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All lumbar puncture procedures in the idiopathic intracranial hypertension group were performed under benzodiazepine sedation. Twenty-four demographic and quantitative MRI features (optic nerve sheath and globe metrics, pituitary measurements, venous sinus findings, and posterior fossa measures) were extracted. Six classifiers (random forest, support vector machine, multilayer perceptron, XGBoost,\n                      <jats:italic>k<\/jats:italic>\n                      -nearest neighbors, and Bagging) were trained using repeated nested cross-validation with Bayesian hyperparameter optimization; performance was summarized on held-out folds.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Across models, accuracies were approximately 0.70\u20130.74, sensitivities approximately 0.64\u20130.72, and specificities approximately 0.69\u20130.84; among non\u2013support vector machine models, the area under the receiver operating characteristic curve was approximately 0.80\u20130.82. Feature selection consistently retained anatomically plausible markers, led by optic nerve sheath measurements, posterior globe flattening, pituitary\/sella configuration, and venous sinus abnormalities. The resulting models generate uncalibrated predicted probabilities for decision support rather than binary diagnostic labels. Performance was estimated across repeated validation splits.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine-learning applied to routinely obtainable MRI markers yields moderate diagnostic performance for pediatric idiopathic intracranial hypertension. Given that lumbar puncture remains the gold standard, these models are intended as decision-support tools to complement clinical assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00247-026-06638-7","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T08:38:02Z","timestamp":1779525482000},"page":"1516-1535","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Diagnostic value of machine-learning using conventional magnetic resonance imaging markers for pediatric idiopathic intracranial hypertension: a retrospective study"],"prefix":"10.1007","volume":"56","author":[{"given":"Ferhat","family":"Cuce","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gokalp","family":"Tulum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammet Ikbal","family":"I\u015f\u0131k","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehmet","family":"Ba\u015ftemur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mutluay","family":"Arslan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B\u00fclent","family":"\u00dcnay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Onur","family":"Osman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,23]]},"reference":[{"key":"6638_CR1","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1212\/WNL.0b013e3182a55f17","volume":"81","author":"DI Friedman","year":"2013","unstructured":"Friedman DI, Liu GT, Digre KB (2013) Revised diagnostic criteria for the pseudotumor cerebri syndrome in adults and children. 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