The efficacy of Denosumab in the treatment of femoral head osteonecrosis: a retrospective comparative study.

The present study aimed to compare clinical and radiological differences of ONFH patients who were treated with denosumab, and a control group. A total of 178 patients (272 hips) with symptomatic, nontraumatic ONFH were divided into a denosumab group (98 patients, 146 hips) and a control group (80 patients, 126 hips). Patients in the denosumab group received a 60 mg subcutaneous dose of denosumab every 6 months. For the clinical assessments, Harris hip scores (HHS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were evaluated. Plain radiographs and MRI were performed before and a minimum of 1 year after administration of denosumab, which were evaluated for radiological results including femoral head collapse (≥ 2 mm) and volume change of necrotic lesion. Femoral head collapse occurred in 36 hips (24.7%) in the denosumab group, and 48 hips (38.1%) in the control group, which was statistically significant (P = 0.012). Twenty-three hips (15.8%) in the denosumab group and 29 hips (23%) in the control group required THA, which showed no significant difference (P = 0.086). At the final follow-up, 71.9% of hips in the denosumab group had a good or excellent HHS compared with 48.9% in the control group, showing a significant difference (P = 0.012). The denosumab group showed a significantly higher rate of necrotic lesion volume reductions compared with the control group (P < 0.001). Denosumab can significantly reduce the volume of necrotic lesions and prevent femoral head collapse in patients with ARCO stage I or II ONFH.

Measurement of interspinous motion in dynamic cervical radiographs using a deep learning-based segmentation model.

OBJECTIVE: Interspinous motion (ISM) is a representative method for evaluating the functional fusion status following anterior cervical discectomy and fusion (ACDF) surgery, but the associated measuring difficulty and potential errors in the clinical setting remain concerns. The aim of this study was to investigate the feasibility of a deep learning-based segmentation model for measuring ISM in patients who underwent ACDF surgery. METHODS: This study is a retrospective analysis of flexion-extension dynamic cervical radiographs from a single institution and a validation of a convolutional neural network (CNN)-based artificial intelligence (AI) algorithm for measuring ISM. Data from 150 lateral cervical radiographs from the normal adult population were used to train the AI algorithm. A total of 106 pairs of dynamic flexion-extension radiographs from patients who underwent ACDF at a single institution were analyzed and validated for measuring ISM. To evaluate the agreement power between human experts and the AI algorithm, the authors assessed the interrater reliability using the intraclass correlation coefficient and root mean square error (RMSE) and performed a Bland-Altman plot analysis. They processed 106 pairs of radiographs from ACDF patients into the AI algorithm for autosegmenting the spinous process created using 150 normal population radiographs. The algorithm automatically segmented the spinous process and converted it to a binary large object (BLOB) image. The rightmost coordinate value of each spinous process from the BLOB image was extracted, and the pixel distance between the upper and lower spinous process coordinate value was calculated. The AI-measured ISM was calculated by multiplying the pixel distance by the pixel spacing value included in the DICOM tag of each radiograph. RESULTS: The AI algorithm showed a favorable prediction power for detecting spinous processes with an accuracy of 99.2% in the test set radiographs. The interrater reliability between the human and AI algorithm of ISM was 0.88 (95% CI 0.83-0.91), and its RMSE was 0.68. In the Bland-Altman plot analysis, the 95% limit of interrater differences ranged from 0.11 to 1.36 mm, and a few observations were outside the 95% limit. The mean difference between observers was 0.02 ± 0.68 mm. CONCLUSIONS: This novel CNN-based autosegmentation algorithm for measuring ISM in dynamic cervical radiographs showed strong agreement power to expert human raters and could help clinicians to evaluate segmental motion following ACDF surgery in clinical settings.