P198 Practical deep learning tool for the scoring of ulcerative colitis disease activity in central reading. (27th May 2021) - Byrne, M; East, J; Iacucci, M; Travis, S; Kalapala, R; Duvvur, N; Rughwani, H; Singh, A; Berry, S; Monsurate, R; Soudan, F; Laage, G; et al.
Abstract: Background: Central Reading Org.& the pharma industry employ subject matter experts(SMEs)to score videos from sites participating in clinical trials for Ulcerative Colitis(UC).As we are developing Artificial Intelligence(AI)models for scoring purposes, we need to build a new software interface that can incorporate these AI models to aid SMEs, making their determination of the scores for each segment of the video & for the video as a whole.We propose a system that reduces the time for SMEs to review & score videos, improving the accuracy of scoring, with the help of our AI models. Methods: We built a web-based interface supported by our AI models which can read, write multiple databases & data stores to read & display videos to be scored by a central reader, as well as the associated metadata required to improve the process.User interface shows a timeline with markers for the segments of the colon, with sections that are blurry, poorly prepped, or unscorable highlighted in different colours.While we could also highlight sections of the video with the precise score assigned to it by the AI, this would bias the central reader's opinion.We hide the precise score generated by our AI models & instead display 3 colours for low, medium or high disease activity.When a video is loaded to be read by the user, the playback marker is set to the first high disease activity section based on known medical indexes such as the Mayo Endoscopic Subscore(MES) & UCEIS(Ulcerative Colitis Endoscopic Index of Severity), usually consists of a few seconds of video & that video is played back continuously in a loop until the reader selects the appropriate score for that section.When the reader saves the section, the software immediately moves the video cursor to the highest scored section of the video.That way the central reader can review only the relevant portions of the video to confirm the score assigned to each segment.If the central reader's scores do not align well with the AI scores then the software continues to show more sections of the video to the user, including sections it may have labeled as unscorable, that may be scorable. Results: The review of the system by 3 key opinion leaders, user experience was positive.Not only does the system allow the reader's attention to be more efficiently used, but the interface allows both AI & central reader scores to be saved, allowing for the latter to be used iteratively to re-train & improve the underlying AI model(s).Our tool was also used by a gastroenterologist specialist in order to perform video quality assessment & colon sections scoring. Conclusion: We developed an AI tool that can be used to improve the efficiency & accuracy of the central reading process in clinical trials for UC.Further work is ongoing to improve the interface.