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Nevertheless, these criteria lack significance to discriminate VEXAS from other inflammatory conditions at the screening step. This work hence first focused on singling out dysplastic features indicative of the syndrome among peripheral blood (PB) polymorphonuclears (PMN). A deep learning algorithm is then proposed for automatic detection of these features.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A multicentric dataset, comprising 9514 annotated PMN images was gathered, including <jats:italic>UBA1<\/jats:italic> mutated VEXAS (<jats:italic>n<\/jats:italic>\u2009=\u200925), <jats:italic>UBA1<\/jats:italic> wildtype myelodysplastic (<jats:italic>n<\/jats:italic>\u2009=\u200914), and <jats:italic>UBA1<\/jats:italic> wildtype cytopenic patients (<jats:italic>n<\/jats:italic>\u2009=\u200925). Statistical analysis on a subset of patients was performed to screen for significant abnormalities. Detection of these features on PB was then automated with a convolutional neural network (CNN) for multilabel classification.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Significant differences were observed in the proportions of PMNs with pseudo\u2010Pelger, nuclear spikes, vacuoles, and hypogranularity between patients with VEXAS and both cytopenic and myelodysplastic controls.<\/jats:p><jats:p>Automatic detection of these abnormalities yielded AUCs in the range [0.85\u20130.97] and a F1\u2010score of 0.70 on the test set. A VEXAS screening score was proposed, leveraging the model outputs and predicting the <jats:italic>UBA1<\/jats:italic> mutational status with 0.82 sensitivity and 0.71 specificity on the test patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study suggests that computer\u2010assisted analysis of PB smears, focusing on suspected VEXAS cases, can provide valuable insights for determining which patients should undergo molecular testing. The presented deep learning approach can help hematologists direct their suspicions before initiating further analyses.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ijlh.14368","type":"journal-article","created":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T08:49:48Z","timestamp":1726303788000},"page":"120-129","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning\u2010Based Blood Abnormalities Detection as a Tool for <scp>VEXAS<\/scp> Syndrome Screening"],"prefix":"10.1111","volume":"47","author":[{"given":"C\u00e9dric","family":"De Almeida Braga","sequence":"first","affiliation":[{"name":"Nantes Universit\u00e9, \u00c9cole Centrale Nantes  Nantes France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxence","family":"Bauvais","sequence":"additional","affiliation":[{"name":"Hematology Biology Nantes University Hospital  Nantes 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