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May 9, 2026DiagnosticsOpen Access

Estimating the Post-Mortem Interval Under Extreme Heat Environments: A Climate-Adaptive Case Series Based on Artificial Intelligence-Supported Diagnostics

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Authors

FSFrancesco SessaPegaso UniversityCGClelia GrippaldiUniversity of CataniaMEMassimiliano EspositoUniversità degli Studi di Enna Kore

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Implication

Retrospective case series shows improved PMI estimation in extreme heat environments, suggesting new diagnostic approaches.

Key Points

  • This research aims to enhance the accuracy of post-mortem interval (PMI) estimations in extreme heat conditions using AI-supported diagnostics.
  • Conducted a retrospective case series analysis on three individuals from summer heatwaves.
  • Utilized PMCT, autopsy, and genetic identification combined with historical meteorological data.
  • Developed a multimodal AI model using Random Forest–LSTM architecture to incorporate environmental and decomposition factors.
  • Classical PMI methods overestimated intervals, predicting 1–6 months compared to true PMIs of approximately 20 days (Cases 1-2) or 36–48 hours (Case 3).
  • The AI model produced predictions of ~21 days (Case 1), ~23 days (Case 2), and ~42 hours (Case 3), with clear prediction intervals.
  • Key drivers identified included thermal load, desiccation pressure, and microenvironmental factors, significantly impacting decomposition.

Cite This Study

Sessa et al. (2026) studied this question.

synapsesocial.com/papers/69fed140b9154b0b828787abhttps://doi.org/10.3390/diagnostics16091407
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