Background/Objectives: Artificial intelligence (AI) algorithms for diagnosing colorectal polyps are emerging but not yet widely used. Trust in AI is lacking and could be improved by visually explainable AI, such as heatmaps. This study aims to investigate the association between heatmap position and AI accuracy for the endoscopic characterization of colorectal polyps. Methods: Four AI algorithms diagnosed 2133 prospectively collected images of 376 colorectal polyps from two hospitals, using histopathology as the gold standard. Heatmap position was compared to the human-annotated polyp position. Generalized estimating equations were used to assess the association between heatmap position and a correct AI diagnosis. Results: Higher percentages of heatmap covering the colorectal polyp were associated with correct diagnoses in all four algorithms (OR 1.013 [95% CI 1.006–1.019], OR 1.025 [95% CI 1.011–1.039], OR 1.038 [95% CI 1.024–1.053], and OR 1.039 [95% CI 1.020–1.058]—allp< 0.001). A higher percentage of polyp not covered by heatmap was associated with a correct diagnosis of Algorithm 1 (OR 1.006 [95% CI 1.003–1.010],p< 0.001), while in Algorithm 2, a lower percentage was associated with a correct diagnosis (OR 0.992 [95% CI 0.985–1.000],p0.044). Algorithms 3 and 4 showed negative, but not statistically significant, associations. Conclusions: Higher percentages of heatmap covering the polyp were associated with correct diagnoses of four AI algorithms. This indicates that it is clinically relevant to strive for AI predictions with heatmaps covering as much colorectal polyp tissue as possible. Knowing how to interpret heatmaps could increase trust in AI and, with that, benefit the implementation of AI in clinical practice.
背景/目的：用于诊断结直肠息肉的人工智能（AI）算法正在兴起，但尚未广泛应用。目前对AI的信任度不足，而可视化可解释AI（如热力图）可提升信任度。本研究旨在探讨热力图定位与AI在结直肠息肉内镜特征诊断中准确性的关联。方法：采用四种AI算法对来自两家医院的376个结直肠息肉的前瞻性采集图像（共2133张）进行诊断，以组织病理学结果为金标准。将热力图定位与人工标注的息肉位置进行对比，采用广义估计方程评估热力图定位与AI正确诊断之间的关联。结果：在所有四种算法中，热力图覆盖结直肠息肉的比例越高，诊断正确率越高（OR值分别为1.013[95% CI 1.006–1.019]、1.025[95% CI 1.011–1.039]、1.038[95% CI 1.024–1.053]和1.039[95% CI 1.020–1.058]，所有p值<0.001）。热力图未覆盖息肉的比例较高与算法1的正确诊断相关（OR 1.006[95% CI 1.003–1.010], p<0.001），而在算法2中，该比例较低与正确诊断相关（OR 0.992[95% CI 0.985–1.000], p=0.044）。算法3和4呈现负相关但无统计学显著性。结论：热力图覆盖息肉的比例越高，四种AI算法的诊断正确率越高。这表明在临床实践中，追求热力图尽可能覆盖更多结直肠息肉组织的AI预测具有重要临床意义。掌握热力图的解读方法可增强对AI的信任，从而促进AI在临床实践中的应用。
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