Volume 4, Issue 4 (December, 2024) – 13 articles
Cover Picture:
Aim: The purpose of this study is to investigate the utility of incorporating magnetic resonance imaging (MRI) into an artificial intelligence (AI) model to preoperatively predict pseudarthrosis for patients undergoing adult spinal deformity (ASD) surgery.
Methods: A retrospective cohort study was conducted on patients undergoing ASD surgery at Vanderbilt University Medical Center with at least 2 years of follow-up. We first collected demographic variables and measured traditional radiographic variables with Surgimap software. The primary outcome of interest was pseudarthrosis, defined as mechanical pain without evidence of bony union with or without a rod fracture. Next, cohort differences between patients diagnosed with and without pseudarthrosis were evaluated with t-tests for continuous variables and chi-squared tests for categorical variables using Bonferroni-Holm multiple comparison correction. Using a subpopulation of patients with preoperative thoracic MRI available, a three-dimensional convolutional neural network (3D-CNN) with five-fold nested cross-validation was developed to predict pseudarthrosis - accuracy was evaluated with the Youden index. Finally, class activation mapping (CAM) was conducted to visualize the MRI features utilized by the model for accurate classifications.
Results: Of 191 patients undergoing ASD surgery, the demographic and traditional radiographic variables were collected, and only age was observed to be significantly different between the patients diagnosed with pseudarthrosis (69.9 ± 10.1 years old) and those without (60.9 ± 19.9), with a t-test P-value of 0.003. The 3D-CNN demonstrated an average Youden index of 0.49 ± 0.25 on the withheld data, with a P-value of 5.50e-3 compared to an equivocal null model. Finally, CAM consistently revealed posterior adipose tissue to be most important in preoperatively predicting pseudarthrosis.
Conclusion: Adipose tissue features in MRI, independent of body mass index (BMI), may be useful for preoperatively predicting pseudarthrosis. Overall, this work demonstrates the capabilities of raw imaging AI in spine surgery and can serve as the basis for a deeper biological inquiry into the pathogenesis of pseudarthrosis.
view this paper Aim: The purpose of this study is to investigate the utility of incorporating magnetic resonance imaging (MRI) into an artificial intelligence (AI) model to preoperatively predict pseudarthrosis for patients undergoing adult spinal deformity (ASD) surgery.
Methods: A retrospective cohort study was conducted on patients undergoing ASD surgery at Vanderbilt University Medical Center with at least 2 years of follow-up. We first collected demographic variables and measured traditional radiographic variables with Surgimap software. The primary outcome of interest was pseudarthrosis, defined as mechanical pain without evidence of bony union with or without a rod fracture. Next, cohort differences between patients diagnosed with and without pseudarthrosis were evaluated with t-tests for continuous variables and chi-squared tests for categorical variables using Bonferroni-Holm multiple comparison correction. Using a subpopulation of patients with preoperative thoracic MRI available, a three-dimensional convolutional neural network (3D-CNN) with five-fold nested cross-validation was developed to predict pseudarthrosis - accuracy was evaluated with the Youden index. Finally, class activation mapping (CAM) was conducted to visualize the MRI features utilized by the model for accurate classifications.
Results: Of 191 patients undergoing ASD surgery, the demographic and traditional radiographic variables were collected, and only age was observed to be significantly different between the patients diagnosed with pseudarthrosis (69.9 ± 10.1 years old) and those without (60.9 ± 19.9), with a t-test P-value of 0.003. The 3D-CNN demonstrated an average Youden index of 0.49 ± 0.25 on the withheld data, with a P-value of 5.50e-3 compared to an equivocal null model. Finally, CAM consistently revealed posterior adipose tissue to be most important in preoperatively predicting pseudarthrosis.
Conclusion: Adipose tissue features in MRI, independent of body mass index (BMI), may be useful for preoperatively predicting pseudarthrosis. Overall, this work demonstrates the capabilities of raw imaging AI in spine surgery and can serve as the basis for a deeper biological inquiry into the pathogenesis of pseudarthrosis.
