Murray J, Heng D, Lygate A, Porto L, Abade A, Manica S, Franco A
Department of Forensic Odontology, University of Dundee, Nethergate, Dundee DD1 4HN, UK.
Department of Forensic Odontology, University of Dundee, Nethergate, Dundee DD1 4HN, UK.
Morphologie. 2024 Mar;108(360):100723. doi: 10.1016/j.morpho.2023.100723. Epub 2023 Oct 31.
Forensic odontologists use biological patterns to estimate chronological age for the judicial system. The age of majority is a legally significant period with a limited set of reliable oral landmarks. Currently, experts rely on the questionable development of third molars to assess whether litigants can be prosecuted as legal adults. Identification of new and novel patterns may illuminate features more dependably indicative of chronological age, which have, until now, remained unseen. Unfortunately, biased perceptions and limited cognitive capacity compromise the ability of researchers to notice new patterns. The present study demonstrates how artificial intelligence can break through identification barriers and generate new estimation modalities. A convolutional neural network was trained with 4003 panoramic-radiographs to sort subjects into 'under-18' and 'over-18' age categories. The resultant architecture identified legal adults with a high predictive accuracy equally balanced between precision, specificity and recall. Moving forward, AI-based methods could improve courtroom efficiency, stand as automated assessment methods and contribute to our understanding of biological ageing.
法医牙科学者利用生物学模式为司法系统估算实足年龄。成年年龄是一个具有法律意义的时期，可靠的口腔标志有限。目前，专家们依靠第三磨牙的可疑发育情况来评估诉讼当事人是否可作为法定成年人被起诉。识别新的模式可能会揭示出更可靠地指示实足年龄的特征，而这些特征迄今为止一直未被发现。不幸的是，有偏见的认知和有限的认知能力损害了研究人员发现新模式的能力。本研究展示了人工智能如何突破识别障碍并生成新的估算方式。一个卷积神经网络用4003张全景X光片进行训练，以将受试者分为“18岁以下”和“18岁以上”年龄类别。由此产生的架构识别法定成年人的预测准确率很高，在精确率、特异性和召回率之间达到了平衡。展望未来，基于人工智能的方法可以提高法庭效率，作为自动化评估方法，并有助于我们对生物衰老的理解。
