Advances in postmortem interval estimation: A systematic review of machine learning and metabolomics across various tissue types.

Journal: Forensic science, medicine, and pathology
Published Date: Jul 24, 2025

Abstract

BACKGROUND: Traditional postmortem interval (PMI) estimation methods rely on observable changes such as rigor mortis, livor mortis, and algor mortis but are often affected by environmental factors. Metabolomics, combined with techniques like nuclear magnetic resonance (NMR) and mass spectrometry, improves accuracy by identifying biochemical changes postmortem. Machine learning methods such as Principal Component Analysis (PCA), Partial Least Squares (PLS), and Support Vector Machines (SVMs), enhance PMI predictions by analyzing metabolite data. This review aims to summarize advances in using machine learning for PMI estimation and identify the optimal combination of tissue samples and algorithms for accurate predictions.

Authors

  • Abdulkreem Abdullah AlJuhani
    Department of Surgery King, Abdulaziz University Hospital, Riyadh, SA, Saudi Arabia. Kroomx@outlook.com.
  • Rodan Mahmoud Desoky
    College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
  • Abdulaziz A Binshalhoub
    Forensic Medicine Services Administration, Riyadh, Saudi Arabia.
  • Mohammed Jamaan Alzahrani
    Faculty of Medicine, King Abdulaziz University, Riyadh, Saudi Arabia.
  • Mofareh Shubban Alraythi
    Faculty of Medicine, Jazan University Saudi Arabia, Jizan, Saudi Arabia.
  • Farouq Faisal Alzahrani
    Nayef College for National Security, Riyadh, Saudi Arabia.

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