The original patient data obtained from the hospitals cover multiple files. However, existing methods focus on the analysis of CT images containing the pancreas, and ignore the importance of screening the original data at an early stage, as shown in Figure Figure11A. The proposed model first screens out transverse plane CT images containing the pancreas before deep-learning diagnosis (Figure (Figure11B).

The original files obtained from the hospitals contain different file formats, different imaging planes and different angiography phases. (A) Artificial intelligence approaches currently used for pancreatic diagnosis focus on the analysis of valid CT images, and ignore the importance of screening the original data at an early stage. (B) Our proposed FEE-DL model first screens out transverse plane CT images containing the pancreas from complex original files before deep-learning diagnosis.

Figure Figure22 shows that the dataset we established is complex with three important characteristics: text reports (CT examination diagnosis reports and patient protocols), different imaging planes (coronal, sagittal, and transverse), and different angiography phases (arterial, venous, and delayed or portal vein phase). To control the image quality, screening selects only transverse plane CT images containing the pancreas.

Multiplex original clinical data. (A-C) Images not directly used by the FEE-DL model containing (A) coronal plane CT scan, (B) sagittal plane CT scan, and (C) CT scan without pancreas. (D) Arterial, (E) venous, and (F) delayed phase CT scans.

Each image in the dataset contains attributes such as 'Patient Name', 'Image shape', and 'Series Description'. The model screens images according to 'Image shape' being 512 × 512 and 'Series Description' being 'Arterial phase', 'Venous phase', or 'Delayed phase'. In consideration of the different specifications of scanners, we enhanced the contrast of the images and then normalized them to 0-255 to highlight the pancreas structure and increase the versatility of the FEE-DL model.

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