European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
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By predicting short-term postoperative outcomes before surgery, patients undergoing cervical laminoplasty (CLP) surgery could benefit from more accurate patient care strategies that could reduce the likelihood of adverse outcomes. With this study, we developed a series of machine learning (ML) models for predicting short-term postoperative outcomes and integrated them into an open-source online application. ⋯ The potential of ML approaches to predict postoperative outcomes following spine surgery is significant. As the volume of data in spine surgery continues to increase, the development of predictive models as clinically relevant decision-making tools could significantly improve risk assessment and prognosis. Here, we present an accessible predictive model for predicting short-term postoperative outcomes following CLP intended to achieve the stated objectives.
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Review Meta Analysis
Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis.
Low back pain is the leading contributor to disability burden globally. It is commonly due to degeneration of the lumbar intervertebral discs (LDD). Magnetic resonance imaging (MRI) is the current best tool to visualize and diagnose LDD, but places high time demands on clinical radiologists. Automated reading of spine MRIs could improve speed, accuracy, reliability and cost effectiveness in radiology departments. The aim of this review and meta-analysis was to determine if current machine learning algorithms perform well identifying disc degeneration, herniation, bulge and Modic change compared to radiologists. ⋯ This review highlights several shortcomings of current approaches, including few validation attempts or use of large sample sizes. To the best of the authors' knowledge, this is the first systematic review to explore this topic. We suggest the utilization of deep learning coupled with semi- or unsupervised learning approaches. Use of all information contained in MRI data will improve accuracy. Clear and complete reporting of study design, statistics and results will improve the reliability and quality of published literature.
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Review Meta Analysis
Incidence of genitourinary anomalies in congenital scoliosis: systematic review and meta-analysis.
The main objective of this study was to assess the overall incidence of genitourinary anomalies in patients with congenital scoliosis by providing the highest level of evidence. The secondary objective was to look for associations and trends influencing the incidence. ⋯ The incidence of genitourinary anomalies associated with congenital scoliosis was 22.91%, and 13.92% were surgically treated. Unilateral kidney was the most common genitourinary abnormality. There were no differences between genders and deformity types. It is important to consider the association between genitourinary anomalies and intraspinal or musculoskeletal anomalies.
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This paper presents a comparison of quantitative computed tomography (QCT) and dual-energy X-ray absorptiometry (DXA) in osteoporosis with vertebral fracture and osteoporosis without fracture. It has been proved that the volumetric bone mineral density (vBMD) measured by QCT exhibits a stronger correlation with fracture risk than areal bone mineral density (aBMD) measured by DXA. ⋯ Both vBMD detected by QCT and aBMD detected by DXA could discriminate fracture status in the spine, and vBMD performed a stronger correlation with fracture risk.
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Multicenter Study
Deep learning-based prediction model for postoperative complications of cervical posterior longitudinal ligament ossification.
Postoperative complication prediction helps surgeons to inform and manage patient expectations. Deep learning, a model that finds patterns in large samples of data, outperform traditional statistical methods in making predictions. This study aimed to create a deep learning-based model (DLM) to predict postoperative complications in patients with cervical ossification of the posterior longitudinal ligament (OPLL). ⋯ A new algorithm using deep learning was able to predict complications after cervical OPLL surgery. This model was well calibrated, with prediction accuracy comparable to that of regression models. The accuracy remained high even for predicting only neurological complications, for which the case number is limited compared to conventional statistical methods.