Methods and Systems for Cystoscopic Imaging Incorporating Machine Learning

    公开(公告)号:US20220160208A1

    公开(公告)日:2022-05-26

    申请号:US17601377

    申请日:2020-04-03

    摘要: Over 2 million cystoscopies are performed annually in the United States and Europe for detection and surveillance of bladder cancer. Adequate identification of suspicious lesions is critical to minimizing recurrence and progression rates, however standard cystoscopy misses up to 20% of bladder cancer. Access to adjunct imaging technology may be limited by cost and availability of experienced personnel. Machine learning holds the potential to enhance medical decision-making in cancer detection and imaging. Various embodiments described herein are directed to methods for identifying cancers, tumors, and/or other abnormalities present in a person's bladder. Additional embodiments are directed to machine learning systems to identify cancers, tumors, and/or other abnormalities present in a person's bladder, while additional embodiments will also identify benign or native structures or features in a person's bladder. Further embodiments incorporate such systems into cystoscopy equipment to allow for real time and/or immediate detection of cancers, tumors, and/or other abnormalities present in a person's bladder during a cystoscopy procedure.