ArticleDiagnostics (Basel, Switzerland)2023
Automatic Detection, Classification, and Grading of Lumbar Intervertebral Disc Degeneration Using an Artificial Neural Network Model.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Reliability and Reproducibility of a Novel MRI-Based Classification System for Lumbar Intervertebral Disc Degeneration: A Comparative Study with the Pfirrmann Classification.Journal of imaging informatics in medicine · 2026Article
- EADC-Net: A Dual-Stage Deep Learning Framework with Spatial and Channel Central Attention for Automated Detection of Lumbar Degenerative Disc Disease from Radiographs.Journal of imaging informatics in medicine · 2026Article
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- SpineScan: a deep learning model for lumbar spine MRI annotation and Pfirrmann grading 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 · 2026Article
- Intervertebral disc degeneration.Nature reviews. Disease primers · 2026Review
- Preoperative evaluation of C2 pedicle screw placement using a deep learning model: Development and validation study.PloS one · 2026Article
- Automated radiomics pipeline integrating TotalSpineSeg for Pfirrmann grading of lumbar disc degeneration with SHAP-based interpretability.Frontiers in medicine · 2026Article
- Text-guided multimodal deep learning in magnetic resonance imaging for spinal structures segmentation and lumbar abnormalities identification.Quantitative imaging in medicine and surgery · 2025Article
- Deep Learning-Based Differentiation of Vertebral Body Lesions on Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Comparative effectiveness of transforaminal epidural steroid injection: subpedicular versus Kambin's triangle technique: a single-centre experience.Journal of spine surgery (Hong Kong) · 2025Article
- Combining Metabolomics and Proteomics to Reveal Key Serum Compounds Related to Canine Intervertebral Disc Herniation.Metabolites · 2025Article
- A systematic review of biportal endoscopic spinal surgery with interbody fusion.Asian spine journal · 2025Article
- Deep Learning Assisted Classification of T1ρ-MR Based Intervertebral Disc Degeneration Phases.Journal of magnetic resonance imaging : JMRI · 2025Article
- An Automatized Deep Segmentation and Classification Model for Lumbar Disk Degeneration and Clarification of Its Impact on Clinical Decisions.Global spine journal · 2025Article
- Performance and clinical implications of machine learning models for detecting cervical ossification of the posterior longitudinal ligament: a systematic review.Asian spine journal · 2025Article
- Enhancing LDD diagnosis with YOLOv9-AID: simultaneous detection of pfirrmann grading, disc herniation, HIZ, and Schmorl's nodules.Frontiers in bioengineering and biotechnology · 2025Article
- External validation of SpineNetV2 on a comprehensive set of radiological features for grading lumbosacral disc pathologies.North American Spine Society journal · 2024Article
- Classification of lumbar spine disorders using large language models and MRI segmentation.BMC medical informatics and decision making · 2024Article
- Applications of Artificial Intelligence and Machine Learning in Spine MRI.Bioengineering (Basel, Switzerland) · 2024Review
- Artificial Intelligence Detection of Cervical Spine Fractures Using Convolutional Neural Network Models.Neurospine · 2024Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
BACKGROUND AND
objectivesIntervertebral disc degeneration (IDD) is a common cause of symptomatic axial low back pain. Magnetic resonance imaging (MRI) is currently the standard for the investigation and diagnosis of IDD. Deep learning artificial intelligence models represent a potential tool for rapidly and automatically detecting and visualizing IDD. This study investigated the use of deep convolutional neural networks (CNNs) for the detection, classification, and grading of IDD.
methodsSagittal images of 1000 IDD T2-weighted MRI images from 515 adult patients with symptomatic low back pain were separated into 800 MRI images using annotation techniques to create a training dataset (80%) and 200 MRI images to create a test dataset (20%). The training dataset was cleaned, labeled, and annotated by a radiologist. All lumbar discs were classified for disc degeneration based on the Pfirrmann grading system. The deep learning CNN model was used for training in detecting and grading IDD. The results of the training with the CNN model were verified by testing the grading of the dataset using an automatic model.
resultsThe training dataset of the sagittal intervertebral disc lumbar MRI images found 220 IDDs of grade I, 530 of grade II, 170 of grade III, 160 of grade IV, and 20 of grade V. The deep CNN model was able to detect and classify lumbar IDD with an accuracy of more than 95%.
conclusionThe deep CNN model can reliably automatically grade routine T2-weighted MRIs using the Pfirrmann grading system, providing a quick and efficient method for lumbar IDD classification.
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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.