Bowel Obstruction Detection and Localization on Abdominal CT with Deep Learning

2026-07-27 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Health & Medical Research · Depth: Expert, extended

Summary

A novel deep learning framework has been developed for automated bowel obstruction detection and transition zone localization on abdominal CT scans. This multi-task system jointly identifies obstructions and pinpoints their critical transition zones, a task previously understudied. An extension, based on the P2P method, further provides inherently interpretable intra-slice localization of the suspected transition point. Evaluated on an internal dataset of 1,427 abdominal CTs, the model achieved a 93% obstruction detection test accuracy and a 95% Hit@10 for transition zone localization. The P2P extension demonstrated a 96% AUROC for detection while localizing the transition point within a minimal 5% of the image area. This advancement significantly reduces the diagnostic workload by narrowing the search to a median of three slices.

Key takeaway

For radiologists managing high CT workloads, this deep learning framework offers a significant advancement in diagnosing bowel obstructions. You can expect improved diagnostic accuracy (93%) and a drastically reduced search space, with the transition zone localized within a median of just three slices. Consider integrating such multi-task, interpretable AI systems into your workflow to accelerate patient outcomes and enhance trust in automated findings, especially for critical clinical landmarks.

Key insights

A multi-task deep learning framework detects bowel obstruction and localizes its transition zone on CTs, enhancing diagnostic workflow.

Principles

• Design medical AI to mirror clinical workflow.
• Hierarchical frameworks improve diagnostic support.
• Interpretable AI enhances trust and robustness.

Method

A multi-task deep learning framework processes CT slices for patient-level obstruction detection and slice-level transition zone localization, extended by P2P for intra-slice transition point identification using a probabilistic selection mask.

In practice

• Integrate multi-task models for comprehensive diagnostic support.
• Adapt interpretable AI (e.g., P2P) for medical image analysis.
• Employ hierarchical evaluation for complex medical AI systems.

Topics

• Bowel Obstruction
• CT Imaging
• Deep Learning
• Transition Zone Localization
• Interpretable AI
• Multi-task Learning

Code references

• agostini335/bo-detection-localization

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.