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Related Experiment Video

Updated: Jul 19, 2026

Multimodal Bioluminescent and Positronic-emission Tomography/Computational Tomography Imaging of Multiple Myeloma Bone Marrow Xenografts in NOG Mice

Multimodal Bioluminescent and Positronic-emission Tomography/Computational Tomography Imaging of Multiple Myeloma Bone Marrow Xenografts in NOG Mice

Published on: January 7, 2019

A computational approach to estimate postmortem interval using postmortem computed tomography of multiple tissues

Zefang Shen¹, Yue Zhong¹, Yucong Wang¹

¹Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China.

International Journal of Legal Medicine

|November 24, 2023

Summary

Estimating the time since death (postmortem interval) is challenging. This study uses machine learning and postmortem CT scans of multiple tissues to accurately predict the postmortem interval.

Keywords:

Forensic pathology Machine learning algorithms Postmortem computed tomography Postmortem interval Stacking algorithm

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Last Updated: Jul 19, 2026

Multimodal Bioluminescent and Positronic-emission Tomography/Computational Tomography Imaging of Multiple Myeloma Bone Marrow Xenografts in NOG Mice

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High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

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Published on: December 16, 2022

Area of Science:

Forensic Medicine
Radiology
Machine Learning

Background:

Accurate postmortem interval (PMI) estimation is crucial in forensic investigations.
Machine learning (ML) approaches are increasingly applied to PMI estimation.
Combining postmortem computed tomography (PMCT) with ML for PMI estimation is an emerging field.

Purpose of the Study:

To develop a multi-tissue machine learning model for PMI estimation using PMCT data.
To evaluate the efficacy of various ML algorithms and a stacking ensemble model.

Main Methods:

Collected PMCT data from seven rabbit tissues (brain, eyeballs, myocardium, liver, kidneys, erector spinae, quadriceps femoris) up to 192 hours postmortem.
Extracted Hounsfield Unit (HU) values from CT images to create a dataset.
Applied Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) models, and developed a stacking ensemble model.

Main Results:

The multi-tissue stacking model achieved an accuracy of 93% and a generalized area under the receiver operating characteristic curve (AUC) of 0.96.
Demonstrated that PMCT can capture postmortem changes in tissue density.
The stacking model exhibited strong predictive and generalization capabilities.

Conclusions:

PMCT data combined with a multi-tissue stacking ML model offers a promising approach for accurate PMI estimation.
This methodology provides novel research avenues for postmortem interval determination.
The study highlights the potential of integrating imaging and computational techniques in forensic science.