OBJECTIVE: The study aimed to develop a single-stage deep learning (DL) screening system for automated binary and multiclass grading of lumbar central stenosis (LCS), lateral recess stenosis (LRS), and lumbar foraminal stenosis (LFS).
OBJECTIVE: Postoperative recovery following lumbar fusion surgery in patients aged 75 years and older often requires a prolonged length of stay (PLOS) in the hospital. Accurately predicting the risk of PLOS and assessing its risk factors for preopera...
OBJECTIVE: Robotics and artificial intelligence (AI) are being increasingly integrated in spine surgery. One emerging application of AI is in hand motion detection to assess surgical skill. However, no standardized framework currently exists for eval...
OBJECTIVE: Multiple studies in the past have developed equations to determine the ideal lumbar lordosis (ILL) in the sagittal plane. These equations differ but all look to accomplish the same goal of providing the surgeon with specific alignment targ...
OBJECTIVE: Software engineering innovations have led to the development of virtual surgical planning software (VSPS) for deformity correction. VSPS uses calibrated radiographs and machine learning predictive models to simulate postoperative spinopelv...
BACKGROUND: Segmentation is a critical process in medical image interpretation. It is also essential for preparing training datasets for machine learning (ML)-based solutions. Despite technological advancements, achieving fully automatic segmentation...
The existing assessment of adjacent segment degeneration (ASD) risk after lumbar fusion surgery focuses on a single type of clinical information or imaging manifestations. In the early stages, it is difficult to show obvious degeneration characterist...
BACKGROUND: Only in recent years it has been demonstrated that the thoracolumbar fascia is involved in low back pain (LBP), thus highlighting its implications for treatments. Furthermore, an easily accessible and non-invasive way to investigate the f...
UNLABELLED: Osteoporosis screening should be systematic in the group of over 50-year-old females with a radius fracture. We tested a phantom combined with machine learning model and studied osteoporosis-related variables. This machine learning model ...
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