ArticleJoint diseases and related surgery2022
Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods.
Article in Joint diseases and related surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Machine Learning and Deep Learning for the Diagnosis of Cervical Degenerative Diseases: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Comparison of clinical outcomes and complications of biportal and uniportal endoscopic decompression for the treatment of cervical spondylotic radiculopathy: A systematic review and meta-analysis.Joint diseases and related surgery · 2024Pooled it
- Comparative Outcomes of Anterior Cervical Discectomy and Fusion Vs Hybrid Surgery in Multilevel Cervical Degenerative Disc Disease: A Systematic Review and Meta-analysis.International journal of spine surgery · 2026Article
- Acupuncture for the Treatment of Cervical Spondylotic Radiculopathy: An Overview of Systematic Reviews.International journal of general medicine · 2025Review
- Comparison of fusion, arthroplasty and hybrid surgery outcomes in patients with two-level cervical disc disease.Joint diseases and related surgery · 2024Article
- Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images.Sensors (Basel, Switzerland) · 2024Article
- Artificial intelligence: Who must have autonomy the machine or the human?Joint diseases and related surgery · 2024Article
- The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods.Joint diseases and related surgery · 2023Article
- Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis.Frontiers in immunology · 2023Article
- Artificial intelligence, machine learning, and deep learning in orthopedic surgery.Joint diseases and related surgery · 2022Article
- Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesIn this study, we aimed to differentiate normal cervical graphs and graphs of diseases that cause mechanical neck pain by using deep convolutional neural networks (DCNN) technology. MATERIALS AND
methodsIn this retrospective study, the convolutional neural networks were used and transfer learning method was applied with the pre-trained VGG-16, VGG-19, Resnet-101, and DenseNet-201 networks. Our data set consisted of 161 normal lateral cervical radiographs and 170 lateral cervical radiographs with osteoarthritis and cervical degenerative disc disease.
resultsWe compared the performances of the classification models in terms of performance metrics such as accuracy, sensitivity, specificity, and precision metrics. Pre-trained VGG-16 network outperformed other models in terms of accuracy (93.9%), sensitivity (95.8%), specificity (92.0%), and precision (92.0%) results.
conclusionThe results of this study suggest that the deep learning methods are promising support tool in automated control of cervical graphs using the DCNN and the exclusion of normal graphs. Such a supportive tool may reduce the diagnosis time and provide radiologists or clinicians to have more time to interpret abnormal graphs.
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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.