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Actuarial Approach01:20

Actuarial Approach

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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Related Experiment Video

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Artificial intelligence in forensic pathology: Multi-organ postmortem pathomics for estimating postmortem interval.

Guoshuai An1, Yu Gao1, Siyuan Cheng1

  • 1School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi 030600, China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi 030600, China.

Computer Methods and Programs in Biomedicine
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces postmortem pathomics, using deep learning on histological images to estimate postmortem intervals. The multi-organ integrated model achieved high accuracy, paving the way for advanced forensic analysis.

Keywords:
Artificial intelligenceDigital pathologyPostmortem interval estimationPostmortem pathomicsWhole slide images

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Area of Science:

  • Forensic pathology
  • Digital pathology
  • Computational biology

Background:

  • Accurate postmortem interval estimation is vital in forensic investigations.
  • Pathomics, using whole-slide images, is a novel approach for disease diagnosis and prognosis.
  • Postmortem pathomics is emerging as a key subfield for forensic image analysis.

Purpose of the Study:

  • To develop a three-level hierarchical strategy using pathomics for postmortem histological image analysis.
  • To create a multi-organ integrated model for accurate postmortem interval estimation.
  • To establish foundational methods for the field of postmortem pathomics.

Main Methods:

  • Whole-slide images of liver, kidney, and skeletal muscle from pigs at various postmortem times were analyzed.
  • Deep learning models (DenseNet121, VGG16) were trained on image patches after quality control and normalization.
  • A stacking ensemble model integrated organ-specific predictions for a final multi-organ individual-level estimation.

Main Results:

  • Organ-specific deep learning models achieved high accuracies: 81.25% (liver), 87.5% (kidney), and 62.5% (muscle).
  • The integrated multi-organ model demonstrated strong performance with internal test accuracy of 93.75% and external validation accuracy of 87.5%.
  • DenseNet121 and VGG16 showed superior performance for specific tissues, forming the basis for specialized 'nets'.

Conclusions:

  • Pathomics and deep learning show significant potential for accurate postmortem interval estimation.
  • The developed three-level framework effectively integrates multi-organ data for improved forensic analysis.
  • Whole-slide imaging offers a novel data modality, advancing strategies in postmortem interval determination.