Chinese Journal of Forensic Sciences ›› 2024 ›› Issue (3): 41-48.DOI: 10.3969/j.issn.1671-2072.2024.03.006
• Special Topic Discussion:New Quality Productive Forces in the Field of Forensic Appraisal • Previous Articles Next Articles
LI Na, ZHANG Shurui, SUN Junhong
Received:
2023-12-25
Published:
2024-05-15
Online:
2024-05-16
李 娜,张书芮,孙俊红
通讯作者:
孙俊红(1975—),男,教授,博士,主要从事法医病理学研究。E-mail:junhong.sun@sxmu.edu.cn
作者简介:
李娜(1992—),女,讲师,博士,主要从事法医损伤病理学研究。E-mail:lina39 @sxmu.edu.cn
基金资助:
CLC Number:
LI Na, ZHANG Shurui, SUN Junhong. Strategies Driven by Artificial Intelligence Technology for Time Problems in Forensic Pathology[J]. Chinese Journal of Forensic Sciences, 2024(3): 41-48.
李 娜, 张书芮, 孙俊红. 人工智能技术驱动的法医病理时间类问题解决策略[J]. 中国司法鉴定, 2024(3): 41-48.
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[ 1 ] SAJID M,TAJ I A,BAJWA U I,et al. Facial asymmetry-based age group estimation:Role in recognizing age-separated face images[J]. Journal of Forensic Sciences,2018,63(6):1727-1749. [ 2 ] ZHANG K,YAN H,LIU R N,et al. Exploring metabolic alterations associated with death from asphyxia and the differentiation of asphyxia from sudden cardiac death by gc-hrms-based untargeted metabolomics[J]. Journal of Chromatography B,2021,1171:122638. [ 3 ] ŠTEPANOVSKÝ M,IBROVÁ A,BUK Z,et al. Novel age estimation model based on development of permanent teeth compared with classical approach and other modern data mining methods[J]. Forensic Science International,2017,279:72-82. [ 4 ] ZHANG J,ZHOU Y,VIEIRA D N,et al. An efficient method for building a database of diatom populations for drowning site inference using a deep learning algorithm[J]. International Journal of Legal Medicine,2021,135(3):817-827. [ 5 ] 黄平,张吉,邹冬华,等. 数字化与智能化技术在法医病理学鉴定中的研究现状与挑战[J]. 中国司法鉴定,2023(2):35-47. [ 6 ] TOURNOIS L,TROUSSET V,HATSCH D,et al. Artificial intelligence in the practice of forensic medicine:A scoping review[J]. International Journal of Legal Medicine,2024,138(3):1023-1037. [ 7 ] PIRAIANU A I,FULGA A,MUSAT C L,et al. Enhancing the evidence with algorithms:How artificial intelligence is transforming forensic medicine[J]. Diagnostics,2023,13(18):2992. [ 8 ] BREIMAN L. Random forests[J]. Machine Learning,2001,45(1):5-32. [ 9 ] ANGRA S,AHUJA S. Machine learning and its applications:A review[C]//Proceedings of the 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). Washington,D.C.,USA:IEEE Press,2017:23-25. [10] LECUN Y,BENGIO Y,HINTON G. Deep learning[J]. Nature,2015,521(7553):436-444. [11] CASSE J M,MARTRILLE L,VIGNAUD J M,et al. Skin wounds vitality markers in forensic pathology:An updated review[J]. Medicine,Science,and the Law,2016,56(2):128-137. [12] LI N,DU Q X,BAI R F,et al. Vitality and wound-age estimation in forensic pathology:Review and future prospects[J]. Forensic Sciences Research,2018,5(1):15-24. [13] TIRADO J,MAURICIO D. Bruise dating using deep learning[J]. Journal of Forensic Sciences,2021,66(1):336-346. [14] DU Q X,WANG L,LI D,et al. Estimating the time of skeletal muscle contusion based on the spatial distribution of neutrophils:A practical approach to forensic problems[J]. International Journal of Legal Medicine,2022,136(1):149-158. [15] BARINGTON K,JENSEN H E,SKOVGAARD K. Forensic aspects of gene expression signatures for age determination in bruises as evaluated in an experimental porcine model[J]. Forensic Science,Medicine,and Pathology,2017,13(2):151-160. [16] DU Q X,LI N,DANG L H,et al. Temporal expression of wound healing-related genes inform wound age estimation in rats after a skeletal muscle contusion:A multivariate statistical model analysis[J]. International Journal of Legal Medicine,2020,134(1):273-282. [17] DANG L H,FENG N,AN G S,et al. Novel insights into wound age estimation:Combined