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Physician experience significantly influences the detection of spinal metastases and the evaluation of associated features. This study aims to develop a deep\u2010learning system (DLS) for automatic detection of spinal metastasis and feature evaluation using computed tomography and to determine the impact of the DLS on physician performance in the detection and assessment of spinal metastasis. DLS assistance in a multireader, multicase test study results in higher sensitivity and specificity in spinal metastasis detection and feature evaluation (all <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). Additionally, resident physicians show a more significant improvement in sensitivity and specificity compared with attending or chief physicians in spinal metastasis detection and most feature evaluation (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.01). In a cohort test study, resident oncologists assisted by the DLS achieve significantly higher sensitivity and specificity compared with those without assistance (all <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.01), except for the sensitivity of vertebral body collapse evaluation (<jats:italic>p\u2009<\/jats:italic>&gt;\u20090.01). 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