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. 2023 Jul 11;43(6):719–736. doi: 10.1177/0272989X231184175

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© The Author(s) 2023

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

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Graphical representation of the DNN emulator for CRC-AIM. The DNN consists of 23 nodes in the input layer representing the unobservable parameters, 4 hidden layers, and an output layer with 8 nodes representing the primary calibration targets, which include CRC incidence by location and gender from SEER 1975-1979, adenoma prevalence by age, and percentage of detected adenomas ≤ 5 mm).

CRC, colorectal cancer; CRC-AIM, Colorectal Cancer-Adenoma Incidence and Mortality model; DNN, Deep neural networks; SEER, Surveillance, Epidemiology, and End Results.