三维卷积神经网络通过颅骨计算机断层扫描估计死者的死亡年龄

IF 0.8 Q4 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Forensic Imaging Pub Date : 2023-09-01 DOI:10.1016/j.fri.2023.200557

Maya A. Joshi , Sean D. Tallman

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引用次数: 0

摘要

尽管缺乏标准化的多变量技术，但准确的死亡年龄（AAD）估计是建立法医人类学生物学图谱不可或缺的一部分。目前的研究开发并测试了一个三维卷积神经网络和三个模型变体，从新墨西哥州死者图像数据库中进行了1224次未识别的颅骨CT扫描。每个模型都需要输入个人的颅骨CT扫描，并输出AAD估计值。模型3是优越的，在1.5年内成功预测了AAD。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Three-dimensional convolutional neural network for age-at-death estimation of deceased individuals through cranial computed tomography scans

Accurate age-at-death (AAD) estimation is integral in establishing biological profiles in forensic anthropology, though standardized multivariate techniques are lacking. The current study developed and tested a three-dimensional convolutional neural network and three model variations with 1,224 de-identified cranial CT scans from the New Mexico Decedent Image Database. Each model required an input of an individual's cranial CT scan and outputted an AAD estimation. Model 3 was superior, successfully predicting AAD within 1.5 decades.

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来源期刊

Forensic Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

2.20

自引率

27.30%

发文量