Skip to main content
. 2024 Mar 1;14:5089. doi: 10.1038/s41598-024-51777-2

Figure 3.

Figure 3

Deep learning attention maps overlaid on computed tomography images of patients in the test dataset. (A) A preoperative axial computed tomography image of a 76-year-old man with a pancreatic cancer in the head of pancreas (not shown). In both attention maps of the deep learning models for predicting the occurrence of (B) in all postoperative pancreatic fistula and (C) clinically relevant postoperative pancreatic fistula, the activated gradient regions focused on the area in the head of pancreas and peripancreatic area around the potential pancreatic resection site. In this patient, the postoperative course was uneventful. (D) A preoperative axial computed tomography image of a 74-year-old man diagnosed with distal bile duct cancer (not shown) who experienced Grade B postoperative pancreatic fistula following pancreaticoduodenectomy. In the attention maps of the deep learning models for predicting the occurrence of (E) all postoperative pancreatic fistula and (F) clinically relevant postoperative pancreatic fistula, the attention of both models is found predominantly in the area around the expected pancreatic resection site. POPF postoperative pancreatic fistula, CR-POPF clinically relevant postoperative pancreatic fistula.