To infer the probability of cervical ossification of the posterior longitudinal ligament and explore its impact on cervical surgery

Zhu, Jichong; Lu, Qiongshi; Zhan, Xinli; Huang, Shengsheng; Zhou, Chenxing; Wu, Shaofeng; Chen, Tianyou; Yao, Yuanlin; Liao, Shijie; Yu, Chaojie; Fan, Binguang; Yang, Zhenwei; Gu, Wenfei; Wang, Yihan; Wei, Wendi; Liu, Chong

doi:10.1038/s41598-023-36992-7

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…Recently, Zhu et al [ 38 ] collected data from 775 patients who underwent cervical spine surgery and screened 84 patient variables to identify differences between patients with positive and negative cervical ossification of the posterior longitudinal ligament (OPLL). They proposed an ML-driven nomogram to predict patients with cervical OPLL and could identify the risk factors and other associated characteristics of cervical OPLL.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence: a new cutting-edge tool in spine surgery

Kalanjiyam,

Chandramohan,

Raman

et al. 2024

Asian Spine J

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The purpose of this narrative review was to comprehensively elaborate the various components of artificial intelligence (AI), their applications in spine surgery, practical concerns, and future directions. Over the years, spine surgery has been continuously transformed in various aspects, including diagnostic strategies, surgical approaches, procedures, and instrumentation, to provide better-quality patient care. Surgeons have also augmented their surgical expertise with rapidly growing technological advancements. AI is an advancing field that has the potential to revolutionize many aspects of spine surgery. We performed a comprehensive narrative review of the various aspects of AI and machine learning in spine surgery. To elaborate on the current role of AI in spine surgery, a review of the literature was performed using PubMed and Google Scholar databases for articles published in English in the last 20 years. The initial search using the keywords “artificial intelligence” AND “spine,” “machine learning” AND “spine,” and “deep learning” AND “spine” extracted a total of 78, 60, and 37 articles and 11,500, 4,610, and 2,270 articles on PubMed and Google Scholar. After the initial screening and exclusion of unrelated articles, duplicates, and non-English articles, 405 articles were identified. After the second stage of screening, 93 articles were included in the review. Studies have shown that AI can be used to analyze patient data and provide personalized treatment recommendations in spine care. It also provides valuable insights for planning surgeries and assisting with precise surgical maneuvers and decisionmaking during the procedures. As more data become available and with further advancements, AI is likely to improve patient outcomes.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence: a new cutting-edge tool in spine surgery

Kalanjiyam,

Chandramohan,

Raman

et al. 2024

Asian Spine J

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“…A LASSO regression model was developed to identify risk factors and determine optimal predictors for STB patients from a pool of variables that could potentially be collinear. The LASSO regression was conducted using the “glmnet” package in the R software [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms

Huang,

Zhuo,

Liu

et al. 2024

Biomol Biomed

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This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The model’s performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patients’ average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses.

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Prevertebral Anchored Kevlar Band to Recover Intact Spine Movement Ranges in Lumbar Disc Arthroplasty

Vanaclocha,

Atienza

et al. 2024

Biomedical Materials & Devices

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Anterior longitudinal ligament and annulus fibrosus removal in total disc replacement induces excessive spinal mobility with zygapophyseal joint overload and osteoarthritic changes causing chronic back pain. To control disc arthroplasty-induced hypermobility with a Kevlar® band. A 3 × 3 cm Kevlar® band implanted in twelve human lumbar cadaveric spines substituted the anterior longitudinal ligament and annulus in disc arthroplasty. Biomechanical studies compared the intact spine, after discectomy, post-disc arthroplasty, and with the Kevlar® band inserted. The Kevlar® band recovered the extension and axial rotation movement ranges without regaining the intact status. Improvement was moderate in lateral bending. The Instantaneous Axis of Rotation improved the discectomy and total disc prosthesis conditions without recovering the initial state. The disc above the operated one also showed increased mobility, particularly in extension and axial rotation, improved by Kevlar® band insertion without recovering the intact spine values. The Kevlar® band improves excess mobility ranges induced by anterior longitudinal ligament and anterior annulus removal in disc arthroplasty in the operated and supra-adjacent discs without recovering the baseline status.

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To infer the probability of cervical ossification of the posterior longitudinal ligament and explore its impact on cervical surgery

Cited by 3 publications

References 40 publications

Artificial intelligence: a new cutting-edge tool in spine surgery

Artificial intelligence: a new cutting-edge tool in spine surgery

Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms

Prevertebral Anchored Kevlar Band to Recover Intact Spine Movement Ranges in Lumbar Disc Arthroplasty

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