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  • Author or Editor: Dae-Woong Hamx
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The importance of thoracolumbar junctional orientation, change in thoracolumbar angle, and overcorrection of lumbar lordosis in development of proximal junctional kyphosis in adult spinal deformity surgery

Dae-Woong Ham, Ho-Joong Kim, Sang-Min Park, Se Jin Park, Jiwon Park, and Jin S. Yeom

OBJECTIVE

Changes in the thoracolumbar angle (TLA) would play a pivotal role in the reciprocal changes following spine realignment surgery, thereby leading to the development of proximal junctional kyphosis (PJK). This study aimed to investigate the association between TLA and the development of PJK following adult spinal deformity surgery.

开云体育世界杯赔率

A total of 107 patients were divided into PJK+ and PJK− groups according to the development of PJK within 12 months after surgery. The TLA and spinopelvic radiological parameters were compared between the PJK+ and PJK− groups. A multivariate logistic regression model was used to identify the risk factors for PJK. The receiver operating characteristic curves of the regression models were used to investigate the cutoff values of significant parameters needed so that PJK would not occur.

RESULTS

The change in TLA (ΔTLA) in the PJK+ group was significantly larger than in the PJK− group (6.7° ± 7.9° and 2.2° ± 8.1°, respectively; p = 0.006). Multivariate logistic regression analysis demonstrated that age, postoperative pelvic incidence–lumbar lordosis, and ΔTLA were significant risk factors for PJK. The risk of developing PJK was higher when the postoperative pelvic incidence–lumbar lordosis was < 5.2 and the ΔTLA was > 3.58°.

CONCLUSIONS

The present study highlights the thinking that extensive correction of TLA and lumbar lordosis should be avoided in patients with adult spinal deformity. Overcorrection of TLA of > 3.58° could result in higher odds of PJK.

InJournal of Neurosurgery: Spine Volume 37: Issue 6 (Dec 2022)

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

Dae-Woong Ham, Yang-seon Choi, Yisack Yoo, Sang-Min Park, and Kwang-Sup Song

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.

开云体育世界杯赔率

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.

In开云手机版app下载安装最新版脊柱神经外科杂志:39开云体育app官方网站下载入口卷:我ssue 3 (Sep 2023)

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