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Intraoperative integration of artificial intelligence during cystoscopic surgery

膀胱镜手术中人工智能的术中整合

基本信息

批准号：
10365872
负责人：
JOSEPH C LIAO
金额：
$ 51.4万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-01 至 2026-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10365872
关键词：
Affect Algorithms Appearance Artificial Intelligence Base Sequence Benign Bladder Bladder Neoplasm Cancer Detection Cancer Diagnostics Cancer Patient Cancerous Cessation of life Clinic Clinical Communities Complex Computer Vision Systems Cystoscopy Data Data Set Detection Diagnosis Diagnostic Ensure Environment Equipment Excision Future Goals Healthcare Histologic Hospitals Human Image Image Analysis Inflammatory Intervention Knowledge Learning Lesion Light Malignant Neoplasms Malignant neoplasm of urinary bladder Medical Imaging Medical center Modeling Morbidity - disease rate Morphology Operating Rooms Operative Surgical Procedures Outcome Papillary Pathologic Patients Performance Physician Assistants Predictive Value Process Protocols documentation Provider Recurrence Research Role Sensitivity and Specificity Site Staging Standardization Surgeon Technology Testing The Cancer Imaging Archive Time Training Translating Transurethral Resection United States Universities Urologist Urology Validation Washington Work annotation system augmented intelligence automated segmentation base body system cancer diagnosis cancer imaging cancer recurrence cancer risk cancer surgery cloud based convolutional neural network cost effective deep learning algorithm deep neural network demographics design experience high risk image guided image processing improved improved outcome indexing millisecond mortality multidisciplinary novel patient stratification prospective recruit recurrent neural network relating to nervous system risk stratification screening segmentation algorithm tool tumor tumor progression

项目摘要

PROJECT SUMMARY Bladder cancer is the sixth most common cancer in the U.S., has one of the highest recurrence rates of all cancers, and is the most expensive cancer to treat from diagnosis to death. Current standard for bladder cancer diagnosis relies on clinic-based white light cystoscopy for initial screening, followed by transurethral resection of bladder tumor in the operating room for pathologic diagnosis and local staging. White light cystoscopy has several well recognized shortcomings, particularly incomplete detection, thereby leading to suboptimal resection and contributing to cancer recurrence and progression. Our goal is to improve outcomes for bladder cancer patients through integration of a deep learning algorithm to improve cystoscopic detection and enhance surgical resection. Artificial intelligence (AI)-based on deep neural networks have demonstrated remarkable capacity to learn complex relationships and incorporate existing knowledge into the inference model. We hypothesize that AI- augmented detection of bladder tumor will improve diagnostic cystoscopy in the clinic setting to identify suspicious lesions and improve the quality of transurethral resection in the operating room, thereby reducing overall cancer recurrence and outcome. Towards the goal of establishing a paradigm of AI-based framework for augmented detection of bladder cancer, we will leverage our strong preliminary data and outstanding environment in AI research. We propose three specific aims: 1) To curate a high-quality annotated cystoscopy imaging dataset to optimize deep neural network CystoNet; 2) To design and optimize CystoNet for real-time cystoscopic navigation and cancer detection; and 3) To conduct a prospective multicenter validation of CystoNet during bladder cancer surgery. Successful completion of the studies proposed here will serve to translate deep learning algorithm to the dynamic environment of cystoscopic surgery without the need for specialized instrumentaitons. We foresee our approach will improve the outcome of a major cancer and genearlizable to other organ systems amenable for endsocopic interventions.

项目总结