## 4 Discussion
In contrast to earlier work which used WLE imagery [10], one drawback of this study is the relatively small amount of unique patients that are used for training and testing. While the deep learning algorithm is exposed to a large amount of single frames, the correlation between sequential frames is very high, since the videos are focused on a single area. For this reason, the practical benefit of using all video frames is limited and only 5 frames are extracted per second instead of the 25 that are available. Additionally, different videos from the same patient are subject to intra-patient correlation as well. While each video is recorded at a unique site, intra-patient correlation can still lead to reduced performance. However, we corrected for this when analyzing the NBI zoom videos, to reduce the intra-patient bias. A second drawback of the current study is that the collection of the zoom videos was performed in multiple hospitals, by different endoscopists and endoscopes, which results in varying levels of image quality and levels of magnification. Normally, this would help generalization over larger populations. However, due to the limited sample size of this pilot study, the algorithm may not be robust against this background noise, which is a clear constraint of the current acquisition approach. Dysplasia in BE is relatively rare which makes data acquisition more difficult, so that simply collecting more data is not a viable short-term solution. Finally, upon review, decreased video quality such as blurring, specular reflections and lens movement was observed in many of the incorrectly classified videos. For example, in Figure 2(b), the likelihood of dysplasia is plotted as a function of time. There is a pronounced dip in the confidence of the algorithm in the first half of the segment. This coincides with lens movement and blurring in this specific video. This indicates that artifacts such as movement, blurring and specular reflections pose a time-varying problem when classifying NBI-zoom videos. In future work, efficacy of the algorithm can be improved by excluding non-informative frames prior to classifying an NBI-zoom video. Other work has shown that removal of non-informative frames is beneficial for classification performance of videos using white light endoscopy [24]. The artifacts that cause non-informative frames are less prevalent in zoom video compared to standard endoscopies, since the entire segment is focused on a single site in the NBI-zoom case. However, analysis from this study has shown that performance also decreases in some cases due to the occurrence of non-informative frames in the video. As alternative future work, more extensive experiments should be performed with additional problems and data sets related to the gastrointestinal tract, such as for example: angiodysplasia detection with pill-cam imagery, polyp detection and classification using colonoscopy imagery, and stomach cancer classification using white light endoscopy. Together with a prior study [15], two separate experimental setups have indicated that endoscopy-driven pretraining is beneficial for BE CAD problems. Future work could indicate whether this finding holds for other gastrointestinal diseases as well.