ReviewAnnals of translational medicine2021
A narrative review of machine learning as promising revolution in clinical practice of scoliosis.
Review in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 47 citations in OpenAlex.
- Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening.Journal of healthcare engineering · 2022Trial
- Using artificial intelligence to predict health-related quality of life for adolescent idiopathic scoliosis.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
- Prediction of Clinically Significant Improvement after Lumbar Fusion Surgery Based on Machine Learning.Yonsei medical journal · 2026Article
- The role of artificial intelligence in advancing scoliosis care: a rapid review of current evidence and future opportunities.Frontiers in medicine · 2026Review
- Artificial intelligence in severe rigid Scoliosis: Opportunities, challenges, and the road ahead.Journal of clinical orthopaedics and trauma · 2025Review
- Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence.Spine deformity · 2024Review
- Radiographic Analysis of Scoliosis Using Convolutional Neural Network in Clinical Practice.Journal of the Korean Society of Radiology · 2024Article
- The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review.Patient safety in surgery · 2024Review
- Artificial Intelligence in Scoliosis: Current Applications and Future Directions.Journal of clinical medicine · 2023Review
- Application of machine learning standardized integral area algorithm in measuring the scoliosis.Scientific reports · 2023Article
- Automatic measurement of the Cobb angle for adolescent idiopathic scoliosis using convolutional neural network.Scientific reports · 2023Article
- Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning.Journal of clinical medicine · 2023Review
- Development of artificial intelligence for automated measurement of cervical lordosis on lateral radiographs.Scientific reports · 2022Article
- Automated Cobb Angle Measurement for Adolescent Idiopathic Scoliosis Using Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Electromyographic Analysis of Paraspinal Muscles of Scoliosis Patients Using Machine Learning Approaches.International journal of environmental research and public health · 2022Article
- Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era.Frontiers in bioengineering and biotechnology · 2022Article
- A Survey of Methods and Technologies Used for Diagnosis of Scoliosis.Sensors (Basel, Switzerland) · 2021Review
- Application of deep learning upon spinal radiographs to predict progression in adolescent idiopathic scoliosis at first clinic visit.EClinicalMedicine · 2021Article
- Artificial Intelligence to Diagnose Tibial Plateau Fractures: An Intelligent Assistant for Orthopedic Physicians.Current medical science · 2021Article
- Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis.PloS one · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML), as an advanced domain of artificial intelligence (AI), is progressively changing our view of the world. By implementing its algorithms, our ability to detect previously undiscoverable patterns in data has the potential to revolutionize predictive analytics. Scoliosis, as a relatively specialized branch in the spine field, mainly covers the pediatric, adult and the elderly populations, and its diagnosis and treatment remain difficult. With recent efforts and interdisciplinary cooperation, ML has been widely applied to investigate issues related to scoliosis, and surprisingly augment a surgeon's ability in clinical practice related to scoliosis. Meanwhile, ML models penetrate in every stage of the clinical practice procedure of scoliosis. In this review, we first present a brief description of the application of ML in the clinical practice procedures regarding scoliosis, including screening, diagnosis and classification, surgical decision making, intraoperative manipulation, complication prediction, prognosis prediction and rehabilitation. Meanwhile, the ML models and specific applications adopted are presented. Additionally, current limitations and future directions are briefly discussed regarding its use in the field of scoliosis. We believe that the implementation of ML is a promising revolution to assist surgeons in all aspects of clinical practice related to scoliosis in the near future.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.