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人工智能技术在法医牙科学中的应用和性能——系统评价。

Application and performance of artificial intelligence technology in forensic odontology - A systematic review.

机构信息

Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.

Department of Preventive Dental Sciences, Divison of Pedodontics, College of Dentistry, Jazan University, Jazan, Saudi Arabia.

出版信息

Leg Med (Tokyo). 2021 Feb;48:101826. doi: 10.1016/j.legalmed.2020.101826. Epub 2020 Dec 10.

Abstract

Forensic odontology (FO) mainly deals with the identification of the individual through the remains, which mainly includes teeth and jawbones. Artificial intelligence (AI) technology has proven to be a breakthrough in providing reliable information in decision making in forensic sciences. This systematic review aimed to report on the application and performance of AI technology in FO. The data was gathered through searching for the articles in the renowned search engines, which have been published between January 2000 - June 2020. QUADAS-2 was adopted for the risk of bias analysis of the included studies. AI technology has been widely applied in FO for identifying bite-marks, predicting mandibular morphology, gender determination, and age estimation. Most of these AI models are based on either artificial neural networks (ANNs) or convolutional neural networks (CNNs). The results of the studies are promising. Studies have reported that these models display accuracy and precision equivalent to that of the trained examiners. These models can be promising tools when identifying victims of mass disasters and as an additive aid in medico-legal situations.

摘要

法医牙科学(FO)主要通过遗骸,主要是牙齿和颌骨,来进行个体识别。人工智能(AI)技术已被证明是在法医科学决策中提供可靠信息的突破。本系统评价旨在报告 AI 技术在 FO 中的应用和性能。数据通过在著名搜索引擎中搜索文章收集,这些文章发表于 2000 年 1 月至 2020 年 6 月之间。采用 QUADAS-2 对纳入研究的偏倚风险进行分析。AI 技术已广泛应用于 FO 中的咬痕识别、下颌形态预测、性别鉴定和年龄估计。这些 AI 模型大多数基于人工神经网络(ANNs)或卷积神经网络(CNNs)。研究结果很有前景。研究报告称,这些模型的准确性和精度与经过训练的检查人员相当。在识别大规模灾难受害者和作为法医情况的附加辅助工具时,这些模型可能是很有前途的工具。

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