with “up,no change,or down” system and cosine similarity in python environment[J]. International Journal of Legal Medicine,2020,134(6):2177-2186. [18] DANG L H,LI J,BAI X,et al. Novel prediction method applied to wound age estimation:Developing a stacking ensemble model to improve predictive performance based on multi-mRNA[J]. Diagnostics,2023,13(3):395. [19] REN K,WANG L L,WANG Y F,et al. Wound age estimation based on next-generation sequencing:Fitting the optimal index system using machine learning[J]. Forensic Science International,2022,59:102722. [20] CAO J,AN G S,LI J,et al. Combined metabolomics and tandem machine-learning models for wound age estimation:A novel analytical strategy[J]. Forensic Sciences Research,2023,8(1):50-61. [21] 马星宇,程浩,张忠铎,等.代谢组学技术结合机器学习算法在损伤时间推断中的研究进展[J]. 法医学杂志,2023,39(6):596-600. [22] NAPOLI P E,NIOI M,D'ALOJA E,et al. Post-mortem corneal thickness measurements with a portable optical coherence tomography system:A reliability study[J]. Scientific Reports,2016,6:30428. [23] CANTÜRK İ,ÖZYILMAZ L. A computational approach to estimate postmortem interval using opacity development of eye for human subjects[J]. Computers in Biology and Medicine,2018,98:93-99. [24] ZHANG J,WEI X,HUANG J,et al. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectral prediction of postmortem interval from vitreous humor samples[J]. Analytical and Bioanalytical Chemistry,2018,410(29):7611-7620. [25] METCALF J L,XU Z Z,WEISS S,et al. Microbial community assembly and metabolic function during mammalian corpse decomposition[J]. Science,2016,351(6269):158-162. [26] 赵广众,梁宇,董春楠. 应用法医微生物学推断死亡时间研究进展[J]. 刑事技术,2024,49(1):80-84. [27] METCALF J L,PARFREY L W,GONZALEZ A,et al. A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system[J]. eLife,2013,2:1-19. [28] LIU R N,GU Y X,SHEN M W,et al. Predicting postmortem interval based on microbial community sequences and machine learning algorithms[J]. Environmental Microbiology,2020,22(6):2273-2291. [29] JOHNSON H R,TRINIDAD D D,GUZMAN S,et al. A machine learning approach for using the postmortem skin microbiome to estimate the postmortem interval[J]. PLoS One,2016,11(12):e0167370. [30] LI N,LIANG X R,ZHOU S D,et al. Exploring postmortem succession of rat intestinal microbiome for PMI based on machine learning algorithms and potential use for humans[J]. Forensic Science International Genetics,2023,66:102904. [31] DONALDSON A E,LAMONT I L. Metabolomics of post-mortem blood:Identifying potential markers of post-mortem interval[J]. Metabolomics,2015,11(1):237-245. [32] ZHANG F Y,WANG L L,DONG W W,et al. A preliminary study on early postmortem submersion interval (PMSI) estimation and cause-of-death discrimination based on nontargeted metabolomics and machine learning algorithms[J]. International Journal of Legal Medicine,2022,136(3):941-954. [33] LU X J,LI J,WEI X,et al. A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques[J]. International Journal of Legal Medicine,2023,137(1):237-249. [34] DU Q X,ZHANG S,LONG F H,et al. Combining with lab-on-chip technology and multi-organ fusion strategy to estimate post-mortem interval of rat[J]. Frontiers in Medicine,2022,9:1083474. [35] 吴世豪,王继芬,白海涛,等. 基于昆虫证据推断死亡时间的研究进展[J]. 应用昆虫学报,2022,59(3):469-488. [36] 任立品,尚艳杰,郭亚东. 昆虫学证据在法庭科学中的应用及进展[J]. 法医学杂志,2021,37(3):295-304. [37] WANG Y,WANG Y H,WANG M,et al. Forensic entomology in China and its challenges[J]. Insects,2021,12(3):230. [38] 王禹,廖明庆,王颖慧,等. 应用嗜尸性昆虫推断死亡时间11例[J]. 法医学杂志,2021,37(3):332-337. [39] 沈肖,廖良柱,旦增晋美,等. 高原地区利用法医昆虫学推断死亡时间2例[J]. 