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Diagnosing FXS is challenging due to the heterogeneity of the condition, subtle physical characteristics at the time of birth and similarity of phenotypes to other conditions. The medical complexity of FXS underscores an urgent need to develop more efficient and effective screening methods to identify individuals with FXS. In this study, we evaluate the effectiveness of using artificial intelligence (AI) and electronic health records (EHRs) to accelerate FXS diagnosis.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>The EHRs of 2.1 million patients served by the University of Wisconsin Health System (UW Health) were the main data source for this retrospective study. UW Health includes patients from south central Wisconsin, with approximately 33\u00a0years (1988\u20132021) of digitized health data. We identified all participants who received a code for FXS in the form of International Classification of Diseases (ICD), Ninth or Tenth Revision (ICD9\u2009=\u2009759.83, ICD10\u2009=\u2009Q99.2). Only individuals who received the FXS code on at least two occasions (\u201cRule of 2\u201d) were classified as clinically diagnosed cases. To ensure the availability of sufficient data prior to clinical diagnosis to test the model, only individuals who were diagnosed after age 10 were included in the analysis. A supervised random forest classifier was used to create an AI-assisted pre-screening tool to identify cases with FXS, 5\u00a0years earlier than the time of clinical diagnosis based on their medical records. The area under receiver operating characteristic curve (AUROC) was reported. The AUROC shows the level of success in identification of cases and controls (AUROC\u2009=\u20091 represents perfect classification).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>52 individuals were identified as target cases and matched with 5200 controls. AI-assisted pre-screening tool successfully identified cases with FXS, 5\u00a0years earlier than the time of clinical diagnosis with an AUROC of 0.717. A separate model trained and tested on UW Health cases achieved the AUROC of 0.798.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>This result shows the potential utility of our tool in accelerating FXS diagnosis in real clinical settings. Earlier diagnosis can lead to more timely intervention and access to services with the goal of improving patients\u2019 health outcomes.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-01896-5","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T05:04:09Z","timestamp":1654837449000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Advancing artificial intelligence-assisted pre-screening for fragile X syndrome"],"prefix":"10.1186","volume":"22","author":[{"given":"Arezoo","family":"Movaghar","sequence":"first","affiliation":[]},{"given":"David","family":"Page","sequence":"additional","affiliation":[]},{"given":"Murray","family":"Brilliant","sequence":"additional","affiliation":[]},{"given":"Marsha","family":"Mailick","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"issue":"1","key":"1896_CR1","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","volume":"17","author":"CJ Kelly","year":"2019","unstructured":"Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. 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