## 4 Discussion  
In clinical practice, endoscopists must perform visual inspection of all detected colorectal polyps. Experience and expertise are factors that currently dictate decision-making during real-time endoscopy. In this study, we have evaluated a CADx system against expert and novice endoscopists. Firstly, in Table 1, a noticeable difference between novices and experts is observed for both intuition and the BASIC classification. Experts showed a higher diagnostic accuracy compared to novices (79.5% vs. 66.7%, \(p=0.005\)) and (81.7% vs. 66.5%, \(p=0.002\)), respectively. Both groups improved the diagnosis of pre-malignant polyps during the second round of assessments, which positively reflects on the training with the BASIC classification received after the first stage of the study. Following the first point, our CADx system showed an overall better performance than both novices and experts in all of the trained architectures. All our trained models contained information of three different hospitals with WL, BLI, and LCI modalities, with a total of 2739 polyp images. Compared to the endoscopists, our training stage utilized the benefits of deep learning, which allowed us to build a robust diagnostic tool. Our final CADx system (EfficientNetB4) correctly assessed a total of 57 out of 60 polyps. The CADx system erroneously classified only three polyps: two adenomas and one hyperplastic polyp. Table 3 presents the individual contribution of each modality to the overall test set. In this study, the performance of the endoscopists was evaluated on the knowledge of WL and BLI. Moreover, the BASIC classification only takes into account the latter modality, hence, comparison with all combined modalities may lead to unreliable one-to-one comparison. Solely observing BLI, our CADx system outperforms experts and novices for both intuition and BASIC. In contrast, WL and LCI do not outperform both groups on classifying enough HP polyps. On the one hand, WL offers limited visualization and enhancement, which makes it difficult to identify small benign polyps; while on the other hand, LCI is not the most common modality in current clinical practice. This is reflected in our training dataset, which contained far more WL and BLI images than LCI data. Lastly, when comparing each single modality with the combined results, we did not find a statistically significant improvement compared to combining modalities. This might indicate that for a further study, the sample size for the test set should be increased. In Figure 3, we showcase the strength of our CADx system, where three predictions are performed--one for each modality--to obtain a final prediction based on the three observations. In the work of [PERSON] et al. (2018), the authors proposed a voting system from the predictions of each combination. Although the voting system would be suitable, we prefer to adopt the same methodology as our previous study ([PERSON] et al., 2018), where the posterior probabilities acquired for each modality are subsequently averaged to compute one unique probability per polyp. In addition, Grad-CAM provides the endoscopists with visual information on the decision region. This has two benefits, one is to provide the endoscopists with visual feedback on the CADx judgment, and the second is to offer an alternative method for tracking polyps (either benign or pre-malignant) during real-time video endoscopy. Our CADx system excels in cases where not all modalities are predicted as correct, such as in the example of Figure 4, where a pre-malignant polyp is incorrectly predicted in the WL modality. Furthermore, a visual informative inspection is supported by the results of Grad-CAM