Intraoperative integration of artificial intelligence during cystoscopic surgery

项目总结 膀胱癌是美国第六大常见癌症，是复发率最高的癌症之一 癌症，是从确诊到死亡治疗费用最高的癌症。现行膀胱标准 癌症的诊断依赖于以临床为基础的白光膀胱镜进行初步筛查，然后经尿路 在手术室切除膀胱肿瘤进行病理诊断和局部分期。白光 膀胱镜检查有几个公认的缺点，特别是不完全检测，从而导致 次最佳切除，并导致癌症复发和进展。我们的目标是改善结果 针对膀胱癌患者通过融合深度学习算法改进膀胱镜检测 并加强手术切除。 基于深度神经网络的人工智能(AI)已经显示出非凡的学习能力 复杂的关系，并将现有知识纳入推理模型。我们假设人工智能- 加强对膀胱肿瘤的检测将在临床环境中改进诊断膀胱镜以识别 提高手术室经尿道电切术的质量，从而减少可疑病变的发生 总的癌症复发和转归。朝着建立基于人工智能的框架范式的目标迈进 为了加强对膀胱癌的检测，我们将利用我们强大的初步数据和出色的 人工智能研究中的环境。我们提出了三个具体目标：1)策划一个高质量的带注释的膀胱镜检查 成像数据集优化深度神经网络CystoNet；2)实时设计和优化CystoNet 膀胱镜导航和癌症检测；以及3)进行前瞻性多中心验证 膀胱癌手术中的CystoNet。 本文提出的研究的成功完成将有助于将深度学习算法转化为 动态的膀胱镜手术环境，不需要专门的仪器。我们预见到 我们的方法将改善一种主要癌症的预后，并可使其他器官系统获得基因组。 用于内窥镜介入治疗。

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.