MP60-18 EFFICIENT AUGMENTED INTELLIGENCE STRATEGY WITH POTENTIAL USE FOR REAL-TIME BLADDER TUMOR DETECTION

Abstract

You have accessJournal of UrologyCME1 Apr 2023MP60-18 EFFICIENT AUGMENTED INTELLIGENCE STRATEGY WITH POTENTIAL USE FOR REAL-TIME BLADDER TUMOR DETECTION Okyaz Eminaga, Timothy Lee, Mark Laurie, Jessie Ge, Vinh LA, Jin Long, Eugene Shkolyar, Xiao Jia, Axel Semjonow, Martin Bogemann, Hubert Lau, Lei Xing, and Joseph Liao Okyaz EminagaOkyaz Eminaga More articles by this author , Timothy LeeTimothy Lee More articles by this author , Mark LaurieMark Laurie More articles by this author , Jessie GeJessie Ge More articles by this author , Vinh LAVinh LA More articles by this author , Jin LongJin Long More articles by this author , Eugene ShkolyarEugene Shkolyar More articles by this author , Xiao JiaXiao Jia More articles by this author , Axel SemjonowAxel Semjonow More articles by this author , Martin BogemannMartin Bogemann More articles by this author , Hubert LauHubert Lau More articles by this author , Lei XingLei Xing More articles by this author , and Joseph LiaoJoseph Liao More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003318.18AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Development of intelligence systems for bladder tumor detection is cost- and labor-intensive. Specifically, image annotation is one of the most expensive tasks in the development of intelligence systems. Moreover, previous studies utilized selective screenshots or frame sequences for model development and validation, despite that cystoscopy is a dynamic visual inspection impacted by random noises. The current work proposes an efficient strategy to develop augmented intelligence strategy ready for real-time bladder tumor detection as computer-aided assistance tool for clinicians. METHODS: We used a previously published educational cystoscopy atlas (n=312 images) and our deep learning models (ConvNeXt, PlexusNet, MobileNet, SwinTransformer) covering a variety of model complexity and computation efficacy to estimate the ratio between cancer and normal confidence scores; We applied an image augmentation strategy called RandAugment to populate the training set for model training and externally validated on video records for the initial diagnostic cystoscopy prior to TURBT from 68 cases with benign and bladder cancers tumors (i.e., region of interest, ROI) at a single center. Each frame of the video was labeled by ROI status. ROI was confirmed by pathology examination and the Delphi method. The ROI status was predicted based on the ratio (if the ratio>1, then the frame is positive for ROI, otherwise negative). On external validation, areas of adequate illumination in video frames were considered as input for models. The performance measurement included specificity and sensitivity at frame level, frame sequence (block) level, and ROI level for each case. The block level split each full-length video into small segments according to the ROI status. RESULTS: Specificity was statistically comparable between four models at frame (range: 30.0–44.8%) and block levels (56–67%) by p>0.05. While sensitivity at frame level (range: 81.4 – 88.1) statistically differed between the models, sensitivity at block level (100%) and ROI level (100%) were comparable between these models, indicating that all ROI are detectable by these models and that the frame-level model performance is impacted by the random noises. MobileNet and PlexusNet were computationally more efficient (22 and 19 frames per second, retrospectively) and suitable for real-time detection task than ConvNeXt and SwinTransformer (13 and 17 frames per second, retrospectively). CONCLUSIONS: Educational cystoscopy atlas and cost-effective model development strategy facilitates the development of accurate and efficient intelligence systems with potential use for real-time bladder tumor detection. Source of Funding: The work was supported by National Institutes of Health R01 CA260426 to JCL. © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e850 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Okyaz Eminaga More articles by this author Timothy Lee More articles by this author Mark Laurie More articles by this author Jessie Ge More articles by this author Vinh LA More articles by this author Jin Long More articles by this author Eugene Shkolyar More articles by this author Xiao Jia More articles by this author Axel Semjonow More articles by this author Martin Bogemann More articles by this author Hubert Lau More articles by this author Lei Xing More articles by this author Joseph Liao More articles by this author Expand All Advertisement PDF downloadLoading ...