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Tournois L, Trousset V, Hatsch D, Delabarde T, Ludes B, Lefèvre T. Artificial intelligence in the practice of forensic medicine: a scoping review. Int J Legal Med 2024; 138:1023-1037. [PMID: 38087052 PMCID: PMC11003914 DOI: 10.1007/s00414-023-03140-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 11/21/2023] [Indexed: 04/11/2024]
Abstract
Forensic medicine is a thriving application field for artificial intelligence (AI). Indeed, AI applications intended to forensic pathologists or forensic physicians have emerged since the last decade. For example, AI models were developed to help estimate the biological age of migrants or human remains. However, the uses of AI applications by forensic pathologists or physicians and their levels of integration in medicolegal practices are not well described yet. Therefore, a scoping review was conducted on PubMed, ScienceDirect, and Scopus databases. This review included articles that mention any AI application used by forensic pathologists or physicians in practice or any AI model applied in one expertise field of the forensic pathologist or physician. Articles in other languages than English or French or dealing mainly with complementary analyses handled by experts who are not forensic pathologists or physicians or with AI to analyze data for research purposes in forensic medicine were excluded from this review. All the relevant information was retrieved in each article from a grid analysis derived and adapted from the TRIPOD checklist. This review included 35 articles and revealed that AI applications are developed in thanatology and in clinical forensic medicine. However, those applications seem to mainly remain in research and development stages. Indeed, the use of AI applications by forensic pathologists or physicians is not actual due to issues discussed in this article. Finally, the integration of AI in daily medicolegal practice involves not only forensic pathologists or physicians but also legal professionals.
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Affiliation(s)
- Laurent Tournois
- Université Paris Cité, CNRS UMR 8045, 75006, Paris, France.
- BioSilicium, Riom, France.
| | - Victor Trousset
- IRIS Institut de Recherche Interdisciplinaire Sur Les Enjeux Sociaux, UMR8156 CNRS - U997 Inserm - EHESS - Université Sorbonne Paris Nord, Paris, France
- Department of Forensic and Social Medicine, AP-HP, Jean Verdier Hospital, Bondy, France
| | | | - Tania Delabarde
- Université Paris Cité, CNRS UMR 8045, 75006, Paris, France
- Institut Médico-Légal de Paris, Paris, France
| | - Bertrand Ludes
- Université Paris Cité, CNRS UMR 8045, 75006, Paris, France
- Institut Médico-Légal de Paris, Paris, France
| | - Thomas Lefèvre
- IRIS Institut de Recherche Interdisciplinaire Sur Les Enjeux Sociaux, UMR8156 CNRS - U997 Inserm - EHESS - Université Sorbonne Paris Nord, Paris, France
- Department of Forensic and Social Medicine, AP-HP, Jean Verdier Hospital, Bondy, France
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Atreya A, Ateriya N, Menezes RG. The eye in forensic practice: In the dead. Med Leg J 2024:258172241230210. [PMID: 38690614 DOI: 10.1177/00258172241230210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Post-mortem examination of the eye provides valuable forensic information yet is often overlooked. This brief review focuses on determining the cause/manner of death and post-mortem interval. External eye findings like corneal haziness and tache noire, combined with post-mortem changes in the iris, lens, retina and vitreous humour, can help estimate time since death. Ocular biometrics (iris/retinal scans) may facilitate identification. Age-related ocular changes can provide insights. The eye offers clues into personality (corneal tattooing, trichotillomania) and cause of death (petechiae in strangulation, retinal haemorrhages in abusive head trauma). Ocular trauma and underlying eye disease may be evident. Toxicology of vitreous humour can detect drugs/poisons. As a window into systemic disease and age-related changes, the eye aids pathology interpretations and, accordingly, post-mortem examinations have value. Ocular findings should not be overlooked in forensic examinations as they provide distinct information in determining cause/manner of death and post-mortem interval.
