Automated microscopy in forensic biology: Validating the use of deep neural networks for the detection of human spermatozoa in Christmas Tree stained slides

IF 1.8 4区医学 Q2 MEDICINE, LEGAL

Science & Justice Pub Date : 2026-01-01 Epub Date: 2025-12-04 DOI:10.1016/j.scijus.2025.101370

Dean C. Topping, Teresa Tran, Colby M. Hymus, Nicholas S. Mountford, Yoon C. Liew, Scott E. Egan, Penny L. Cooper

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引用次数: 0

Abstract

The most widely accepted technique for the confirmation of semen presence in forensic biology is the microscopic identification of sperm cells. The process of manual microscopic examination, however, can often be time consuming, particularly where sperm numbers are low or where sperm cells are absent, particularly when in the presence of numerous non-sperm cells. The findings of this study demonstrate that automated microscopy utilising Deep Neural Network (DNN) allows for an improvement of the efficiency of examination times associated with microscopy. Additionally, the use of instrument and slide data as diagnostic tools permits rapid detection of invalid results. Metafer with Sperm Detection, using DNN v3.1.1, successfully identified sperm cells present in both high and low semen concentrations in mixed-cell substrates, often with a higher accuracy than manual microscopic examinations where very low sperm numbers were present (1–5 sperm cells on an entire slide). Whilst there were instances of false positive and false negative sperm cell classifications observed, the overall sensitivity, specificity and accuracy of automated microscopy using Metafer with Sperm Detection (DNN v3.1.1) was 0.9999, 0.9996 and 0.9999, respectively. The conversion of Metafer sperm counts to an existing sperm grading system allows compatibility with manual microscopy outcomes and allows decisions regarding sample triage and submission for specific DNA extraction protocols in routine casework to be made with confidence.

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自动显微镜在法医生物学：验证使用深度神经网络检测人类精子在圣诞树染色玻片

在法医生物学中，最广泛接受的确认精液存在的技术是精子细胞的显微鉴定。然而，人工显微镜检查的过程往往是耗时的，特别是在精子数量少或精子细胞缺失的情况下，特别是在存在大量非精子细胞的情况下。本研究的结果表明，利用深度神经网络（DNN）的自动显微镜可以提高与显微镜相关的检查时间的效率。此外，使用仪器和载玻片数据作为诊断工具，可以快速检测无效结果。Metafer with Sperm Detection使用DNN v3.1.1，成功地鉴定出混合细胞基质中精液浓度高和浓度低的精子细胞，通常比人工显微镜检查的准确性更高，其中精子数量非常低（整个载玻片上有1-5个精子细胞）。虽然观察到存在假阳性和假阴性精子细胞分类的情况，但使用Metafer with sperm Detection （DNN v3.1.1）的自动显微镜的总体灵敏度、特异性和准确性分别为0.9999、0.9996和0.9999。将Metafer精子计数转换为现有的精子分级系统，允许与人工显微镜结果兼容，并允许在常规病例工作中对样本分类和提交特定DNA提取方案做出有信心的决定。

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来源期刊

Science & Justice 医学-病理学

CiteScore

4.20

自引率

15.80%

发文量

审稿时长

81 days

期刊介绍： Science & Justice provides a forum to promote communication and publication of original articles, reviews and correspondence on subjects that spark debates within the Forensic Science Community and the criminal justice sector. The journal provides a medium whereby all aspects of applying science to legal proceedings can be debated and progressed. Science & Justice is published six times a year, and will be of interest primarily to practising forensic scientists and their colleagues in related fields. It is chiefly concerned with the publication of formal scientific papers, in keeping with its international learned status, but will not accept any article describing experimentation on animals which does not meet strict ethical standards. Promote communication and informed debate within the Forensic Science Community and the criminal justice sector. To promote the publication of learned and original research findings from all areas of the forensic sciences and by so doing to advance the profession. To promote the publication of case based material by way of case reviews. To promote the publication of conference proceedings which are of interest to the forensic science community. To provide a medium whereby all aspects of applying science to legal proceedings can be debated and progressed. To appeal to all those with an interest in the forensic sciences.