Automatic detection and voxel‐wise mapping of lumbar spine Modic changes with deep learning

Gao, Kenneth T.; Tibrewala, Radhika; Hess, Madeline; Bharadwaj, Upasana; Inamdar, Gaurav; Link, Thomas M.; Chin, Cynthia T.; Pedoia, Valentina; Majumdar, Sharmila

doi:10.1002/jsp2.1204

Cited by 11 publications

(14 citation statements)

References 39 publications

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“…While the study achieved a high accuracy level, it only explored the presence of bone marrow signal changes and endplate defects, without assessing specific signal changes in MRI. In 2021, Gao et al 24 . analyzed the differences in the consistency of physicians' diagnostic MCs typing with or without deep learning model assistance.…”

Section: Discussionmentioning

confidence: 99%

“…However, the study only investigated the presence of bone marrow signal changes without specifying MRI signal changes. In 2021, Gao et al 24 examined consistency differences in physicians' diagnostic MCs staging with and without deep learning model assistance, revealing increased inter-rater agreement scores when using a deep learning model. In 2022, Windsor et al 25 proposed the SpineNetv2 network, offering faster vertebral phase detection than the SpineNet network but only exploring bone marrow signal changes without specific MRI signal change determination.…”

Section: Introductionmentioning

confidence: 99%

“…However, the study only investigated the presence of bone marrow signal changes without specifying MRI signal changes. In 2021, Gao et al 24 . examined consistency differences in physicians' diagnostic MCs staging with and without deep learning model assistance, revealing increased inter‐rater agreement scores when using a deep learning model.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Detection and Classification of Modic Changes in MRI Images Using Deep Learning: Intelligent Assisted Diagnosis System

Liu,

Wang,

You

et al. 2023

Orthopaedic Surgery

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ObjectiveModic changes (MCs) are the most prevalent classification system for describing intravertebral MRI signal intensity changes. However, interpreting these intricate MRI images is a complex and time‐consuming process. This study investigates the performance of single shot multibox detector (SSD) and ResNet18 network‐based automatic detection and classification of MCs. Additionally, it compares the inter‐observer agreement and observer‐classifier agreement in MCs diagnosis to validate the feasibility of deep learning network‐assisted detection of classified MCs.MethodA retrospective analysis of 140 patients with MCs who underwent MRI diagnosis and met the inclusion and exclusion criteria in Tianjin Hospital from June 2020 to June 2021 was used as the internal dataset. This group consisted of 55 males and 85 females, aged 25 to 89 years, with a mean age of (59.0 ± 13.7) years. An external test dataset of 28 patients, who met the same criteria and were assessed using different MRI equipment at Tianjin Hospital, was also gathered, including 11 males and 17 females, aged 31 to 84 years, with a mean age of 62.7 ± 10.9 years. After Physician 1 (with 15 years of experience) annotated all MRI images, the internal dataset was imported into the deep learning model for training. The model comprises an SSD network for lesion localization and a ResNet18 network for lesion classification. Performance metrics, including accuracy, recall, precision, F1 score, confusion matrix, and inter‐observer agreement parameter Kappa value, were used to evaluate the model's performance on the internal and external datasets. Physician 2 (with 1 year of experience) re‐labeled the internal and external test datasets to compare the inter‐observer agreement and observer‐classifier agreement.ResultsIn the internal dataset, when models were utilized for the detection and classification of MCs, the accuracy, recall, precision and F1 score reached 86.25%, 87.77%, 84.92% and 85.60%, respectively. The Kappa value of the inter‐observer agreement was 0.768 (95% CI: 0.656, 0.847),while observer‐classifier agreement was 0.717 (95% CI: 0.589, 0.809).In the external test dataset, the model's the accuracy, recall, precision and F1 scores for diagnosing MCs reached 75%, 77.08%, 77.80% and 74.97%, respectively. The inter‐observer agreement was 0.681 (95% CI: 0.512, 0.677), and observer‐classifier agreement was 0.519 (95% CI: 0.290, 0.690).ConclusionThe model demonstrated strong performance in detecting and classifying MCs, achieving high agreement with physicians in MCs diagnosis. These results suggest that deep learning models have the potential to facilitate the application of intelligent assisted diagnosis techniques in the field of spine research.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Detection and Classification of Modic Changes in MRI Images Using Deep Learning: Intelligent Assisted Diagnosis System

Liu,

Wang,

You

et al. 2023

Orthopaedic Surgery

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“…A deep learning‐based approach to the analysis of Modic images has provided findings that are substantially in agreement with spinal evaluations by radiologists with the potential to improve inter‐rater reliability of such evaluations of MRI assessments. 269 AI and DML are thus revolutionizing spinal imaging, including visualization of vertebrae and IVDs in diagnostic and surgical outcome and rehabilitation assessments, biomechanics, and motion analysis. 270 …”

