ArticleJournal of pain research2023
Deep Learning Algorithm Trained on Cervical Magnetic Resonance Imaging to Predict Outcomes of Transforaminal Epidural Steroid Injection for Radicular Pain from Cervical Foraminal Stenosis.
Article in Journal of pain research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for prediction of clinical response and therapeutic value in interventional pain management: a scoping review.Frontiers in digital health · 2026Pooled it
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- Artificial Intelligence for Procedural Guidance of Spinal Epidural Steroid Injections: A Scoping Review.Cureus · 2026Review
- Machine learning for patient selection in corticosteroid decision making in knee osteoarthritis: A feasibility model.World journal of methodology · 2025Article
- A 68-year-old man presenting with tingling pain in the medial aspect of the right forearm and hand.Journal of Yeungnam medical science · 2025Article
- Artificial Intelligence and Machine Learning in Spine Research: A New Frontier.Bioengineering (Basel, Switzerland) · 2024Article
- Assessment of Bone Age Based on Hand Radiographs Using Regression-Based Multi-Modal Deep Learning.Life (Basel, Switzerland) · 2024Article
- Convolutional neural network algorithm trained on lumbar spine radiographs to predict outcomes of transforaminal epidural steroid injection for lumbosacral radicular pain from spinal stenosis.Scientific reports · 2024Article
- Deep Learning Algorithm Trained on Oblique Cervical Radiographs to Predict Outcomes of Transforaminal Epidural Steroid Injection for Pain from Cervical Foraminal Stenosis.Pain and therapy · 2024Article
- Use of artificial intelligence in the field of pain medicine.World journal of clinical cases · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: A convolutional neural network (CNN) is one of the representative deep learning (DL) model that is especially useful for image recognition and classification. In the current study, using cervical axial magnetic resonance imaging (MRI) data obtained prior to transforaminal epidural steroid injection (TFESI), we developed a CNN model to predict the therapeutic outcome of cervical TFESI in patients with cervical foraminal stenosis. Patients and Methods: We retrospectively recruited 288 patients with cervical foraminal stenosis who received cervical TFESI due to cervical radicular pain. We collected single T2-axial spine MR image obtained from each patient. The image showing narrowest width of the neural foramen in the level at which TFESI was performed was used for input data. A "favor outcome" was defined as a ≥ 50% reduction in the NRS score at 2 months post-TFESI vs the pretreatment NRS score. A "poor outcome" was defined as a < 50% reduction in the NRS score at 2 months post-TFESI vs the pretreatment score. Results: The area under the curve of our developed model for predicting therapeutic outcome of cervical TFESI in patients with cervical spinal stenosis was 0.801. Conclusion: We showed that a CNN model trained using cervical axial MRI could be helpful for predicting therapeutic outcome after cervical TFESI in patients with cervical foraminal stenosis.
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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.