostic labels, small or internally derived datasets, and internal cross-validation, which introduces the risk of overfitting and performance inflation. Explicit multimodal or multi-source modeling was identified in three of 23 studies involving combinations of gaze data with demographic, cognitive, linguistic, VR-bed, text-derived, saliency-map, or CNN-based features. Two additional studies used EOG as an alternative eye-movement signal modality rather than true multi-source fusion. Therefore, the evidence base remains dominated by eye-movement and gaze-derived approaches, while multimodal evidence should be interpreted as emerging and exploratory. Altogether, eye-movement based computational systems are a promising, non-invasive method for scalable dyslexia screening.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic review registration<\/jats:title>\n                    <jats:p>PROSPERO, identifier (RD42061332527).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1847464","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T05:42:39Z","timestamp":1781847759000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-enabled eye-movement and emerging multimodal frameworks for precision dyslexia screening and reading pattern analysis"],"prefix":"10.3389","volume":"13","author":[{"given":"Ashit Kumar","family":"Dutta","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Systems, College of Medical Sciences, AlMaarefa University","place":["Dariyah, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moattar Raza","family":"Rizvi","sequence":"additional","affiliation":[{"name":"Faculty of Allied Health Sciences, Santosh Deemed to be University","place":["Ghaziabad, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farha Mujeeb Ahmed","family":"Shaikh","sequence":"additional","affiliation":[{"name":"Department of Basic Medical Science, College of Medicine, AlMaarefa University","place":["Diriyah, Riyadh, Saudi Arabia"]},{"name":"King Salman Center for Disability Research","place":["Riyadh, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adel Mefleh","family":"Widyan","sequence":"additional","affiliation":[{"name":"King Salman Center for Disability Research","place":["Riyadh, Saudi Arabia"]},{"name":"Department of Mathematics, College of Science, Qassim University","place":["Buraydah, Saudi 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