AIを活用した新ツールが法医学における外傷性脳損傷の調査を強化 (New AI-powered Tool Could Enhance Traumatic Brain Injury Investigations in Forensics and Law Enforcement)

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2025-02-26 カーディフ大学

カーディフ大学を含む国際研究チームは、AIを活用した新しいツールを開発し、外傷性脳損傷(TBI)の法医学的調査を支援することに成功しました。このツールは、物理学に基づくAI駆動型技術であり、機械学習と力学シミュレーションを組み合わせることで、警察や法医学チームが報告された暴行シナリオに基づいてTBIの結果を正確に予測するのに役立ちます。研究はオックスフォード大学が主導し、カーディフ大学、テムズバレー警察、英国国家犯罪対策庁、ジョン・ラドクリフ病院、Lurtis Ltdとの共同で行われ、成果は『Nature Communications Engineering』誌に掲載されました。このAIフレームワークは、匿名化された実際の警察報告書や法医学データで訓練され、頭蓋骨骨折で94%、意識喪失と頭蓋内出血でそれぞれ79%の高い予測精度を達成しました。この技術は、法医学的調査の精度と一貫性を向上させ、TBI関連の法的手続きにおいて客観的な評価を提供する可能性があります。

<関連情報>

警察・法医学捜査における外傷性脳損傷予測のための力学情報に基づく機械学習フレームワーク A mechanics-informed machine learning framework for traumatic brain injury prediction in police and forensic investigations

Yuyang Wei,Jeremy Oldroyd,Phoebe Haste,Jayaratnam Jayamohan,Michael Jones,Nicholas Casey,Jose-Maria Peña,Sonya Baylis,Stan Gilmour & Antoine Jérusalem
Communications Engineering  Published:26 February 2025
DOI:https://doi.org/10.1038/s44172-025-00352-2

AIを活用した新ツールが法医学における外傷性脳損傷の調査を強化 (New AI-powered Tool Could Enhance Traumatic Brain Injury Investigations in Forensics and Law Enforcement)

Abstract

Police forensic investigations are not immune to our society’s ubiquitous search for better predictive ability. In the particular and very topical case of Traumatic Brain Injury (TBI), police forensic investigations aim at evaluating whether a given impact or assault scenario led to the clinically observed TBI. This question is traditionally answered by means of forensic biomechanics and neurosurgical expertise which cannot provide a fully objective probabilistic measure. To this end, we propose here a numerical framework-based solution coupling biomechanical simulations of a variety of injurious impacts to machine learning training of police reports provided by the UK’s Thames Valley Police and the National Crime Agency’s National Injury Database. In this approach, the biomechanical predictions of mechanical metrics such as strain and stress distributions are interpreted by the machine learning model by additionally considering assault specific metadata to predict brain injury outcomes. The framework, only taking as input information typically available in police reports, reaches prediction accuracies exceeding 94% for skull fracture, 79% for loss of consciousness and intracranial haemorrhage, and is able to identify the best predictive features for each targeted injury. Overall, the proposed framework offers new avenues for the prediction, directly from police reports, of any TBI related symptom as required by forensic law enforcement investigations.

医療・健康
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