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Deep learning-based structure segmentation and intramuscular fat annotation on lumbar magnetic resonance imaging. | LitMetric

AI Article Synopsis

  • Lumbar disc herniation (LDH) often leads to low back pain and muscle issues like atrophy and fatty infiltration, and this study aims to create a dual-model using MRI for better assessment of these conditions.
  • The research utilized a dataset of 417 LDH patients, where the muscle segmentation model showed strong performance metrics, such as a Dice similarity coefficient of 0.92, while the fat annotation model also performed well with a recall of 91.30%.
  • Results indicated that the models provided lower error rates in fat infiltration measurements compared to traditional threshold algorithms, showcasing their potential for improving clinical evaluations of LDH.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11406510PMC
http://dx.doi.org/10.1002/jsp2.70003DOI Listing

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