Deep learning-based automated high-accuracy location and identification of fresh vertebral compression fractures from spinal radiographs: a multicenter cohort study

Abstract

Background: Digital radiography (DR) is a common and widely available examination. However, spinal DR cannot detect bone marrow edema, therefore, determining vertebral compression fractures (VCFs), especially fresh VCFs, remains challenging for clinicians.

Methods: We trained, validated, and externally tested the deep residual network (DRN) model that automated the detection and identification of fresh VCFs from spinal DR images. A total of 1,747 participants from five institutions were enrolled in this study and divided into the training cohort, validation cohort and external test cohorts (YHDH and BMUH cohorts). We evaluated the performance of DRN model based on the area under the receiver operating characteristic curve (AUC), feature attention maps, sensitivity, specificity, and accuracy. We compared it with five other deep learning models and validated and tested the model internally and externally and explored whether it remains highly accurate for an external test cohort. In addition, the influence of old VCFs on the performance of the DRN model was assessed.

Results: The AUC was 0.99, 0.89, and 0.88 in the validation, YHDH, and BMUH cohorts, respectively, for the DRN model for detecting and discriminating fresh VCFs. The accuracies were 81.45% and 72.90%, sensitivities were 84.75% and 91.43%, and specificities were 80.25% and 63.89% in the YHDH and BMUH cohorts, respectively. The DRN model generated correct activation on the fresh VCFs and accurate peak responses on the area of the target vertebral body parts and demonstrated better feature representation learning and classification performance. The AUC was 0.90 (95% confidence interval [CI] 0.84-0.95) and 0.84 (95% CI 0.72-0.93) in the non-old VCFs and old VCFs groups, respectively, in the YHDH cohort (p = 0.067). The AUC was 0.89 (95% CI 0.84-0.94) and 0.85 (95% CI 0.72-0.95) in the non-old VCFs and old VCFs groups, respectively, in the BMUH cohort (p = 0.051).

Conclusion: In present study, we developed the DRN model for automated diagnosis and identification of fresh VCFs from spinal DR images. The DRN model can provide interpretable attention maps to support the excellent prediction results, which is the key that most clinicians care about when using the model to assist decision-making.

Keywords: compression; deep learning; fractures; radiography; spine.

FIGURE 1

Study flowchart.

FIGURE 2

Attention heatmaps of different models for representative participants. T1-weighted imaging (T1WI), T2WI, FS T2WI, DR image, and attention heatmaps of different models of participants who were predicted successfully in (A) fresh VCFs and (B) non-fresh VCFs group. The fresh VCFs are pointed by the red boxes.

FIGURE 3

Attention heatmaps of the DRN model in the external test cohorts. The image on the left is the FS T2WI; The image in the middle is corresponding DR image; the image on the right is the corresponding attention heatmap of the DRN model. The fresh VCFs are pointed by the red boxes.

FIGURE 4

Performance of the DRN model in the validation and external test cohorts. ROC curves and confusion matrix of DRN model in (A) the validation cohort, (B) the YHDH cohort and (C) the BMUH cohort.

FIGURE 5

Performance of other models in external test cohorts. ROC curves of ResNet-50, Shufflenet-v2, EfficientnetV2-S, EfficientnetV2-M and EfficientnetV2-L models in (A) (the first line) YHDH cohort and (B) (the second line) BMUH cohort.

FIGURE 6

Performance of the DRN model in the subgroups of the external test cohorts. ROC curves and confusion matrix of the DRN model in (A) the non-old VCFs group and (B) the old VCFs group of the YHDH cohort. ROC curves and confusion matrix of the DRN model in (C) the non-old VCFs group and (D) the old VCFs group of the BMUH cohort.

FIGURE 7

Attention heatmaps of the DRN model in the old VCFs group of the external test cohorts. The image on the left is the DR image of participants without fresh VCFs who were predicted successfully; the image on the right is the corresponding attention heatmap of the DRN model. The old VCFs are pointed by the yellow boxes.