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Related Experiment Video

Updated: Sep 9, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Automated Detection of Necrotizing Soft Tissue Infection Features by Computed Tomography.

Heng-Yu Lin¹, Ming-Chuan Chiu², Tzu-Lun Kao²

¹Division of Plastic Surgery, Department of Surgery, Chi Mei Medical Center, Tainan 710, Taiwan.

Diagnostics (Basel, Switzerland)

|August 28, 2025

Summary

An automated system using YOLOv10 effectively detects necrotizing soft tissue infection (NSTI) features on CT scans. This AI tool aids in faster diagnosis and improved surgical planning for NSTI.

Keywords:

YOLOv10 artificial intelligence computed tomography necrotizing soft tissue infection object detection

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Necrotizing soft tissue infection (NSTI) requires prompt diagnosis for effective treatment.
Computed tomography (CT) is crucial for identifying NSTI features.
Automated detection systems can enhance diagnostic efficiency.

Purpose of the Study:

To develop and evaluate an automated detection system for NSTI features on CT images.
To utilize the You Only Look Once version 10 (YOLOv10) model for this task.
To improve diagnostic efficiency and surgical planning for NSTI.

Main Methods:

Retrospective study of 31 patients with surgically confirmed NSTIs (2017-2023).
Annotation of 9001 CT images for four NSTI features: ectopic gas, fluid accumulation, fascia edema, and non-enhancement.
Performance evaluation using mean Average Precision (mAP), recall, and precision.

Main Results:

The YOLOv10 model achieved an overall mAP of 0.75.
Recall and precision were 0.74 and 0.72, respectively.
High recall for fascia edematous changes (0.92) and soft tissue ectopic gas (0.76).

Conclusions:

The YOLOv10-based system demonstrates effectiveness in detecting key NSTI features on CT.
This AI approach shows promise for improving NSTI diagnosis and surgical planning.
The system accurately identifies soft tissue ectopic gas, fluid accumulation, fascia edematous changes, and soft tissue non-enhancement.