ArticleEClinicalMedicine2022
An artificial intelligence powered platform for auto-analyses of spine alignment irrespective of image quality with prospective validation.
Article in EClinicalMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Selecting the Substantially Touched Vertebra as the Lowest Instrumented Vertebra in Spinal Surgeries for B3GALT6 -Related Disorders: Clinical Experience and Literature Review.Orthopaedic surgery · 2025Pooled it
- Deep learning in Cobb angle automated measurement on X-rays: a systematic review and meta-analysis.Spine deformity · 2025Pooled it
- State-of-the-art review in AI and digital technologies for spine deformity.Spine deformity · 2026Review
- Automated radiographic shoulder balance assessment in scoliosisGlobal health & medicine · 2026Article
- Scalable and Robust Artificial Intelligence for Spine Alignment Assessment: Multicenter Study Enabled by Real-Time Data Transformation.Journal of medical Internet research · 2026Article
- Performance of a deep learning-based algorithm for automated measurements of Cobb angles on preoperative spine radiographs in adolescent idiopathic scoliosis.Pediatric radiology · 2026Article
- The Application of Artificial Intelligence in Spine Surgery: A Scoping Review.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2025Article
- SiameseNet based on multiple instance learning for accurate identification of the histological grade of ICC tumors.Frontiers in oncology · 2025Article
- Superior performance of a center-point AI model over VFLDNet in automated cobb angle estimation for scoliosis assessment.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 · 2024Article
- Fully Automated Measurement of Cobb Angles in Coronal Plane Spine Radiographs.Journal of clinical medicine · 2024Article
- Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.Neurospine · 2024Article
- Achieving enhanced diagnostic precision in endometrial lesion analysis through a data enhancement framework.Frontiers in oncology · 2024Article
- Artificial Intelligence in Scoliosis Classification: An Investigation of Language-Based Models.Journal of personalized medicine · 2023Article
- SpineHRformer: A Transformer-Based Deep Learning Model for Automatic Spine Deformity Assessment with Prospective Validation.Bioengineering (Basel, Switzerland) · 2023Article
- Deep Learning Model to Classify and Monitor Idiopathic Scoliosis in Adolescents Using a Single Smartphone Photograph.JAMA network open · 2023Article
- Radiograph-comparable image synthesis for spine alignment analysis using deep learning with prospective clinical validation.EClinicalMedicine · 2023Article
- Evaluating Scoliosis Severity Based on Posturographic X-ray Images Using a Contrastive Language-Image Pretraining Model.Diagnostics (Basel, Switzerland) · 2023Article
- An Evolution Gaining Momentum-The Growing Role of Artificial Intelligence in the Diagnosis and Treatment of Spinal Diseases.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAssessment of spine alignment is crucial in the management of scoliosis, but current auto-analysis of spine alignment suffers from low accuracy. We aim to develop and validate a hybrid model named SpineHRNet+, which integrates artificial intelligence (AI) and rule-based methods to improve auto-alignment reliability and interpretability.
methodsFrom December 2019 to November 2020, 1,542 consecutive patients with scoliosis attending two local scoliosis clinics (The Duchess of Kent Children's Hospital at Sandy Bay in Hong Kong; Queen Mary Hospital in Pok Fu Lam on Hong Kong Island) were recruited. The biplanar radiographs of each patient were collected with our medical machine EOS™. The collected radiographs were recaptured using smartphones or screenshots, with deidentified images securely stored. Manually labelled landmarks and alignment parameters by a spine surgeon were considered as ground truth (GT). The data were split 8:2 to train and internally test SpineHRNet+, respectively. This was followed by a prospective validation on another 337 patients. Quantitative analyses of landmark predictions were conducted, and reliabilities of auto-alignment were assessed using linear regression and Bland-Altman plots. Deformity severity and sagittal abnormality classifications were evaluated by confusion matrices.
findingsSpineHRNet+ achieved accurate landmark detection with mean Euclidean distance errors of 2·78 and 5·52 pixels on posteroanterior and lateral radiographs, respectively. The mean angle errors between predictions and GT were 3·18° and 6·32° coronally and sagittally. All predicted alignments were strongly correlated with GT (
interpretationThe auto-analysis provided by SpineHRNet+ was reliable and continuous and it might offer the potential to assist clinical work and facilitate large-scale clinical studies.
fundingRGC Research Impact Fund (R5017-18F), Innovation and Technology Fund (ITS/404/18), and the AOSpine East Asia Fund (AOSEA(R)2019-06).
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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.