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.

描述膀胱镜检查检测和诊断中人类决策的过程，以优化膀胱癌风险分析的人工智能解决方案。

• 批准号: 520382567
• 负责人: Dr. Shane O' Sullivan, Ph.D.
• 金额: --
• 依托单位: Klinik für Urologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/520382567?language=en
• 关键词: Characterising; procedure; human; decision; making

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).

膀胱癌 (BCa) 是全球第十大常见癌症，2018 年估计有 549,000 例新发病例和 200,000 例死亡。膀胱镜检查是诊断 BCa 的第一步，也是最重要的一步。仅在美国每年就进行约一百万次膀胱镜检查。自德国泌尿科医生 Maximilian Nitze 于 130 多年前推出以来，它在患者舒适度和诊断准确性方面不断提高。然而，尽管有指南，膀胱镜检查结果却多种多样，并且通常难以分类。目前膀胱镜诊断中假阴性/阳性的程度尚不清楚。我们怀疑存在一定程度的诊断不足（未能检测出恶性肿瘤）和过度诊断（将患者送去进行不必要的经尿道膀胱肿瘤切除术 [TUR-BTs]/麻醉活检），使患者处于危险之中。我们的假设是，泌尿科医生拥有关于潜在恶性肿瘤的隐性（隐性）知识，可以通过分析他们的眼球追踪和膀胱镜检查反馈数据来明确这些知识。然后，隐式和显式知识可用于构建优化人工智能算法的训练数据集。缺乏以下方面的文献： i) 膀胱镜检查检测和诊断中的人类决策； ii) 膀胱病变的标准化文件和描述； iii) 与机器学习相关的眼球追踪工作。为了填补文献空白，该项目建议进行一项研究，使用眼动仪来：1）跟踪泌尿科专家在 TUR 期间进行实时膀胱镜检查时所看到的内容； 2) 跟踪独立泌尿科顾问在审查泌尿科专家进行的膀胱镜检查的 DVD 记录时所查看的内容。为了克服眼球追踪的局限性，实验将涉及一个迭代的多阶段过程，由泌尿科专家通过特定形式的眼球追踪进行输入，这些眼球追踪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查明病变并记录他们对所看到的内容及其含义的口头描述）。当泌尿科医生分析膀胱时，他们的计划是利用眼球追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，帮助泌尿科医生进行膀胱镜检查和诊断。这两个目标是： a) 整理膀胱镜检查和实验数据； b) 与泌尿科医师合作来描述决策程序的特征。该项目的研究结果将支持我们的合作者联盟优化其人工智能软件。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。这提高了患者的安全性。我们为开发其他内窥镜检查（例如结肠镜检查）的人工智能解决方案奠定了基础。