ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2021
Artificial intelligence clustering of adult spinal deformity sagittal plane morphology predicts surgical characteristics, alignment, and outcomes.
Article in 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, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 37 citations in OpenAlex.
- Diagnostic accuracy of artificial intelligence for spinopelvic parameters in standing total spine X-ray and limitations after fusion surgery.Skeletal radiology · 2026Article
- Advent of Artificial Intelligence in Spine Research: An Updated Perspective.Journal of clinical medicine · 2026Article
- Comparison of two clustering methods on surgical patients with adult spinal deformity: Importance of the variable choice on the obtained results and their interpretation.Brain & spine · 2026Article
- Radiographic phenotype-driven clustering in lumbar decompression: comparative study of outcome and reoperation risk.The spine journal : official journal of the North American Spine Society · 2026Article
- Artificial intelligence and machine learning in spine care: Advancing precision diagnosis, treatment, and rehabilitation.World journal of orthopedics · 2025Review
- Alterations in Magnitude and Shape of Thoracic Kyphosis Following Surgical Correction for Adult Spinal Deformity.Global spine journal · 2025Article
- Scoping Review of Machine Learning and Patient-Reported Outcomes in Spine Surgery.Bioengineering (Basel, Switzerland) · 2025Review
- Thoracolumbar fusions for adult lumbar deformity show superior QALY gain and lower costs compared with upper thoracic fusions.Spine deformity · 2024Article
- A machine learning based quantification system for automated diagnosis of lumbar spondylolisthesis on spinal X-rays.Heliyon · 2024Article
- Using Artificial Intelligence to Predict the Development of Kyphosis Disease: A Systematic Review.Cureus · 2023Review
- Emerging Technologies within Spine Surgery.Life (Basel, Switzerland) · 2023Review
- Radiomics in Spine Surgery.International journal of spine surgery · 2023Article
- Potential Applications of Artificial Intelligence and Machine Learning in Spine Surgery Across the Continuum of Care.International journal of spine surgery · 2023Article
- Real-World Implementation of Artificial Intelligence/Machine Learning for Managing Surgical Spine Patients at 2 Academic Health Care Systems.International journal of spine surgery · 2023Article
- An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery.Journal of clinical medicine · 2022Article
- AI Prediction of Neuropathic Pain after Lumbar Disc Herniation-Machine Learning Reveals Influencing Factors.Biomedicines · 2022Article
- An Evolution Gaining Momentum-The Growing Role of Artificial Intelligence in the Diagnosis and Treatment of Spinal Diseases.Diagnostics (Basel, Switzerland) · 2022Review
- The application of artificial intelligence in spine surgery.Frontiers in surgery · 2022Review
- The Influence of Martial Arts on Spine CT Image Morphological Structure Based on Optimized Ant Colony Algorithm.Computational intelligence and neuroscience · 2022Article
- Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain-A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
25 authors at 16 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAI algorithms have shown promise in medical image analysis. Previous studies of ASD clusters have analyzed alignment metrics-this study sought to complement these efforts by analyzing images of sagittal anatomical spinopelvic landmarks. We hypothesized that an AI algorithm would cluster preoperative lateral radiographs into groups with distinct morphology.
methodsThis was a retrospective review of a multicenter, prospectively collected database of adult spinal deformity. A total of 915 patients with adult spinal deformity and preoperative lateral radiographs were included. A 2 × 3, self-organizing map-a form of artificial neural network frequently employed in unsupervised classification tasks-was developed. The mean spine shape was plotted for each of the six clusters. Alignment, surgical characteristics, and outcomes were compared.
resultsQualitatively, clusters C and D exhibited only mild sagittal plane deformity. Clusters B, E, and F, however, exhibited marked positive sagittal balance and loss of lumbar lordosis. Cluster A had mixed characteristics, likely representing compensated deformity. Patients in clusters B, E, and F disproportionately underwent 3-CO. PJK and PJF were particularly prevalent among clusters A and E. Among clusters B and F, patients who experienced PJK had significantly greater positive sagittal balance than those who did not.
conclusionsThis study clustered preoperative lateral radiographs of ASD patients into groups with highly distinct overall spinal morphology and association with sagittal alignment parameters, baseline HRQOL, and surgical characteristics. The relationship between SVA and PJK differed by cluster. This study represents significant progress toward incorporation of computer vision into clinically relevant classification systems in adult spinal deformity. LEVEL OF EVIDENCE IV: Diagnostic: individual cross-sectional studies with the consistently applied reference standard and blinding.
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