Study Design. Retrospective analysis of magnetic resonance imaging (MRI). Objective. The aim of this study was to evaluate the performance of our convolutional neural network (CNN) in differentiating between spinal schwannoma and meningioma on MRI. We compared the performance of the CNN and that of two expert radiologists. Summary of Background Data. Preoperative discrimination between spinal schwannomas and meningiomas is crucial because different surgical procedures are required for their treatment. A deep-learning approach based on CNNs is gaining interest in the medical imaging field. Methods. We retrospectively reviewed data from patients with spinal schwannoma and meningioma who had undergone MRI and tumor resection. There were 50 patients with schwannoma and 34 patients with meningioma. Sagittal T2-weighted magnetic resonance imaging (T2WI) and sagittal contrast-enhanced T1-weighted magnetic resonance imaging (T1WI) were used for the CNN training and validation. The deep learning framework Tensorflow was used to construct the CNN architecture. To evaluate the performance of the CNN, we plotted the receiver-operating characteristic (ROC) curve and calculated the area under the curve (AUC). We calculated and compared the sensitivity, specificity, and accuracy of the diagnosis by the CNN and two board-certified radiologists. Results. . The AUC of ROC curves of the CNN based on T2WI and contrast-enhanced T1WI were 0.876 and 0.870, respectively. The sensitivity of the CNN based on T2WI was 78%; 100% for radiologist 1; and 95% for radiologist 2. The specificity was 82%, 26%, and 42%, respectively. The accuracy was 80%, 69%, and 73%, respectively. By contrast, the sensitivity of the CNN based on contrast-enhanced T1WI was 85%; 100% for radiologist 1; and 96% for radiologist 2. The specificity was 75%, 56, and 58%, respectively. The accuracy was 81%, 82%, and 81%, respectively. Conclusion. We have successfully differentiated spinal schwannomas and meningiomas using the CNN with high diagnostic accuracy comparable to that of experienced radiologists. Level of Evidence: 4
The emergency department is an environment with a potential risk for diagnostic errors during trauma care, particularly for fractures. Convolutional neural network (CNN) deep learning methods are now widely used in medicine because they improve diagnostic accuracy, decrease misinterpretation, and improve efficiency. In this study, we investigated whether automatic localization and classification using CNN could be applied to pelvic, rib, and spine fractures. We also examined whether this fracture detection algorithm could help physicians in fracture diagnosis. A total of 7664 whole-body CT axial slices (chest, abdomen, pelvis) from 200 patients were used. Sensitivity, precision, and F1-score were calculated to evaluate the performance of the CNN model. For the grouped mean values for pelvic, spine, or rib fractures, the sensitivity was 0.786, precision was 0.648, and F1-score was 0.711. Moreover, with CNN model assistance, surgeons showed improved sensitivity for detecting fractures and the time of reading and interpreting CT scans was reduced, especially for less experienced orthopedic surgeons. Application of the CNN model may lead to reductions in missed fractures from whole-body CT images and to faster workflows and improved patient care through efficient diagnosis in polytrauma patients.
We compared the diagnostic ability of a convolutional neural network (CNN) to that of spine surgeons for differentiating between osteoporotic vertebral fractures and malignant vertebral compression fractures based on MRI. The performance of the CNN was equal or superior to that of spine surgeons.
Study Design: A retrospective case-control study. Objective: The objective of this study was to assess mid-term surgical outcomes after posterior decompression with instrumented fusion (PDF) in patients with K-line (−) type cervical ossification of the posterior longitudinal ligament (OPLL). Summary of Background Data: The poor surgical outcome for K-line (−) type cervical OPLL can result from posterior decompression alone. Materials and Methods: We reviewed cases of K-line (−) type cervical OPLL in 24 patients who underwent PDF in our institute from 2002 to 2014. As a control, we used K-line (−) type cervical OPLL in 9 patients who underwent laminoplasty before 2002 (LMP group). The neurological status and radiographic findings were evaluated retrospectively. Results: The preoperative Japanese Orthopedic Association score was 7.9±2.4 points in the PDF group and 7.4±2.3 points in the LMP group (P=0.584). The postoperative Japanese Orthopedic Association score was 11.7±2.6 points in the PDF group and 9.2±2.0 points in the LMP group at a 5-year follow-up (P=0.008). The recovery rate on average was 39.0% in the PDF group and 14.9% in the LMP group at a 5-year follow-up (P=0.037). The range of motion postoperatively at the maximal spinal cord compression level decreased significantly in the PDF group. The C2–C7 angle was 2.7 degrees of kyphosis in the PDF group, whereas 5.5 degrees of kyphosis was found in the LMP group at a 5-year follow-up (P=0.303). The center of gravity of the head-C7 sagittal vertical axis was 40 mm in the PDF group and 43 mm in the LMP group (P=0.936). Conclusions: The relatively good surgical outcome could be obtained by PDF for patients with K-line (−)-type cervical OPLL. The addition of posterior instrumented fusion eliminated the dynamic factor at the level of maximal spinal cord compression. Level of Evidence: Level IV.
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