# Reduction of Video Capsule Endoscopy Reading Times Using Deep Learning with Small Data
## 4 Discussion  
The goal of our study was to reduce the physician time needed to read VCE studies by excluding frames classified as normal without sacrificing diagnostic accuracy. Our study used a robust convolutional neural network (CNN) that was trained and tested to classify a frame as normal or abnormal, with the goal of minimizing the number of false negatives. As such, using our CNN model, the physician can confidently exclude normal frames and carefully analyze abnormal frames to ensure accurate diagnosis. Using the method proposed in our study, we were able to reduce the video length on average by 47%. By doing this, our model was able to remove frames confidently classified as normal while accurately classifying frames within abnormal segments. Using this model, we captured frames from all 119 of the abnormal segments, for an accuracy of 100%. We would note that KID video 2 is of lesser quality than the two other videos in the database. Our approach has the promise of significantly reducing the reading time needed by a physician to review VCE images. This study contributes to the growing field of CNN in the detection of subtle \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Video Name** & **Total Frames** & \begin{tabular}{c} **Number of** \\ **Abnormal** \\ **Segments** \\ \end{tabular} & \
\begin{tabular}{c} **Reduced** \\ **Number of** \\ **Frames** \\ \end{tabular} &
\begin{tabular}{c} **Abnormal** \\ **Segments** \\ \end{tabular} \\ \hline KID Video 1 & 28,480 (95 min) & 22 & 14,828 (49 min) & 22 \\ KID Video 2 & 117,565 (391 min) & 86 & 84,672 (282 min) & 86 \\ KID Video 3 & 74,762 (249 min) & 11 & 27,099 (90 min) & 11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ensemble of Networks Trained on Augmented Videos. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **n** & **Mean** & **Variance** & **t-calc** & **t-crit** & **df** & \(\mathbf{p}\) \\ \hline Ensemble No Aug & 119 & 0.714 & 0.206 & 6.87 & 1.98 & 118 & \(3.21\times 10^{-10}\) \\ Ensemble With Aug & 119 & 1 & 0 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Paired \(t\)-test Results Comparing Accuracy of Ensemble without Augmentation vs. Ensemble with Augmentation. lesions in the small bowel by demonstrating the ability to perform well with small training data. This is important due to the cost associated with collecting and annotating large amounts of medical data. Similar studies have efficiently detected pathology in VCE in shorter time periods. As described in [35], the authors attempted to use AI to improve the performance of VCE using machine learning called Computer-Assisted Diagnosis for CAPsule Endoscopy (CAD-CAP). CAD-CAP had a sensitivity of 100% in the detection of angicastasiasi; however, this was done with clean and still images. Our study had the benefit of having the physician review all the abnormal frames, thus ensuring an accurate diagnosis. As described in [36], a custom CNN was trained and evaluated using a total of 5825 WCE videos and was able to detect 95.9% of abnormal findings. In [37], capsule endoscopy data was used to train a CNN to find protruding lesions in the small bowel. Using 30,000 images from 292 subjects, they obtained 0.91 AUC with a sensitivity of 90.7%. In [38], the authors obtained a sensitivity of 88.2% using a CNN trained on 5360 images when looking for erosions and ulcerations in the small bowel. In [39], the authors were able to train a CNN to classify abnormal pathology from normal pathology with sensitivities and specificity as high as 99.88% and 99.09% compared with physician reads that had sensitivities and specificity of 74.57% and 76.89%. Their method reduces reading times on average from 96.6 min to 5.9 min. However, their study involved the use of around 5000 videos collected and annotated by 21 physicians in China with unknown specialties, experience, and skill. Our study has some limitations which offer opportunity for improvement.