The gap between surgeon goal and achieved sagittal alignment in adult cervical spine deformity surgery.

Objective: Malalignment following cervical spine deformity (CSD) surgery can negatively impact outcomes and increase complications. Despite the growing ability to plan alignment, it remains unclear whether preoperative goals are achieved with surgery. The objective of this study was to assess how good surgeons are at achieving their preoperative goal alignment following CSD surgery.

How Reliable is the Assessment of Fusion Status Following ACDF Using Dynamic Flexion-Extension Radiographs?

Study Design: Reliability study.

Objectives: The radiographic diagnosis of non-union is not standardized. Prior authors have suggested using a cutoff of <1 mm interspinous process motion (ISPM) on flexion-extension radiographs, but the ability of practicing surgeons to make these measurements reliably is not clear.

Artificial Intelligence Detection of Cervical Spine Fractures Using Convolutional Neural Network Models.

Objective: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making.

Methods: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs.

Artificial Intelligence-Assisted MRI Diagnosis in Lumbar Degenerative Disc Disease: A Systematic Review.

Study Design: Systematic review.

Objectives: Lumbar degenerative disc disease (DDD) poses a significant global health care challenge, with accurate diagnosis being difficult using conventional methods. Artificial intelligence (AI), particularly machine learning and deep learning, offers promising tools for improving diagnostic accuracy and workflow in lumbar DDD.