Deep Learning for Adjacent Segment Disease at Preoperative MRI for                     Cervical Radiculopathy

Goedmakers, Caroline M W; Lak, Asad M.; Duey, Akiro H.; Senko, Alexander W.; Arnaout, Omar; Groff, Michael W.; Smith, Timothy R.; Vleggeert-Lankamp, Carmen; Zaidi, Hasan A.; Rana, Aakanksha; Boaro, Alessandro

doi:10.1148/radiol.2021204731

Cited by 15 publications

(9 citation statements)

References 27 publications

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“…However, patients who suffered from neck and shoulder pain were at risk of developing CR. [11] Since delayed diagnosis and CR treatment could lead to irreversible and serious consequences, early recognition of potential risk factors was important. However, identifying patients who may develop CR remains challenging for clinicians.…”

Section: Discussionmentioning

confidence: 99%

Age and flexors as risk factors for cervical radiculopathy: A new machine learning method

Pan,

Liu,

Chen

et al. 2024

Medicine

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This study aimed to investigate the risk factors for cervical radiculopathy (CR) along with identifying the relationships between age, cervical flexors, and CR. This was a retrospective cohort study, including 60 patients with CR enrolled between December 2018 and June 2020. In this study, we measured C2 to C7 Cobb angle, disc degeneration, endplate degeneration, and morphology of paraspinal muscles and evaluated the value of predictive methods using receiver operating characteristic curves. Next, we established a diagnostic model for CR using Fisher discriminant model and compared different models by calculating the kappa value. Age and cervical flexor factors were used to construct clinical predictive models, which were further evaluated by C-index, receiver operating characteristic curve, calibration curve, and decision curve analysis. Multivariate analysis showed that age and cervical flexors were potential risk factors for CR, while the diagnostic model indicated that both exerted the best diagnostic effect. The obtained diagnostic equation was as follows: y1 = 0.33 × 1 + 10.302 × 2–24.139; y2 = 0.259 × 1 + 13.605 × 2–32.579. Both the C-index and AUC in the training set reached 0.939. Moreover, the C-index and AUC values in the external validation set reached 0.961. We developed 2 models for predicting CR and also confirmed their validity. Age and cervical flexors were considered potential risk factors for CR. Our noninvasive inspection method could provide clinicians with a more potential diagnostic value to detect CR accurately.

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Section: Discussionmentioning

confidence: 99%

Age and flexors as risk factors for cervical radiculopathy: A new machine learning method

Pan,

Liu,

Chen

et al. 2024

Medicine

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“…Radiomics and deep learning (DL) approaches have proven to be effective methods for medical image analysis and obtained significant advances in the field of musculoskeletal system disease in recent years ( Leung et al, 2020 ; von Schacky et al, 2020 ; Huang et al, 2020 ; Won et al, 2020 ; Gao et al, 2021 ; Goedmakers et al, 2021 ; Bayramoglu et al, 2021 ; Wang et al, 2022 ; Hallinan et al, 2021 ; Niemeyer et al, 2021 ; Zheng et al, 2022 ; Abdullah and Rajasekaran, 2022 ; Gebre et al, 2022 ). Swiecicki et al (2021) proposed an automated DL model to evaluate the severity of knee osteoarthritis using knee radiographs according to the Kellgren–Lawrence grading system.…”

Section: Introductionmentioning

confidence: 99%

MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study

Xie,

Yang,

Jiang

et al. 2024

Front. Physiol.

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Objectives: To develop and validate an MRI radiomics-based decision support tool for the automated grading of cervical disc degeneration.Methods: The retrospective study included 2,610 cervical disc samples of 435 patients from two hospitals. The cervical magnetic resonance imaging (MRI) analysis of patients confirmed cervical disc degeneration grades using the Pfirrmann grading system. A training set (1,830 samples of 305 patients) and an independent test set (780 samples of 130 patients) were divided for the construction and validation of the machine learning model, respectively. We provided a fine-tuned MedSAM model for automated cervical disc segmentation. Then, we extracted 924 radiomic features from each segmented disc in T1 and T2 MRI modalities. All features were processed and selected using minimum redundancy maximum relevance (mRMR) and multiple machine learning algorithms. Meanwhile, the radiomics models of various machine learning algorithms and MRI images were constructed and compared. Finally, the combined radiomics model was constructed in the training set and validated in the test set. Radiomic feature mapping was provided for auxiliary diagnosis.Results: Of the 2,610 cervical disc samples, 794 (30.4%) were classified as low grade and 1,816 (69.6%) were classified as high grade. The fine-tuned MedSAM model achieved good segmentation performance, with the mean Dice coefficient of 0.93. Higher-order texture features contributed to the dominant force in the diagnostic task (80%). Among various machine learning models, random forest performed better than the other algorithms (p < 0.01), and the T2 MRI radiomics model showed better results than T1 MRI in the diagnostic performance (p < 0.05). The final combined radiomics model had an area under the receiver operating characteristic curve (AUC) of 0.95, an accuracy of 89.51%, a precision of 87.07%, a recall of 98.83%, and an F1 score of 0.93 in the test set, which were all better than those of other models (p < 0.05).Conclusion: The radiomics-based decision support tool using T1 and T2 MRI modalities can be used for cervical disc degeneration grading, facilitating individualized management.

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“…As the cognition of spine develops, DL presents a tremendous potential and function as an advantageous tool in this eld, which mainly focus on radiography [13][14][15]. However, this eld still lacks a global and comprehensive report to help researchers quickly understand the research trend and hotspot.…”

Section: Introductionmentioning

confidence: 99%

Emerging trends and research foci of deep learning in spine: bibliometric and visualization study

et al. 2023

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As the cognition of spine develops, deep learning (DL) presents a tremendous potential and function as an advantageous tool in this eld. In this study, bibliometrics and visual methods were adopted with a retrieval of Web of Science to provide a comprehensive overview of DL-spine research. VOSviewer and Citespace were primarily used for literature measurement and knowledge graph analysis. A total of 273 studies was retrieved focusing on DL in spine with a sum of 2407 citations, and the global total number of articles published showed a continuous increasing trend. China was the country with the largest number of publications, while USA was the country with the most citation. The top 2 journals were "European spine journal" and "Medical image analysis", and the most involved research area was Radiology Nuclear Medicine Medical Imaging. VOSviewer visually presented three clusters into "segmentation", "area", and "neural network". And CiteSpace indicated the keywords with the longest use were "magnetic resonance image" and "lumbar", while "agreement" and "automated detection" were the most popular keywords. The stage of DL-spine research is still in its infancy and its future is bright. Intercontinental cooperation, extensive application and more interpretable algorithms will exert more vitality in this eld.

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Deep Learning for Adjacent Segment Disease at Preoperative MRI for Cervical Radiculopathy

Cited by 15 publications

References 27 publications

Age and flexors as risk factors for cervical radiculopathy: A new machine learning method

Age and flexors as risk factors for cervical radiculopathy: A new machine learning method

MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study

Emerging trends and research foci of deep learning in spine: bibliometric and visualization study

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