ArticleFrontiers in public health2023
Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts.
Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review.European radiology · 2025Pooled it
- A Comprehensive Clinical Decision Support System for the Early Diagnosis of Axial Spondyloarthritis: Multi-Sequence MRI, Clinical Risk Integration, and Explainable Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Automated grading and diagnosis of sacroiliitis on CT images using a 3D convolutional neural network: a multicenter retrospective study.Scientific reports · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection model.Annals of medicine · 2025Article
- Diagnosing Ankylosing Spondylitis via Architecture-Modified ResNet and Combined Conventional Magnetic Resonance Imagery.Journal of imaging informatics in medicine · 2025Article
- FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI.Scientific reports · 2025Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- Digital Health for Equitable Rheumatic Care: Integrating Real-World Experiences to Guide Policy Pathways.Healthcare (Basel, Switzerland) · 2025Review
- From advanced imaging to molecular insights: the state of the art in omics for axial spondyloarthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- Exploring T Cell and NK Cell Involvement in Ankylosing Spondylitis Through Single-Cell Sequencing.Journal of cellular and molecular medicine · 2024Article
- Machine learning and deep learning for the diagnosis and treatment of ankylosing spondylitis- a scoping review.Journal of clinical orthopaedics and trauma · 2024Article
- Radiological Insights into Sacroiliitis: A Narrative Review.Clinics and practice · 2024Review
- ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI.Biomedicines · 2023Article
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Authors and funding
31 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: The diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS. Methods: In this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients. Results: The ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care. Discussion: In this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.
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