Department of Urology, Stanford University School of Medicine, Stanford, CA, USA.
Department of Urology, Stanford University School of Medicine, 453 Quarry Road, Mail Code 5656, Palo Alto, CA, 94304, USA.
J Med Syst. 2022 Oct 3;46(11):73. doi: 10.1007/s10916-022-01862-8.
Processing full-length cystoscopy videos is challenging for documentation and research purposes. We therefore designed a surgeon-guided framework to extract short video clips with bladder lesions for more efficient content navigation and extraction. Screenshots of bladder lesions were captured during transurethral resection of bladder tumor, then manually labeled according to case identification, date, lesion location, imaging modality, and pathology. The framework used the screenshot to search for and extract a corresponding 10-seconds video clip. Each video clip included a one-second space holder with a QR barcode informing the video content. The success of the framework was measured by the secondary use of these short clips and the reduction of storage volume required for video materials. From 86 cases, the framework successfully generated 249 video clips from 230 screenshots, with 14 erroneous video clips from 8 screenshots excluded. The HIPPA-compliant barcodes provided information of video contents with a 100% data completeness. A web-based educational gallery was curated with various diagnostic categories and annotated frame sequences. Compared with the unedited videos, the informative short video clips reduced the storage volume by 99.5%. In conclusion, our framework expedites the generation of visual contents with surgeon's instruction for cystoscopy and potential incorporation of video data towards applications including clinical documentation, education, and research.
处理全膀胱镜检查视频对于文档记录和研究目的来说具有挑战性。因此,我们设计了一个由外科医生指导的框架,用于提取带有膀胱病变的短视频片段,以实现更高效的内容导航和提取。在经尿道膀胱肿瘤切除术期间拍摄膀胱病变的截图,然后根据病例标识、日期、病变位置、成像方式和病理学进行手动标记。该框架使用截图搜索并提取相应的 10 秒钟视频片段。每个视频片段都包含一个 1 秒钟的占位符,其中包含一个 QR 条形码,告知视频内容。该框架的成功通过这些短视频片段的二次使用以及视频材料所需存储量的减少来衡量。从 86 个病例中,该框架成功地从 230 个截图中生成了 249 个视频片段,其中有 8 个截图的 14 个错误视频片段被排除在外。符合 HIPAA 标准的条形码提供了带有 100%数据完整性的视频内容信息。创建了一个基于网络的教育画廊,其中包含各种诊断类别和带注释的帧序列。与未经编辑的视频相比,信息丰富的短视频片段将存储量减少了 99.5%。总之,我们的框架加快了带有外科医生指导的膀胱镜检查视觉内容的生成速度,并有可能将视频数据纳入临床文档记录、教育和研究等应用中。