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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Radiomic Fingerprints: Automated Personal Identification in Mass Disasters Using Shape-Based Features of Thoracic

Shota Ichikawa1,2, Yohan Kondo3, Masashi Okamoto3

  • 1Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-Dori, Chuo-Ku, Niigata, 951-8518, Japan. ichikawa@clg.niigata-u.ac.jp.

Journal of Imaging Informatics in Medicine
|June 24, 2025
PubMed
Summary

Automated personal identification using CT scan radiomic features of thoracic vertebrae achieved 98.4% accuracy in mass disaster victim identification. This method offers a reliable approach for forensic identification.

Keywords:
Computed tomography (CT)Euclidean distance-based similarityPersonal identificationRadiomicsThoracic vertebral bodies

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Area of Science:

  • Radiology
  • Forensic Science
  • Medical Imaging

Background:

  • Accurate personal identification is critical for mass disaster response.
  • Current methods can be time-consuming and resource-intensive.
  • Novel automated approaches are needed for efficient victim identification.

Purpose of the Study:

  • To evaluate an automated method for personal identification using shape-based radiomic features of thoracic vertebral bodies on CT scans.
  • To assess the accuracy and reliability of this method in a mass disaster context.

Main Methods:

  • Retrospective analysis of antemortem and postmortem CT scans from 66 individuals.
  • Segmentation of thoracic vertebral bodies (T1-T12) and extraction of 14 shape-based radiomic features.
  • Application of Mahalanobis distance for outlier detection and Euclidean distance for similarity scoring.

Main Results:

  • A top-1 match rate of 98.4% was achieved for 61 postmortem cases after outlier exclusion.
  • Significantly higher similarity scores were observed for top-1 matches compared to top-2 matches (P < 0.001).
  • An optimal similarity score threshold of 0.832 provided clear differentiation between matches and nonmatches (AUC = 0.99938).

Conclusions:

  • Automated identification using thoracic vertebral body radiomics demonstrates high accuracy for victim identification in mass disasters.
  • This technique shows significant potential as a reliable and efficient tool for forensic identification.
  • Further validation in diverse populations and disaster scenarios is warranted.