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However, the routine MR sequences for patients with IDD are T1\u2010 and T2\u2010MR, T1\u03c1\u2010MR is not commonly used due to long scanning time and extra expenses, which limits the application of T1\u03c1\u2010MR based IDD phases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To build a deep learning model to achieve the classification of T1\u03c1\u2010MR based IDD phases from routine T1\u2010MR images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>Sixty (M\/F: 35\/25) patients with low back pain or lower limb radiculopathy are randomly divided into training (N\u2009=\u200950) and test (N\u2009=\u200910) sets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequences<\/jats:title><jats:p>1.<jats:styled-content style=\"fixed-case\">5\u2009T MR<\/jats:styled-content> scanner; <jats:styled-content style=\"fixed-case\">T1<\/jats:styled-content>\u2010, <jats:styled-content style=\"fixed-case\">T2<\/jats:styled-content>\u2010, and T1\u03c1\u2010<jats:styled-content style=\"fixed-case\">MR<\/jats:styled-content> sequence (spin echo).<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>The T1\u03c1 values of the nucleus pulposus in intervertebral discs (IVDs) were measured. IVDs were divided into three phases based on the mean T1\u03c1 value: pre\u2010degeneration phase (mean T1\u03c1 value &gt;110\u2009msec), rapid degeneration phase (mean T1\u03c1 value: 80\u2013110\u2009msec), and late degeneration phase (mean T1\u03c1 value &lt;80\u2009msec). After measurement, the T1\u03c1 values, phases, and levels of IVDs were input into the model as labels.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Intraclass correlation coefficient, area under the receiver operating characteristic curve (AUC), F1\u2010score, accuracy, precision, and recall (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05 was considered significant).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the test dataset, the model achieved a mean average precision of 0.996 for detecting IVD levels. The diagnostic accuracy of the T1\u03c1\u2010MR based IDD phases was 0.840 and the AUC was 0.871, the average AUC of 5\u2010folds cross validation was 0.843.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>The proposed deep learning model achieved the classification of T1\u03c1\u2010MR based IDD phases from routine T1\u2010MR images, which may provide a method to facilitate the application of T1\u03c1\u2010MR in IDD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/jats:title><jats:p>4<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 2<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29499","type":"journal-article","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T06:56:00Z","timestamp":1721112960000},"page":"1492-1500","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Learning Assisted Classification of 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