ArticleGlobal spine journal2022
Can Machine Learning Accurately Predict Postoperative Compensation for the Uninstrumented Thoracic Spine and Pelvis After Fusion From the Lower Thoracic Spine to the Sacrum?
Article in Global spine journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Improved immediate postoperative T4-L1 hip axis with use of pre-contoured patient-specific rods versus surgeon-contoured rods in adolescent idiopathic scoliosis.Journal of spine surgery (Hong Kong) · 2026Article
- Subject-Specific Musculoskeletal Modeling: The Future of Predicting and Preventing Proximal Junctional Failure in Adult Spinal Deformity.JOR spine · 2025Article
- Enabling technology in adult spinal deformity.Spine deformity · 2025Review
- How Does Spino-Pelvic Fixation Affect Post-Operative Compensatory Mechanisms in Adult Spinal Deformity?Global spine journal · 2025Article
- Personalizing adult spinal deformity surgery through multimodal artificial intelligence.Acta orthopaedica et traumatologica turcica · 2024Review
- Using Artificial Intelligence to Predict the Development of Kyphosis Disease: A Systematic Review.Cureus · 2023Review
- Current Applications of Machine Learning in Spine: From Clinical View.Global spine journal · 2022Review
- Emerging Technologies in the Treatment of Adult Spinal Deformity.Neurospine · 2021Article
- Assessment of Clinical Decision Support System Efficiency in Spinal Neurosurgery for Personalized Minimally Invasive Technologies Used on Lumbar Spine.Sovremennye tekhnologii v meditsine · 2021Article
- Evaluation of long-term clinical outcomes and the incidence of adjacent proximal segment degenerative disease with algorithmic transforaminal interbody fusion: A multicenter prospective study.Journal of craniovertebral junction & spineArticle
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designConsecutively collected cases.
objectiveTo determine if a machine-learning (ML) program can accurately predict the postoperative thoracic kyphosis through the uninstrumented thoracic spine and pelvic compensation in patients who undergo fusion from the lower thoracic spine (T10 or T11) to the sacrum.
methodsFrom 2015 to 2019, a consecutive series of adult (≥18 years old) patients with adult spinal deformity underwent corrective spinal fusion from the lower thoracic spine (T10 or T11) to the sacrum. Deidentified data was processed by a ML system-based platform to predict the postoperative thoracic kyphosis (TK) and pelvic tilt (PT) for each patient. To validate the ML model, the postoperative TK (T4-T12, instrumented thoracic, and uninstrumented thoracic) and the pelvic tilt were compared against the predicted values.
resultsA total of 20 adult patients with a minimum 6-month follow-up (mean: 22.4 ± 11.3 months) were included in this study. No significant differences were observed for TK (predicted 37.6° vs postoperative 38.3°,
conclusionML algorithms can accurately predict the spinopelvic compensation after spinal fusion from the lower thoracic spine to the sacrum. These findings suggest that surgeons may be able to leverage this technology to reduce the risk of proximal junctional kyphosis in this population.
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