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Affiliation(s)
- Alok Atreya
- Department of Forensic Medicine, Lumbini Medical College, Palpa, Nepal
| | - Navneet Ateriya
- Department of Forensic Medicine & Toxicology, All India Institute of Medical Sciences, Gorakhpur, India
| | - Ritesh G Menezes
- Forensic Medicine Division, Department of Pathology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
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Shen Z, Zhong Y, Wang Y, Zhu H, Liu R, Yu S, Zhang H, Wang M, Yang T, Zhang M. A computational approach to estimate postmortem interval using postmortem computed tomography of multiple tissues based on animal experiments. Int J Legal Med 2024; 138:1093-1107. [PMID: 37999765 DOI: 10.1007/s00414-023-03127-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/27/2023] [Indexed: 11/25/2023]
Abstract
The estimation of postmortem interval (PMI) is a complex and challenging problem in forensic medicine. In recent years, many studies have begun to use machine learning methods to estimate PMI. However, research combining postmortem computed tomography (PMCT) with machine learning models for PMI estimation is still in early stages. This study aims to establish a multi-tissue machine learning model for PMI estimation using PMCT data from various tissues. We collected PMCT data of seven tissues, including brain, eyeballs, myocardium, liver, kidneys, erector spinae, and quadriceps femoris from 10 rabbits after death. CT images were taken every 12 h until 192 h after death, and HU values were extracted from the CT images of each tissue as a dataset. Support vector machine, random forest, and K-nearest neighbors were performed to establish PMI estimation models, and after adjusting the parameters of each model, they were used as first-level classification to build a stacking model to further improve the PMI estimation accuracy. The accuracy and generalized area under the receiver operating characteristic curve of the multi-tissue stacking model were able to reach 93% and 0.96, respectively. Results indicated that PMCT detection could be used to obtain postmortem change of different tissue densities, and the stacking model demonstrated strong predictive and generalization abilities. This approach provides new research methods and ideas for the study of PMI estimation.
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Affiliation(s)
- Zefang Shen
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Yue Zhong
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Yucong Wang
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Haibiao Zhu
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Ran Liu
- Forensic Science Center of Beijing Huatong Junjian Science and Technology Company Limited, Beijing, 100016, China
| | - Shengnan Yu
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Haidong Zhang
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Min Wang
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China
| | - Tiantong Yang
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China.
| | - Mengzhou Zhang
- Key Laboratory of Evidence Science (China University of Political Science and Law), Ministry of Education, No. 25 Xitucheng Road, Haidian District, Beijing, 100088, China.
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Cao J, An G, Li J, Wang L, Ren K, Du Q, Yun K, Wang Y, Sun J. Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy. Forensic Sci Res 2023; 8:50-61. [PMID: 37415796 PMCID: PMC10265958 DOI: 10.1093/fsr/owad007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 02/10/2023] [Indexed: 07/08/2023] Open
Abstract
Wound age estimation is one of the most challenging and indispensable issues for forensic pathologists. Although many methods based on physical findings and biochemical tests can be used to estimate wound age, an objective and reliable method for inferring the time interval after injury remains difficult. In the present study, endogenous metabolites of contused skeletal muscle were investigated to estimate the time interval after injury. Animal model of skeletal muscle injury was established using Sprague-Dawley rat, and the contused muscles were sampled at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h postcontusion (n = 9). Then, the samples were analysed using ultraperformance liquid chromatography coupled with high-resolution mass spectrometry. A total of 43 differential metabolites in contused muscle were determined by metabolomics method. They were applied to construct a two-level tandem prediction model for wound age estimation based on multilayer perceptron algorithm. As a result, all muscle samples were eventually divided into the following subgroups: 4, 8, 12, 16-20, 24-32, 36-40, and 44-48 h. The tandem model exhibited a robust performance and achieved a prediction accuracy of 92.6%, which was much higher than that of the single model. In summary, the multilayer perceptron-multilayer perceptron tandem machine-learning model based on metabolomics data can be used as a novel strategy for wound age estimation in future forensic casework. Key Points The changes of metabolite profile were correlated with the time interval after injury in contused skeletal muscle.A panel of 43 endogenous metabolites screened by ultraperformance liquid chromatography coupled with high-resolution mass spectrometry could distinguish the wound ages.The multilayer perceptron (MLP) algorithm exhibited a robust performance in wound age estimation using metabolites.The combination of matabolomics and MLP-MLP tandem model could improve the accuracy of inferring the time interval after injury.
