Marchese Aizenman Gabriel, Salvagnini Pietro, Cherubini Andrea, Biffi Carlo
Cosmo Intelligent Medical Devices, Dublin, Ireland.
Milan Center for Neuroscience, University of Milano-Bicocca, Milano, Italy.
Front Oncol. 2024 Aug 1;14:1422942. doi: 10.3389/fonc.2024.1422942. eCollection 2024.
Ensuring accurate polyp detection during colonoscopy is essential for preventing colorectal cancer (CRC). Recent advances in deep learning-based computer-aided detection (CADe) systems have shown promise in enhancing endoscopists' performances. Effective CADe systems must achieve high polyp detection rates from the initial seconds of polyp appearance while maintaining low false positive (FP) detection rates throughout the procedure.
We integrated four open-access datasets into a unified platform containing over 340,000 images from various centers, including 380 annotated polyps, with distinct data splits for comprehensive model development and benchmarking. The REAL-Colon dataset, comprising 60 full-procedure colonoscopy videos from six centers, is used as the fifth dataset of the platform to simulate clinical conditions for model evaluation on unseen center data. Performance assessment includes traditional object detection metrics and new metrics that better meet clinical needs. Specifically, by defining detection events as sequences of consecutive detections, we compute per-polyp recall at early detection stages and average per-patient FPs, enabling the generation of Free-Response Receiver Operating Characteristic (FROC) curves.
Using YOLOv7, we trained and tested several models across the proposed data splits, showcasing the robustness of our open-access platform for CADe system development and benchmarking. The introduction of new metrics allows for the optimization of CADe operational parameters based on clinically relevant criteria, such as per-patient FPs and early polyp detection. Our findings also reveal that omitting full-procedure videos leads to non-realistic assessments and that detecting small polyp bounding boxes poses the greatest challenge.
This study demonstrates how newly available open-access data supports ongoing research progress in environments that closely mimic clinical settings. The introduced metrics and FROC curves illustrate CADe clinical efficacy and can aid in tuning CADe hyperparameters.
在结肠镜检查期间确保准确检测息肉对于预防结直肠癌（CRC）至关重要。基于深度学习的计算机辅助检测（CADe）系统的最新进展已显示出提高内镜医师表现的潜力。有效的CADe系统必须在息肉出现的最初几秒内实现高息肉检测率，同时在整个过程中保持低假阳性（FP）检测率。
我们将四个开放获取的数据集整合到一个统一平台，该平台包含来自各个中心的超过340,000张图像，其中包括380个标注的息肉，并进行了不同的数据划分，以用于全面的模型开发和基准测试。包含来自六个中心的60个全流程结肠镜检查视频的REAL-Colon数据集被用作该平台的第五个数据集，以模拟临床情况，用于对未知中心数据进行模型评估。性能评估包括传统的目标检测指标和更符合临床需求的新指标。具体而言，通过将检测事件定义为连续检测的序列，我们计算早期检测阶段的每个息肉召回率和每位患者的平均FP数，从而生成自由响应接收器操作特征（FROC）曲线。
使用YOLOv7，我们在所提出的数据划分上训练和测试了多个模型，展示了我们用于CADe系统开发和基准测试的开放获取平台的稳健性。新指标的引入允许根据临床相关标准（如每位患者的FP数和早期息肉检测）优化CADe操作参数。我们的研究结果还表明，省略全流程视频会导致不现实的评估，并且检测小息肉边界框是最大的挑战。
本研究展示了新可用的开放获取数据如何在紧密模拟临床环境的情况下支持正在进行的研究进展。引入的指标和FROC曲线说明了CADe的临床疗效，并有助于调整CADe超参数。
