Trial reportBMC neurology2022
Deep learning algorithm to evaluate cervical spondylotic myelopathy using lateral cervical spine radiograph.
Trial report in BMC neurology, 2022. 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.
What it found
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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.
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, 1 synthesis or guideline pooled it, 38 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
- Efficacy Comparison of Osteopathic Muscle Energy Techniques and Cervical Mobilization on Pain, Disability, and Proprioception in Cervical Spondylosis Patients.Medical science monitor : international medical journal of experimental and clinical research · 2024Trial
- Degenerative cervical myelopathy diagnosis: applicability of artificial intelligence-enhanced modalities in a systematic review and meta-analysis.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 · 2026Review
- Development and multi-institutional validation of a deep learning algorithm for predicting cervical cord compression using dynamic cervical lateral radiographs.Scientific reports · 2026Article
- Artificial intelligence and machine learning based predictive analytics in cervical myelopathy - a scoping review.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
- Automated diagnostic of cervical spondylosis on multimodal medical images with a multi-task deep learning model.Nature communications · 2026Article
- CerviHFENet: hybrid feature extraction-based deep learning for multi-label classification of upper cervical spine abnormalities in X-ray imaging.BMC medical imaging · 2025Article
- Deep learning-based prediction of cervical canal stenosis from mid-sagittal T2-weighted MRI.Skeletal radiology · 2025Article
- Prediction of cervical spondylotic myelopathy from a plain radiograph using deep learning with convolutional neural networks.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 · 2025Article
- Knowledge, attitude, and practice toward cervical spondylosis among the healthy general population.BMC public health · 2025Article
- Automated detection of cervical spondylotic myelopathy: harnessing the power of natural language processing.Frontiers in neuroscience · 2025Article
- Automated measurement of lumbar pedicle screw parameters using deep learning algorithm on preoperative CT scans.Journal of bone oncology · 2024Article
- Detection of Cervical Foraminal Stenosis from Oblique Radiograph Using Convolutional Neural Network Algorithm.Yonsei medical journal · 2024Article
- Would the one-stage combined approach lead to better long-term neurological outcomes than the posterior approach alone in multilevel degenerative cervical myelopathy patients with T2-Weighted increased signal intensity? An 8-year follow-up results and propensity score matching analysis.BMC musculoskeletal disorders · 2024Article
- Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.Neurospine · 2024Article
- Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images.Sensors (Basel, Switzerland) · 2024Article
- Initial study on an expert system for spine diseases screening using inertial measurement unit.Scientific reports · 2023Article
- Real-World Implementation of Artificial Intelligence/Machine Learning for Managing Surgical Spine Patients at 2 Academic Health Care Systems.International journal of spine surgery · 2023Article
- [Back-forward bending CT in simulated surgical position to evaluate the remaining real angle and flexibility of thoracolumbar kyphosis secondary to old osteoporotic vertebral compression fracture].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2023Article
- A deep belief network-based clinical decision system for patients with osteosarcoma.Frontiers in immunology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep learning (DL) is an advanced machine learning approach used in different areas such as image analysis, bioinformatics, and natural language processing. A convolutional neural network (CNN) is a representative DL model that is highly advantageous for imaging recognition and classification This study aimed to develop a CNN using lateral cervical spine radiograph to detect cervical spondylotic myelopathy (CSM).
methodsWe retrospectively recruited 207 patients who visited the spine center of a university hospital. Of them, 96 had CSM (CSM patients) while 111 did not have CSM (non-CSM patients). CNN algorithm was used to detect cervical spondylotic myelopathy. Of the included patients, 70% (145 images) were assigned randomly to the training set, while the remaining 30% (62 images) to the test set to measure the model performance.
resultsThe accuracy of detecting CSM was 87.1%, and the area under the curve was 0.864 (95% CI, 0.780-0.949).
conclusionThe CNN model using the lateral cervical spine radiographs of each patient could be helpful in the diagnosis of CSM.
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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.