ArticleFrontiers in bioengineering and biotechnology2020
Prediction of Proximal Junctional Kyphosis After Posterior Scoliosis Surgery With Machine Learning in the Lenke 5 Adolescent Idiopathic Scoliosis Patient.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Incidence and risk factors of proximal junctional kyphosis in adolescent idiopathic scoliosis after correction surgery: a meta-analysis and systematic review.Journal of orthopaedic surgery and research · 2024Pooled it
- Algorithmic fairness reporting in artificial intelligence for spinal deformity: a systematic review.Spine deformity · 2026Review
- Utilization of posterior column osteotomies with posterior spinal fusion for correction of Lenke type 5 adolescent idiopathic scoliosis: a systematic review.Spine deformity · 2026Article
- Global research trends and hotspots in robot-assisted spinal surgery for spinal deformity: a bibliometric analysis.Journal of robotic surgery · 2026Article
- Deep learning based vertebra localization and Cobb angle estimation using x-ray images.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 · 2026Article
- Subject-Specific Musculoskeletal Modeling: The Future of Predicting and Preventing Proximal Junctional Failure in Adult Spinal Deformity.JOR spine · 2025Article
- Implementation of artificial intelligence (AI) in ASD treatment.North American Spine Society journal · 2025Article
- AI and machine learning in paediatric spine deformity surgery.Bone & joint open · 2025Article
- The Application of Artificial Intelligence in Spine Surgery: A Scoping Review.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2025Article
- 2D and 3D Classification Systems for Adolescent Idiopathic Scoliosis: Clinical Implications and Technological Advances.Orthopaedic surgery · 2025Review
- Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence.Communications medicine · 2025Article
- A 20-year research trend analysis of the artificial intelligence on scoliosis using bibliometric methods.Frontiers in pediatrics · 2025Review
- Sagittal Balance Parameters and Proximal Junctional Kyphosis in Adolescent Idiopathic Scoliosis.Journal of clinical medicine · 2024Review
- Systematic review and meta-analysis for the proximal junctional kyphosis in adolescent idiopathic scoliosis.Frontiers in pediatrics · 2024Review
- Risk factors for proximal junctional kyphosis in a multicenter study of Lenke type 5 and 6 adolescent idiopathic scoliosis patients.Spine deformity · 2024Article
- Artificial Intelligence in Scoliosis: Current Applications and Future Directions.Journal of clinical medicine · 2023Review
- Advances and Evolving Challenges in Spinal Deformity Surgery.Journal of clinical medicine · 2023Review
- Article
- Current Applications of Machine Learning in Spine: From Clinical View.Global spine journal · 2022Review
- Correlations of proximal junctional kyphosis with radiographic measurements, spinopelvic parameters, and health-related quality of life in Lenke type V adolescent idiopathic scoliosis.Joint diseases and related surgery · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo build a model for proximal junctional kyphosis (PJK) prognostication in Lenke 5 adolescent idiopathic scoliosis (AIS) patients undergoing long posterior instrumentation and fusion surgery by machine learning and analyze the risk factors for PJK. MATERIALS AND
methodsIn total, 44 AIS patients (female/male: 34/10; PJK/non-PJK: 34/10) who met the inclusion criteria between January 2013 and December 2018 were retrospectively recruited from West China Hospital. Thirty-seven clinical and radiological features were acquired by two independent investigators. Univariate analyses between PJK and non-PJK groups were carried out. Twelve models were built by using four types of machine learning algorithms in conjunction with two oversampling methods [the synthetic minority technique (SMOTE) and random oversampling]. Area under the receiver operating characteristic curve (AUC) was used for model discrimination, and the clinical utility was evaluated by using F1 score and accuracy. The risk factors were simultaneously analyzed by a Cox regression and machine learning.
resultsStatistical differences between PJK and non-PJK groups were as follows: gender (
conclusionThe random forest using SMOTE model has the great value for predicting the individual risk of developing PJK after long instrumentation and fusion surgery in Lenke 5 AIS patients. Moreover, the combination of the outcomes of a Cox model and the feature ranking extracted by machine learning is more valuable than any one alone, especially in the interpretation of risk factors.
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