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Summary: Background: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. Methods: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists’ detection performance. Findings: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p 0.99), and it detected 2 cases that had been missed by radiologists. Interpretation: The developed DL model can accurately detect colorectal cancer and improve radiologists’ detection performance, showing its potential as an effective computer-aided detection tool. Funding: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).

The β-nickel sulfide (β-NiS) nanobelts were fabricated by electrodepositing a nickel nanosheet film on Indium tin oxide (ITO)-coated glass substrates and sulfuring the nickel film on ITO-coated glass substrates. The sulfurization method can be used to form nanobelts without a template. A small glass tube was used to anneal the sulfur sheet with a nickel nanosheet film. After applying vacuum to the tube, the specimen was annealed at 500 °C. By adjusting the weight of the sulfur sheet in a small glass tube, a nanobelt structure can be formed on the film for 4 h. The β-NiS nanobelt film had a sulfide and nickel molar ratio that was nearly 0.7 (S/Ni). After five years of a long-term storage test, the β-NiS nanobelt films were able to measure the glucose in a solution with the value of sensitivity of 8.67 µA cm−2 µM−1. The β-NiS nanobelt film also detected glucose with a limit of low detection (LOD) of around 0.173 µM. The estimation of reproducibility was over 98%. Therefore, the β-NiS nanobelt film has a significant ability to detect low concentrations of glucose in a solution.