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Automated Prediction of Postoperative Pancreatic Fistula Using Preoperative Computed Tomography

:::info Stub — Full Engineering Breakdown Coming This paper was auto-fetched from arXiv on 2026-06-01. A full breakdown with production viability rating, implementation notes, and honest limitations is being written. Subscribe to AI Letters → :::

AuthorsAshok Choudhary et al.
Year2026
FieldComputer Vision
arXiv2605.31539
PDFDownload
Categoriescs.CV, cs.LG

Abstract​

Postoperative pancreatic fistula (POPF) is a serious complication after pancreatic resection, increasing morbidity, hospital stay, and healthcare costs. We present an automatic, end-to-end deep learning pipeline-from pancreatic segmentation to classification-for preoperative POPF risk estimation and stratification using preoperative CT scans. A data set with auto-segmented pancreas volumes and surgical outcomes was used to evaluate multiple architectures, including a custom lightweight 3D CNN baseline (CNN3D), R(2+1)D ResNet-18, and ResNet-MC3-18 models. Evaluation across multiple 3D architectures demonstrated promising predictive performance. This approach offers a clinically valuable tool and a methodological benchmark for pancreas-specific CT classification, supporting improved preoperative decision-making in pancreatic surgery.

Engineering Breakdown​

The Problem​

Postoperative pancreatic fistula (POPF) is a serious complication after pancreatic resection, increasing morbidity, hospital stay, and healthcare costs.

The Approach​

We present an automatic, end-to-end deep learning pipeline-from pancreatic segmentation to classification-for preoperative POPF risk estimation and stratification using preoperative CT scans.

Key Results​

This approach offers a clinically valuable tool and a methodological benchmark for pancreas-specific CT classification, supporting improved preoperative decision-making in pancreatic surgery.

Research Areas​

This paper contributes to the following areas of AI/ML engineering:

  • Image recognition
  • Object detection
  • Visual transformers
  • Convolutional networks
  • Multimodal learning
  • Automated

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