{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T20:29:23Z","timestamp":1786566563949,"version":"build-2736575974"},"reference-count":50,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","award":["RS\u20102025\u201002221011"],"award-info":[{"award-number":["RS\u20102025\u201002221011"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Brain age estimation provides a noninvasive MRI biomarker of neurodevelopment. In infancy, rapid regionally ordered myelination reflects brain maturation, yet early\u2010life brain age estimation remains underexplored, particularly with myelination\u2010sensitive MRI and biologically informed modeling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and evaluate a biologically informed deep learning framework for infant brain age estimation using T1w\/T2w ratio MRI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>Internal cohort: 629 infants aged 0\u201324\u2009months (626 with age\u2010appropriate myelination, train\/validation\/test\u2009=\u2009376\/125\/125), 3 with myelin\u2010related developmental abnormalities for qualitative review. External cohort: 10 healthy infants aged 0\u201315\u2009months (5 females, 5 males).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>Internal: 3T; 3D gradient\u2010echo or 2D spin\u2010echo T1w, and 2D turbo spin\u2010echo T2w. External: 3T; 3D gradient\u2010echo T1w and 2D turbo spin\u2010echo T2w.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>3D convolutional neural networks were trained with T1w, T2w, and T1w\/T2w ratio inputs using manually defined biological age labels from visual myelination assessment. The model incorporated multi\u2010task learning for age regression, white matter segmentation, and image reconstruction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>Performance was evaluated using five\u2010fold cross\u2010validation with repeated random splits. Metrics included mean absolute error, root mean squared error, , and Pearson and Spearman correlations. Modality differences were tested using one\u2010way ANOVA, \u2010tests, and Mann\u2013Whitney , with Cohen's  and 95% confidence intervals. In the external cohort, absolute prediction errors were compared using the Wilcoxon signed\u2010rank test. Statistical significance was defined as .<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>T1w\/T2w ratio models achieved the best overall performance (MAE: 1.489\u20090.302\u2009months; \u2009=\u20090.966\u2009\u20090.012), compared with T1w (2.055\u2009\u20090.944; 0.933\u2009\u20090.061), T2w (1.794\u2009\u20090.434; 0.947\u2009\u20090.023), T1w+T2w (1.546\u2009\u20090.291; 0.960\u2009\u20090.013), and T1w+T2w+RI (1.498\u2009\u20090.313; 0.9630.012). Modality effects were significant for MAE, RMSE, , , but not for  (). Auxiliary\u2010task and multi\u2010scale modeling numerically improved performance (MAE, 1.203\u2009months; \u2009=\u20090.979). External validation showed the lowest error for the RI\u2010based model (MAE, 1.16\u2009months), and Grad\u2010CAM highlighted myelination\u2010relevant white matter.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>T1w\/T2w ratio MRI combined with biologically informed deep learning enabled accurate and interpretable infant brain age estimation. This framework showed promising cross\u2010scanner performance and may support MRI\u2010based assessment of early brain maturation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Evidence Level<\/jats:title>\n                    <jats:p>3.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Technical Efficacy<\/jats:title>\n                    <jats:p>2.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmri.70376","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T17:15:49Z","timestamp":1780679749000},"page":"787-801","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Infant Brain Age Estimation With T1w\/T2w Ratio\n                    <scp>MRI<\/scp>\n                    : A Myelination\u2010Aware Deep Learning Approach"],"prefix":"10.1002","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5342-8129","authenticated-orcid":false,"given":"Hyeryn","family":"Park","sequence":"first","affiliation":[{"name":"DEEPNOID Inc.  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