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Related Experiment Video

Updated: Jan 12, 2026

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Multifaceted Preprocessing Optimization for Post-Mortem Radiomics Analysis: A Pilot Study on Forensic Age Estimation

Despoina E Flouri1, Matthaios Triantafyllou2, Kostas Marias3

  • 1Forensic Medicine Unit, University Hospital of Heraklion, University of Crete, Heraklion, Crete, Greece. despina.flouri@hotmail.com.

Journal of Imaging Informatics in Medicine
|November 6, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces postmortem CT radiomics for estimating age at death. The best model achieved an R2 of 0.527, showing promise for automated forensic anthropology.

Keywords:
Forensic age estimationPostmortem CTPreprocessingRadiomics

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Area of Science:

  • Forensic Medicine
  • Forensic Anthropology
  • Radiology

Background:

  • Age at death estimation is crucial in forensic medicine and anthropology.
  • Current methods may have limitations, necessitating novel approaches.

Purpose of the Study:

  • To propose and evaluate a novel approach using postmortem CT radiomics for age at death estimation.
  • To optimize preprocessing parameters for radiomic feature extraction.

Main Methods:

  • A pilot study involving 60 proximal right femora segmented using 3D Slicer.
  • Radiomic features extracted across 12 preprocessing configurations (resampling, LoG kernel size, bin width).
  • Feature stability assessed, followed by feature selection and 12 Random Forest Regression models.

Main Results:

  • Most feature groups showed similar stability, except NGTDM features.
  • First-order features and wavelet decompositions dominated feature selection.
  • The best Random Forest model achieved R2 = 0.527 and RMSE ≈ 12.8 years.

Conclusions:

  • Preliminary results are promising for an automated age at death estimation model.
  • This approach has the potential to revolutionize forensic medicine and anthropology.
  • Further development could lead to a robust, automated tool for age estimation.