3. Results
3.1 Clinical Characteristics of the Study Population
After screening, we enrolled 878 eligible patients in the training (n = 702) and validation (n = 176) cohorts of the classification model. There were no significant between-cohort differences in the general information.
3.2 Results of the Semantic Segmentation Models
After training for 20 epochs, the performance of U-net and Deeplabv3 quickly stabilized; moreover, they had Dice coefficients of 0.953 and 0.961, respectively (Figure 4). Deeplabv3 performed slightly better than U-net. We used the Deeplabv3 network trained to segment the sinus region in CT images.
3.3 Performance of the Classification Models
When trained using a single image as a unit, the areas under the curve (AUCs) of the efficientnet_b0, resnet50, inception_resnet_v2, and Xception networks were 0.84, 0.86, 0.83, and 0.86, respectively. When trained using each patient as a unit, the AUCs of the four neural networks increased to 0.89, 0.90, 0.88, and 0.90, respectively (Figure 5). The accuracy of the training process reflects the overall classification performance of the DL models (Figure: 6).
We incorporated a confusion matrix for class-wise comparison to evaluate whether the model could reliably detect and classify objects. As shown in Figures 7 and 8, the specificity and sensitivity of the four networks were higher when using each patient, rather than single images, as units.
The presentation of Grad-Cams allows elucidation of how the DL network captured image features for prediction and resolves doubts regarding the network’s ability to learn in the appropriate direction. Yellow areas shown in the Grad-Cams had the strongest correlation with the classification. For patients with ECRS, the yellow areas represent characteristics associated with an increased risk of ECRS. The efficientnet_b0 network was used as an example. Figures 9, 10, 11, and 12 show Grad-Cams for both the bone window and soft tissue window images of the patients. For both diseases, yellow areas were concentrated in areas with lesions, which is consistent with our medical experience.