The Association Between Heatmap Position and the Diagnostic Accuracy of Artificial Intelligence for Colorectal Polyp Diagnosis

• Published in: Cancers Vol. 17; no. 10; p. 1620
• Main Authors: Thijssen, Ayla, Dehghani, Nikoo, Schrauwen, Ruud W. M., Keulen, Eric T. P., Rondagh, Eveline J. A., van Avesaat, Mark H. P., Soufidi, Khalida, Reumkens, Ankie, Bours, Paul H. A., van der Zander, Quirine E. W., de With, Peter H. N., Winkens, Bjorn, Sommen, Fons van der, Schoon, Erik J.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.05.2025; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Cancer; Clinical medicine; Colorectal cancer; Colorectal carcinoma; Datasets; Diagnosis; Endoscopy; Medical imaging equipment; Patients; Polyps; Statistical analysis; Tumors; Netherlands; Japan; colorectal polyps; computer-aided diagnosis; colonoscopy; visually explainable artificial intelligence
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers17101620

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]—all p < 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], p 0.044). 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.