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Affiliation(s)
| | | | - Jian Li
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Liangliang Wang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Kang Ren
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Qiuxiang Du
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Keming Yun
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Yingyuan Wang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, China
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Galante N, Cotroneo R, Furci D, Lodetti G, Casali MB. Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives. Int J Legal Med 2023; 137:445-458. [PMID: 36507961 DOI: 10.1007/s00414-022-02928-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 12/04/2022] [Indexed: 12/14/2022]
Abstract
In recent years, new studies based on artificial intelligence (AI) have been conducted in the forensic field, posing new challenges and demonstrating the advantages and disadvantages of using AI methodologies to solve forensic well-known problems. Specifically, AI technology has tried to overcome the human subjective bias limitations of the traditional approach of the forensic sciences, which include sex prediction and age estimation from morphometric measurements in forensic anthropology or evaluating the third molar stage of development in forensic odontology. Likewise, AI has been studied as an assisting tool in forensic pathology for a quick and easy identification of the taxonomy of diatoms. The present systematic review follows the PRISMA 2020 statements and aims to explore an emerging topic that has been poorly analyzed in the forensic literature. Benefits, limitations, and forensic implications concerning AI are therefore highlighted, by providing an extensive critical review of its current applications on forensic sciences as well as its future directions. Results are divided into 5 subsections which included forensic anthropology, forensic odontology, forensic pathology, forensic genetics, and other forensic branches. The discussion offers a useful instrument to investigate the potential benefits of AI in the forensic fields as well as to point out the existing open questions and issues concerning its application on real-life scenarios. Procedural notes and technical aspects are also provided to the readers.
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Affiliation(s)
- Nicola Galante
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy.
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy.
| | - Rosy Cotroneo
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
| | - Domenico Furci
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
| | - Giorgia Lodetti
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Biomedical Sciences for Health, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
| | - Michelangelo Bruno Casali
- Healthcare Accountability Lab, Institute of Legal Medicine of Milan, University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
- Department of Oncology and Hemato-Oncology (DIPO), University of Milan, Via Luigi Mangiagalli 37, 20133, Milan, Italy
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Van der Veer J, Rzepczyk S, Żaba C. Keep an eye on the crime – a new look at the forensic use of post-mortem eye examination to estimate time of death. JOURNAL OF MEDICAL SCIENCE 2023. [DOI: 10.20883/medical.e753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
Abstract
Determining the time of death plays a crucial role in a forensic post-mortem examination. Many methods for the time of death (TOD) determination have been developed. However, most are not applicable during the first hours after death and produce large post-mortem interval (PMI) ranges. Eye examination makes it possible to precisely determine the time of death during the initial period after death with half-hour accuracy.. In recent years methods for estimating the time of death by measuring the changes in the eye have made great strides. Those methods use the changes in the reaction to drugs and macroscopically visible morphological changes. Experimental studies also produced equations that can estimate the post-mortem interval using biochemical, electrochemical and thermal changes within the eye.
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Application of artificial intelligence and machine learning technology for the prediction of postmortem interval: A systematic review of preclinical and clinical studies. Forensic Sci Int 2022; 340:111473. [PMID: 36166880 DOI: 10.1016/j.forsciint.2022.111473] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 06/28/2022] [Accepted: 09/18/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND /PURPOSE Establishing an accurate postmortem interval (PMI) is exceptionally crucial in forensic investigation. Artificial intelligence (AI) and Machine learning (ML) models are widely employed in forensic practice. ML is a part of AI, both terms are highly associated and sometimes used interchangeably. This systematic review aims to evaluate the application and performance of AI technology for the prediction of PMI. METHODS Systematic literature search across different electronic databases using PubMed/Google Scholar/EMBASE/Scopus/CINAHL/Web of Science/Cochrane library was conducted from inception to 3 December 2021 for preclinical and clinical studies reported ML models for PMI estimation. RESULTS We identified 18 studies (12 preclinical and 06 clinical) that met the inclusion criteria in the qualitative analysis. Most of the studies employed supervised learning (N = 15), and others employed unsupervised learning (N = 3). Due to the heterogeneity of the samples, quantitative analysis was not performed. CONCLUSION In this systematic review, we discussed the performance of AI-based automated systems in PMI estimation. ML models have demonstrated accuracy and precision and the ability to overcome human errors and bias. However, the research is limited, conducted in primarily small, selected human populations. In addition, we suggest further research in larger population-based studies is needed to fully understand the extent of integrated ML models.
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AI in Forensic Medicine for the Practicing Doctor. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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10
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Artificial Intelligence in Forensic Medicine. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_220-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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11
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AI in Forensic Medicine for the Practicing Doctor. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_221-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Kuwayama K, Nariai M, Miyaguchi H, Iwata YT, Kanamori T, Tsujikawa K, Yamamuro T, Segawa H, Abe H, Iwase H, Inoue H. Estimation of day of death using micro-segmental hair analysis based on drug use history: a case of lidocaine use as a marker. Int J Legal Med 2018; 133:117-122. [DOI: 10.1007/s00414-018-1939-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Accepted: 09/11/2018] [Indexed: 01/25/2023]
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