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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Spinopelvic sagittal balance ensures efficient posture and minimizes energy expenditure by aligning the spine, pelvis, and lower extremities. Deviations can cause clinical issues like back pain and functional limitations. Key radiographic parameters, including pelvic tilt (PT), pelvic incidence (PI), sacral slope (SS), and lumbar lordosis (LL), are essential for evaluating spinal pathologies and planning surgeries. Accurate PI measurement is challenging in certain conditions, necessitating alternative parameters. This study aimed to introduce a new, easily measurable parameter and examine its reliability and correlation with established sagittal parameters. ⋯ The S1PA is a dependable parameter for evaluating the morphology and orientation of the pelvis. PI could be precisely predicted using the S1PA. These insights are valuable for clinicians, enhancing their ability to assess spinopelvic sagittal alignment accurately.
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The surprising increase observed in recent years in the use of minimally invasive sacroiliac joint arthrodesis techniques as a treatment for low back pain justifies an objective review of this results. ⋯ Although the clinical results regarding the effectiveness of SIJ fusion are forceful for their effectiveness, we recommend considering some aspects for their analysis and especially long-term studies.
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Lumbar spinal stenosis (LSS) is a frequently occurring condition defined by narrowing of the spinal or nerve root canal due to degenerative changes. Physicians use MRI scans to determine the severity of stenosis, occasionally complementing it with X-ray or CT scans during the diagnostic work-up. However, manual grading of stenosis is time-consuming and induces inter-reader variability as a standardized grading system is lacking. Machine Learning (ML) has the potential to aid physicians in this process by automating segmentation and classification of LSS. However, it is unclear what models currently exist to perform these tasks. ⋯ DL models achieve excellent performance for segmentation and classification tasks for LSS, outperforming conventional ML algorithms. However, comparisons between studies are challenging due to the variety in outcome measures and test datasets. Future studies should focus on the segmentation task using DL models and utilize a standardized set of outcome measures and publicly available test dataset to express model performance. In addition, these models need to be externally validated to assess generalizability.