Characterising the procedure of human decision-making in cystoscopy detection and diagnosis for the purpose of optimising AI solutions for risk profiling of bladder cancer.

膀胱癌(BCA)是全球第十大最常见的癌症形式，2018年估计有54.9万新病例和20万人死亡。膀胱镜检查是诊断BCA的第一步，也是最重要的一步。仅在美国，每年就有大约一百万例膀胱镜检查。自130多年前由德国泌尿科医生马克西米利安·尼策推出以来，它在患者舒适性和诊断准确性方面不断提高。然而，尽管有指南，膀胱镜检查的结果是多样的，往往具有分类的挑战性。目前，膀胱镜检查诊断的假阴性/假阳性的程度尚不清楚。我们怀疑存在一定程度的诊断不足(未能发现恶性肿瘤)和过度诊断(将患者送往不必要的经尿道膀胱肿瘤电切术[TUR-BTS]/麻醉下活检)，从而将患者置于危险之中。我们的假设是，泌尿科医生拥有关于潜在恶性肿瘤的隐性(默示)知识，这些知识可以通过分析他们与膀胱镜检查相关的眼球跟踪和反馈数据而显性地表现出来。然后，隐式和显式知识可以用于构建训练数据集，以优化人工智能算法。缺乏关于以下方面的文献：i)膀胱镜检查和诊断中的人类决策；ii)膀胱病变的标准化文档和描述；iii)与机器学习相关的眼睛跟踪工作。为了填补文献中的空白，该项目建议进行一项研究，使用眼睛跟踪器来：1)跟踪泌尿科专家在TURS期间进行实时膀胱镜检查时正在观看的内容；以及2)跟踪独立的泌尿科顾问正在观看的内容，同时回顾由专业泌尿科医生进行的膀胱镜检查的DVD记录。为了克服眼睛追踪的局限性，实验将涉及一个迭代的多阶段过程，由专业泌尿科医生通过特定形式的眼睛追踪输入，依赖于精确定位的语音记录(例如，泌尿科医生使用数字笔立即精确定位病变，并记录他们对所见内容及其意义的口头描述)。在泌尿科医生分析膀胱的同时，他们的计划是使用眼球跟踪来揭示他们的决策模式。我们的中心目标是通过使用人类决策模式的数据集训练人工智能来优化人工智能解决方案，以帮助泌尿科医生进行膀胱镜检测和诊断。两个目标是：a)管理膀胱镜检查和实验数据；b)与泌尿科医生合作，确定决策过程的特征。这个项目的发现将支持我们的合作者财团优化他们的人工智能软件。该软件为膀胱镜检查提供专业的第二意见，以便更准确地预测和减少BCA的复发/进展，从而避免不必要的TUR/活检。这增加了病人的安全。我们为开发其他内窥镜(例如结肠镜检查)的人工智能解决方案奠定了基础。

Bladder cancer (BCa) is the 10th most common form of cancer worldwide, with an estimated 549,000 new cases and 200,000 deaths in 2018. Cystoscopy is the first and most important step in the diagnosis of BCa. About a million cystoscopies are performed every year in the USA alone. Since its introduction by German urologist Maximilian Nitze over 130 years ago, it has improved continuously regarding patient comfort and diagnostic accuracy. However, despite having guidelines, cystoscopic findings are diverse and often challenging to classify. The extent of the false negatives/positives in cystoscopy diagnosis is currently unknown. We suspect that there is a degree of under-diagnosis (failure to detect malignant tumors) and over-diagnosis (sending the patient for an unnecessary transurethral resection of bladder tumors [TUR-BTs]/biopsy with anesthesia) that put the patient at risk. Our hypothesis is that urologists possess implicit (tacit) knowledge about potential malignancy that can be made explicit by analyzing their eye-tracking and feedback data with respect to cystoscopy. The implicit and explicit knowledge can then be used in building training datasets for optimizing AI algorithms. There is a lack of literature on: i) human decision- making in cystoscopy detection and diagnosis; ii) standardized documentation and descriptions for bladder lesions; iii) eye-tracking work related to machine learning. To fill the gaps in the literature, this project proposes to conduct a study that uses an eye-tracker to: 1) track what an expert urologist is viewing whilst performing live cystoscopy during TURs; and 2) track what independent urology consultants are viewing whilst reviewing DVD recordings of the cystoscopy performed by the expert urologist. To overcome the limitations of eye-tracking, the experimentation will involve an iterative multi-stage process with inputs from expert urologists by specified forms of eye-tracking that rely on voice-recording with pinpointing (e.g. urologists use a digital pen to immediately pinpoint the lesion and record their verbalized descriptions about what they view and its meaning). While the urologists are analyzing the bladder, the plan is to use eye-tracking to uncover their decision patterns. Our central goal is to optimize AI solutions that assist urologists in cystoscopy detection and diagnosis by training the AI with datasets of human decision patterns. The two objectives are: a) Curate the cystoscopy and experimental data; b) Collaborate with urologists to characterize the decision-making procedure. The findings from this project will support our collaborators’ consortium in the optimization of their AI software. The software provides a professional second opinion for cystoscopy in order to more accurately predict and reduce recurrence/progression of BCa, and thus avoid unnecessary TURs/biopsies. This increases patient safety. We lay the groundwork to develop AI solutions for other endoscopies (e.g. colonoscopy).