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Evaluating artificial intelligence for comparative radiography.

Óscar Gómez1, Pablo Mesejo2,3,4, Óscar Ibáñez2,4,5

  • 1Andalusian Research Institute DaSCI, University of Granada, Granada, Spain. ogomez@decsai.ugr.es.

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Summary
This summary is machine-generated.

This study introduces an AI framework to automate forensic identification by comparing frontal sinuses in radiographs. The system successfully shortlists candidates, significantly reducing manual comparison time and improving accuracy in forensic identification scenarios.

Keywords:
Artificial intelligenceComparative radiographyComputer-aided decision support systemsDeep learningImage registrationImage segmentationSkeleton-based forensic human identification

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Area of Science:

  • Forensic Science
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Comparative radiography is a manual forensic identification technique using skeletal images.
  • Current methods are time-consuming, limiting their use in large-scale identification.
  • Automating this process can enhance efficiency and accuracy in forensic casework.

Purpose of the Study:

  • To develop and validate an AI-powered framework for automating candidate shortlisting in forensic identification.
  • To improve the efficiency of comparative radiography by reducing manual comparison time.
  • To assess the accuracy and reliability of the automated framework.

Main Methods:

  • Utilized deep learning for segmenting frontal sinuses from radiographs.
  • Developed a novel superposition method to align 3D computed tomography data with 2D radiographs.
  • Implemented a decision-making algorithm for ranking candidates based on similarity metrics.

Main Results:

  • Achieved high-quality frontal sinus segmentation using deep learning techniques.
  • The automated framework successfully filtered out 40% of potential candidates based on similarity.
  • Manual validation demonstrated the framework's ability to filter out 73% of candidates, showing robustness to expert variability.

Conclusions:

  • The proposed AI framework significantly automates and enhances the efficiency of forensic identification through comparative radiography.
  • The system demonstrates high accuracy and reliability in shortlisting candidates, comparable to manual methods.
  • This technology has the potential to revolutionize forensic identification processes, especially in mass disaster scenarios.