Section: How Ai and Dml Have Im...mentioning

confidence: 99%

“…A deep learningbased approach to the analysis of Modic images has provided findings that are substantially in agreement with spinal evaluations by radiologists with the potential to improve inter-rater reliability of such evaluations of MRI assessments. 269 AI and DML are thus revolutionizing spinal imaging, including visualization of vertebrae and IVDs in diagnostic and surgical outcome and rehabilitation assessments, biomechanics, and motion analysis. 270 DML has also improved the analysis of clinical spinal images in the last 6 years in the segmentation, pathology detection, diagnosis, and quantitative evaluation of MRIs.…”

Section: Application Of Ai In the Analysis Of Ivdd Lbp And Patient Tr...mentioning

confidence: 99%

An update on animal models of intervertebral disc degeneration and low back pain: Exploring the potential of artificial intelligence to improve research analysis and development of prospective therapeutics

et al. 2023

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Animal models have been invaluable in the identification of molecular events occurring in and contributing to intervertebral disc (IVD) degeneration and important therapeutic targets have been identified. Some outstanding animal models (murine, ovine, chondrodystrophoid canine) have been identified with their own strengths and weaknesses. The llama/alpaca, horse and kangaroo have emerged as new large species for IVD studies, and only time will tell if they will surpass the utility of existing models. The complexity of IVD degeneration poses difficulties in the selection of the most appropriate molecular target of many potential candidates, to focus on in the formulation of strategies to effect disc repair and regeneration. It may well be that many therapeutic objectives should be targeted simultaneously to effect a favorable outcome in human IVD degeneration. Use of animal models in isolation will not allow resolution of this complex issue and a paradigm shift and adoption of new methodologies is required to provide the next step forward in the determination of an effective repairative strategy for the IVD. AI has improved the accuracy and assessment of spinal imaging supporting clinical diagnostics and research efforts to better understand IVD degeneration and its treatment. Implementation of AI in the evaluation of histology data has improved the usefulness of a popular murine IVD model and could also be used in an ovine histopathological grading scheme that has been used to quantify degenerative IVD changes and stem cell mediated regeneration. These models are also attractive candidates for the evaluation of novel anti‐oxidant compounds that counter inflammatory conditions in degenerate IVDs and promote IVD regeneration. Some of these compounds also have pain‐relieving properties. AI has facilitated development of facial recognition pain assessment in animal IVD models offering the possibility of correlating the potential pain alleviating properties of some of these compounds with IVD regeneration.

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Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis

et al. 2023

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Introduction Low back pain is the leading contributor to disability burden globally. It is commonly due to degeneration of the lumbar intervertebral discs (LDD). Magnetic resonance imaging (MRI) is the current best tool to visualize and diagnose LDD, but places high time demands on clinical radiologists. Automated reading of spine MRIs could improve speed, accuracy, reliability and cost effectiveness in radiology departments. The aim of this review and meta-analysis was to determine if current machine learning algorithms perform well identifying disc degeneration, herniation, bulge and Modic change compared to radiologists. Methods A PRISMA systematic review protocol was developed and four electronic databases and reference lists were searched. Strict inclusion and exclusion criteria were defined. A PROBAST risk of bias and applicability analysis was performed. Results 1350 articles were extracted. Duplicates were removed and title and abstract searching identified original research articles that used machine learning (ML) algorithms to identify disc degeneration, herniation, bulge and Modic change from MRIs. 27 studies were included in the review; 25 and 14 studies were included multi-variate and bivariate meta-analysis, respectively. Studies used machine learning algorithms to assess LDD, disc herniation, bulge and Modic change. Models using deep learning, support vector machine, k-nearest neighbors, random forest and naïve Bayes algorithms were included. Meta-analyses found no differences in algorithm or classification performance. When algorithms were tested in replication or external validation studies, they did not perform as well as when assessed in developmental studies. Data augmentation improved algorithm performance when compared to models used with smaller datasets, there were no performance differences between augmented data and large datasets. Discussion This review highlights several shortcomings of current approaches, including few validation attempts or use of large sample sizes. To the best of the authors' knowledge, this is the first systematic review to explore this topic. We suggest the utilization of deep learning coupled with semi- or unsupervised learning approaches. Use of all information contained in MRI data will improve accuracy. Clear and complete reporting of study design, statistics and results will improve the reliability and quality of published literature.

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Automatic detection and voxel‐wise mapping of lumbar spine Modic changes with deep learning

Cited by 11 publications

References 39 publications

Automatic Detection and Classification of Modic Changes in MRI Images Using Deep Learning: Intelligent Assisted Diagnosis System

Automatic Detection and Classification of Modic Changes in MRI Images Using Deep Learning: Intelligent Assisted Diagnosis System

An update on animal models of intervertebral disc degeneration and low back pain: Exploring the potential of artificial intelligence to improve research analysis and development of prospective therapeutics

Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis

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