ABSTRACT
Background: Lumbar disc herniation (LDH) is a prevalent cause of low back pain. LDH patients commonly experience paraspinal muscle atrophy and fatty infiltration (FI), which further exacerbates the symptoms of low back pain. Magnetic resonance imaging (MRI) is crucial for assessing paraspinal muscle condition. Our study aims to develop a dual-model for automated muscle segmentation and FI annotation on MRI, assisting clinicians evaluate LDH conditions comprehensively. Methods: The study retrospectively collected data diagnosed with LDH from December 2020 to May 2022. The dataset was split into a 7:3 ratio for training and testing, with an external test set prepared to validate model generalizability. The model's performance was evaluated using average precision (AP), recall and F1 score. The consistency was assessed using the Dice similarity coefficient (DSC) and Cohen's Kappa. The mean absolute percentage error (MAPE) was calculated to assess the error of the model measurements of relative cross-sectional area (rCSA) and FI. Calculate the MAPE of FI measured by threshold algorithms to compare with the model. Results: A total of 417 patients being evaluated, comprising 216 males and 201 females, with a mean age of 49 ± 15 years. In the internal test set, the muscle segmentation model achieved an overall DSC of 0.92 ± 0.10, recall of 92.60%, and AP of 0.98. The fat annotation model attained a recall of 91.30%, F1 Score of 0.82, and Cohen's Kappa of 0.76. However, there was a decrease on the external test set. For rCSA measurements, except for longissimus (10.89%), the MAPE of other muscles was less than 10%. When comparing the errors of FI for each paraspinal muscle, the MAPE of the model was lower than that of the threshold algorithm. Conclusion: The models demonstrate outstanding performance, with lower error in FI measurement compared to thresholding algorithms.
ABSTRACT
BACKGROUND: Methods for grading and localization of lumbar disc herniation (LDH) on MRI are complex, time-consuming, and subjective. Utilizing deep learning (DL) models as assistance would mitigate such complexities. PURPOSE: To develop an interpretable DL model capable of grading and localizing LDH. STUDY TYPE: Retrospective. SUBJECTS: 1496 patients (M/F: 783/713) were evaluated, and randomly divided into training (70%), validation (10%), and test (20%) sets. FIELD STRENGTH/SEQUENCE: 1.5T MRI for axial T2-weighted sequences (spin echo). ASSESSMENT: The training set was annotated by three spinal surgeons using the Michigan State University classification to train the DL model. The test set was annotated by a spinal surgery expert (as ground truth labels), and two spinal surgeons (comparison with the trained model). An external test set was employed to evaluate the generalizability of the DL model. STATISTICAL TESTS: Calculated intersection over union (IoU) for detection consistency, utilized Gwet's AC1 to assess interobserver agreement, and evaluated model performance based on sensitivity and specificity, with statistical significance set at P < 0.05. RESULTS: The DL model achieved high detection consistency in both the internal test dataset (grading: mean IoU 0.84, recall 99.6%; localization: IoU 0.82, recall 99.5%) and external test dataset (grading: 0.72, 98.0%; localization: 0.71, 97.6%). For internal testing, the DL model (grading: 0.81; localization: 0.76), Rater 1 (0.88; 0.82), and Rater 2 (0.86; 0.83) demonstrated results highly consistent with the ground truth labels. The overall sensitivity of the DL model was 87.0% for grading and 84.0% for localization, while the specificity was 95.5% and 94.4%. For external testing, the DL model showed an appreciable decrease in consistency (grading: 0.69; localization: 0.66), sensitivity (77.2%; 76.7%), and specificity (92.3%; 91.8%). DATA CONCLUSION: The classification capabilities of the DL model closely resemble those of spinal surgeons. For future improvement, enriching the diversity of cases could enhance the model's generalization. TECHNICAL EFFICACY: Stage 2.
ABSTRACT
BACKGROUND: ACDF treatment of CSM is currently recognized as a surgical method with reliable efficacy. However, the cervical radiographic findings in a certain group of patients showed that the symptoms were not completely relieved. This study will investigate the relationship between cervical parameters and prognoses after ACDF surgery. METHODS: This study collected cases of CSM treated with ACDF in Zhongda Hospital from May 2014 to June 2018. The investigators recorded gender, age, cervical sagittal parameters, fusion segment, BMI, symptom duration, and NDI score. To compare the changes of parameters after surgery and explore the correlation between each factor and NDI score. RESULTS: Generally, cervical lordosis increased and TS-CL decreased after surgery and during follow-up. Postoperative T1S, SVA and SCA decreased significantly compared to preoperative. T1S was positively correlated with CL (r = 0.245), SVA (r = 0.184), and negatively correlated with SCA (r = - 0.314) and NT (r = - 0.222). The last follow-up NDI score was positively correlated with T1S (r = 0.689), SVA (r = 0.155), TS-CL (r = 0.496), and age (r = 0.194), while negatively correlated with SCA (r = - 0.142). A linear regression model was established with the following formula: NDI = 0.809 × (T1S) - 0.152 × (CL) + 1.962 × (Sex) + 0.110 × (Age). T1S (B = 0.205, P < 0.001), CL (B = - 0.094, P = 0.041), and NT (B = 0.142, P = 0.023) were independent risk factors that affected whether the last follow-up NDI score was greater than preoperative. CONCLUSIONS: In ACDF treatment of CSM, there exists a close correlation between cervical sagittal parameters and NDI scores. T1S, CL, sex, and age were linearly dependent on NDI scores. The increase of T1S, NT, and the decrease of CL were risk factors that affected follow-up NDI score greater than preoperative. Reducing T1S is beneficial to clinical recovery.