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Automated size-specific dose estimates using deep learning image processing.

Jan Juszczyk1, Pawel Badura2, Joanna Czajkowska2

  • 1Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland; Radpoint Sp. z o.o., Gliwicka 275, 40-862 Katowice, Poland.

Medical Image Analysis
|November 28, 2020
PubMed
Summary
This summary is machine-generated.

This study presents an automated system for precise computed tomography (CT) dose monitoring, calculating size-specific dose estimates (SSDE) using AI and optical character recognition. The vendor-independent system accurately determines patient dimensions for improved radiation dose management.

Keywords:
Artificial intelligenceComputed tomographyDeep learningDose managementMedical information systemsRadiation imaging

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Area of Science:

  • Medical Physics
  • Radiology
  • Artificial Intelligence in Healthcare

Background:

  • Accurate radiation dose monitoring in computed tomography (CT) is crucial for patient safety.
  • Existing dose monitoring systems can be vendor-dependent and may struggle with incomplete metadata.

Purpose of the Study:

  • To develop and validate an automated, vendor-independent system for precise dose monitoring in CT examinations.
  • To implement size-specific dose estimates (SSDE) compliant with AAPM regulations.
  • To assess the system's performance against commercial solutions.

Main Methods:

  • Developed an automated system utilizing optical character recognition (OCR) to extract information from dose report images.
  • Employed a convolutional neural network (CNN) for semantic segmentation of axial CT slices to determine patient diameters.
  • Validated the system using 335 CT series (over 60,000 images) from public and clinical data.
  • Compared SSDE results with GE DoseWatch across head, chest, and abdomen regions.

Main Results:

  • Achieved high accuracy in body area segmentation (0.9955 mean accuracy, 0.9752 Jaccard index).
  • Demonstrated low errors in effective diameter (<2 mm) and water equivalent diameter (<1 mm) determination.
  • Showed comparable and statistically significant results to a commercial dose monitoring system, particularly in the head region.

Conclusions:

  • The developed automated system provides accurate and vendor-independent CT dose monitoring.
  • The AI-driven approach for patient dimension estimation enhances the precision of size-specific dose estimates.
  • This system offers a robust solution for radiation dose management in CT imaging.