Current development and prospects of deep learning in spine image analysis: a literature review

Qu, Bo; Cao, Jianpeng; Qian, Chen; Wu, Jin‐Lei; Lin, Jianzhong; Wang, Liansheng; Ouyang, Lin; Chen, Yongfa; Yan, Liyue; Hong, Qing; Zheng, Gaofeng; Qu, Xiaobo

doi:10.21037/qims-21-939

Cited by 22 publications

(12 citation statements)

References 150 publications

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“…The use of artificial intelligence for the automatic, quantitative, and efficient measurement of VBH and IDH is an urgent need for clinical diagnosis and treatment of lumbar spine diseases and is expected to provide clinicians with a basis for diagnosing and grading lumbar spine diseases, selecting treatment options, and assessing prognosis (19,(25)(26)(27). IDH was the first imaging indicator used to evaluate intervertebral disc degeneration, and there is a relationship between morphological changes in the lumbar vertebral body and VBH and IDH, as well as between LBP and disc pathology (26,28).…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based quantitative morphological study of anteroposterior digital radiographs of the lumbar spine

Chen¹,

Wang²,

Chen³

et al. 2024

Quant Imaging Med Surg

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Background: Morphological parameters of the lumbar spine are valuable in assessing lumbar spine diseases. However, manual measurement of lumbar morphological parameters is time-consuming. Deep learning has automatic quantitative and qualitative analysis capabilities. To develop a deep learning-based model for the automatic quantitative measurement of morphological parameters from anteroposterior digital radiographs of the lumbar spine and to evaluate its performance. Methods: This study used 1,368 anteroposterior digital radiographs of the lumbar spine to train a deep learning model to measure the quantitative morphological indicators, including L1 to L5 vertebral body height (VBH) and L1-L2 to L4-L5 intervertebral disc height (IDH). The means of the manual measurements by three radiologists were used as the reference standard. The parameters predicted by the model were analyzed against the manual measurements using paired t-tests. Percentage of Correct Key Points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots were performed to assess the performance of the model.Results: Within the 3-mm distance threshold, the model had a PCK range of 99.77-99.46% for the L1 to L4 vertebrae and 77.37% for the L5 vertebrae. Except for VBH-L5 and IDH_L3-L4, IDH_L4-L5 (P<0.05), the estimated values of the model in the remaining parameters were not statistically significant compared with the reference standard (P>0.05). Except for VBH-L5 and IDH_L4-L5, the model showed good correlation and consistency with the reference standard (ICC =0.84-0.96, r=0.85-0.97, MAE =0.

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Section: Discussionmentioning

confidence: 99%

Deep learning-based quantitative morphological study of anteroposterior digital radiographs of the lumbar spine

Chen¹,

Wang²,

Chen³

et al. 2024

Quant Imaging Med Surg

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“…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. 271 Positron emission tomography/computed tomography (PET/CT) of the lumbar spine using 18 F‐fluoro‐deoxyglucose (FDG) and 18 F‐sodium fluoride (NaF) tracers have imaged spinal inflammation and microcalcification, in degenerate spinal structures. 272 Functional MRI and 18 F‐FDG PET imaging have helped to define pain‐relevant, physiologically active brain regions.…”

Section: How Ai and Dml Have Im...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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“…In particular, deep learning has been used in radiation oncology applications to investigate automated error detection [26][27][28] and to analyze the vertebral column [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Automated detection of vertebral body misalignments in orthogonal kV and MV guided radiotherapy: application to a comprehensive retrospective dataset

Charters,

Luximon,

Petragallo

et al. 2024

Biomed. Phys. Eng. Express

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Objective: In image-guided radiotherapy (IGRT), off-by-one vertebral body misalignments are rare but potentially catastrophic. In this study, an automated method to detect such misalignments with IGRT radiographs was investigated using densely-connected convolutional networks (DenseNets) for applications towards real-time error prevention and retrospective error auditing. Approach: A total of 4213 images acquired from 527 radiotherapy patients aligned with planar kV or MV radiographs were used to develop and test error-detection software modules. Digitally reconstructed radiographs (DRRs) and setup images were retrieved and co-registered according to the clinically applied alignment contained in the DICOM REG files. A semi-automated algorithm was developed to simulate patient positioning errors on the anterior-posterior (AP) and lateral (LAT) images shifted by one vertebral body. A DenseNet architecture was designed to classify either AP images individually or AP and LAT image pairs. Receiver-operator characteristic curves (ROC) and areas under the curves (AUC) were computed to evaluate the classifiers on test subsets. Subsequently, the algorithm was applied to the entire dataset in order to retrospectively determine the absolute off-by-one vertebral body error rate for planar radiograph guided RT at our institution from 2011-2021. Main results: The AUCs for the kV models were 0.98 for unpaired AP and 0.99 for paired AP-LAT. The AUC for the MV AP model was 0.92. For a specificity of 95%, the paired kV model achieved a sensitivity of 99%. Application of the model to the entire dataset yielded a per-fraction off-by-one vertebral body error rate of 0.044% [0.0022%, 0.21%] for paired kV IGRT including one previously unreported error. Significance: Our error detection algorithm was successful in classifying vertebral body positioning errors with sufficient accuracy for retrospective quality control and real-time error prevention. The reported positioning error rate for planar radiograph IGRT is unique in being determined independently of an error reporting system.

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Current development and prospects of deep learning in spine image analysis: a literature review

Cited by 22 publications

References 150 publications

Deep learning-based quantitative morphological study of anteroposterior digital radiographs of the lumbar spine

Deep learning-based quantitative morphological study of anteroposterior digital radiographs of the lumbar spine

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

Automated detection of vertebral body misalignments in orthogonal kV and MV guided radiotherapy: application to a comprehensive retrospective dataset

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