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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...

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Related Experiment Video

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Deep learning algorithm applied to plain CT images to identify superior mesenteric artery abnormalities.

Junhao Mei1, Hui Yan2, Zheyu Tang1

  • 1Department of Interventional and Vascular Surgery, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, China.

European Journal of Radiology
|February 27, 2024
PubMed
Summary

A deep learning model using YOLOv8x on CT scans effectively detected superior mesenteric artery (SMA) abnormalities, outperforming clinical models and radiologists. This AI approach shows promise for improving early diagnosis and patient outcomes.

Keywords:
Deep learningPlain CTSMA abnormalitiesSuperior mesenteric artery

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Vascular Surgery

Background:

  • Diagnosing superior mesenteric artery (SMA) abnormalities is challenging due to atypical presentations, lack of biomarkers, and limitations of plain computed tomography (CT).
  • Delayed diagnosis of SMA abnormalities can lead to poor clinical outcomes.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL) model for detecting SMA abnormalities using plain CT images.
  • To compare the DL model's performance against a clinical model and experienced radiologists.

Main Methods:

  • A total of 1048 patients were included in internal and external cohorts.
  • Five You Only Look Once version 8 (YOLOv8)-based DL submodels were developed.
  • The optimal submodel (YOLOv8x) performance was compared with a clinical model and radiologist assessments.

Main Results:

  • The YOLOv8x submodel demonstrated superior performance with a higher area under the curve (AUC) compared to the clinical model and radiologists.
  • YOLOv8x achieved significantly higher sensitivity and specificity in detecting SMA abnormalities than radiologists in both internal and external test sets.

Conclusions:

  • The YOLOv8x DL model efficiently identifies SMA abnormalities from plain CT images.
  • This AI-driven approach has the potential to enhance early diagnosis accuracy for SMA abnormalities, ultimately improving clinical outcomes.