ArticleBMC medical informatics and decision making2022
Advancing artificial intelligence-assisted pre-screening for fragile X syndrome.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Use of Machine Learning to Identify Markers of Risk for Fragile X-Associated Tremor/Ataxia Syndrome: A Preliminary Analysis.Annals of neurology · 2026Article
- Expanded Carrier Screening: Current Evidence and Future Directions in the Era of Population Genomics.Genes · 2026Review
- Fragile X Syndrome in Adulthood: A Study of Electronic Health Records.American journal on intellectual and developmental disabilities · 2026Article
- Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies.BMC medical informatics and decision making · 2024Article
- Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity.Orphanet journal of rare diseases · 2024Article
- Harnessing the potential of human induced pluripotent stem cells, functional assays and machine learning for neurodevelopmental disorders.Frontiers in neuroscience · 2024Review
- Phenotypic presentation of Mendelian disease across the diagnostic trajectory in electronic health records.Genetics in medicine : official journal of the American College of Medical Genetics · 2023Article
- Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- Combined extended reality and reinforcement learning to promote healthcare and reduce social anxiety in fragile X syndrome: a new assessment tool and a rehabilitative strategy.Frontiers in psychology · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundFragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism, is significantly underdiagnosed in the general population. 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.
methodsThe 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 years (1988-2021) 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 = 759.83, ICD10 = Q99.2). Only individuals who received the FXS code on at least two occasions ("Rule of 2") 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 years 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 = 1 represents perfect classification).
results52 individuals were identified as target cases and matched with 5200 controls. AI-assisted pre-screening tool successfully identified cases with FXS, 5 years 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.
conclusionsThis 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' health outcomes.
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