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A customized deep learning tool is accurate in the detection and quantification of hemorrhage on NCCT. Demonstrated high performance on prospective NCCTs ordered from the emergency department suggests the clinical viability of the proposed deep learning tool.

Background Posterior hip instability is an increasingly recognized injury in athletes; however, the function of patients after these injuries and an understanding of the pathoanatomy and underlying mechanism are currently unclear.

External rotation and abduction maneuvers of the morphologically normal human hip joint in moderate flexion or extension can generate substantial tensile strains in the anterior part of the acetabular labrum. This finding supports the hypothesis that injury to the anterior part of the labrum may occur from recurrent twisting or pivoting maneuvers of the hip joint in moderate flexion or extension without femoroacetabular impingement.