刑事技术,2022,47(6):652-656. [40] BUTCHER J B,MOORE H E,DAY C R,et al. Artificial neural network analysis of hydrocarbon profiles for the ageing of Lucilia sericata for post mortem interval estimation[J]. Forensic Science International,2013,232(1/2/3):25-31. [41] MOORE H E,BUTCHER J B,DAY C R,et al. Adult fly age estimations using cuticular hydrocarbons and artificial neural networks in forensically important calliphoridae species[J]. Forensic Science International,2017,280:233-244. [42] 李奕. 法医昆虫学PMI推断相关软件开发及应用[D]. 长沙:中南大学,2023. [43] 夏鹏,彭谨,刘振江,等. 基于集成学习技术对死亡时间的研究[J]. 中国法医学杂志,2022,37(4):323-326. [44] BREMMER R H,DE BRUIN K G,VAN GEMERT M J C,et al. Forensic quest for age determination of bloodstains[J]. Forensic Science International,2012,216(1/2/3):1-11. [45] 杨志超,赵森,蔡竞,等. 基于拉曼光谱的血液遗留时间研究与模型预测[J]. 中国法医学杂志,2022,37(1):61-64. [46] TSUTSUMI A,YAMAMOTO Y,ISHIZU H. Determination of the age of bloodstains by enzyme activities in blood cells[J]. Nihon Hoigaku Zasshi,1983,37(6):770-776. [47] BAUER M,POLZIN S,PATZELT D. Quantification of RNA degradation by semi-quantitative duplex and competitive RT-PCR:A possible indicator of the age of bloodstains?[J]. Forensic Science International,2003,138(1/2/3):94-103. [48] FU J,ALLEN R W. A method to estimate the age of bloodstains using quantitative PCR[J]. Forensic Science International Genetics,2019,39:103-108. [49] 孙婷怡,刘良,杨天潼,等. 家兔死后离体血液ATP含量变化与放置时间的关系[J]. 中国法医学杂志,2012,27(2):93-96. [50] 冯颖,蔡竞. 基于高光谱成像技术的不同介质血迹陈旧度研究[J]. 激光与光电子学进展,2020,57(5):279-284. [51] LEE S,MUN S,LEE Y R,et al. Validation of the metabolite ergothioneine as a forensic marker in bloodstains[J]. Molecules,2022,27(24):8885. [52] GIULIETTI N,DISCEPOLO S,CASTELLINI P,et al. Neural network based hyperspectral imaging for substrate independent bloodstain age estimation[J]. Forensic Science International,2023,349:111742. [53] MARRONE A,LA RUSSA D,MONTESANTO A,et al. Short and long time bloodstains age determination by colorimetric analysis:A pilot study[J]. Molecules,2021,26(20):6272. [54] SEOK A E,LEE J,LEE Y R,et al. Estimation of age of bloodstains by mass-spectrometry:A metabolomic app-roach[J]. Analytical Chemistry,2018,90(21):12431-12441. [55] WORLEY B,HALOUSKA S,POWERS R. Utilities for quantifying separation in PCA/PLS-DA scores plots[J]. Analytical Biochemistry,2013,433(2):102-104. [56] ROHART F,GAUTIER B,SINGH A,et al. Mixomics:An R package for ’omics feature selection and multiple data integration[J]. PLoS Computational Biology,2017,13(11):e1005752. [57] RUIZ-PEREZ D,GUAN H,MADHIVANAN P,et al. So you think you can PLS-DA?[J]. BMC Bioinformatics,2020,21(S1):2. [58] LI H Y,SHEN C,WANG G J,et al. BloodNet:An attention-based deep network for accurate,efficient,and costless bloodstain time since deposition inference[J]. Briefings in Bioinformatics,2023,24(1):557. [59] BREMMER R H,DE BRUIN D M,DE JOODE M,et al. Biphasic oxidation of oxy-hemoglobin in bloodstains[J]. PLoS One,2011,6(7):e21845. [60] MOLLER P. Studien uber die embolische und autochtone thrombose in der arteria pulmonalis[J]. Beiträge Pathol Anat,1923,71:26. [61] LEU A J,LEU H J. Special problems in the histologic age determination of thrombi and emboli[J]. Der Pathologe,1989,10(2):87-92. [62] IRNIGER W. Histologische altersbestimmung von thrombosen und embolien[J]. Virchows Archiv für Pathologische Anatomie und Physiologie und für klinische Medizin,1963,336(3):220-237. [63] 高卫民,林羽赫,曹志鹏,等. 26例肺动脉血栓栓塞的法医学分析[J]. 中国法医学杂志,2011,26(05):409-410. [64] DI FAZIO N,DELOGU G,CIALLELLA C,et al. State-of-art in the age determination of venous thromboembolism:A systematic review[J]. Diagnostics,2021,11(12):2397. [65] 杨琛腾,左敏,王松军,等. 血栓形成时间的推断[J]. 法医学杂志,2018,34(4):352-358. [66] FINESCHI V,TURILLAZZI E,NERI M,et al. Histological age determination of venous thrombosis:A neglected forensic task in fatal pulmonary thrombo-embolism[J]. Forensic Science International,2009,186(1/2/3):22-28. [67] NOSAKA M,ISHIDA Y,KIMURA A,et al. Time-dependent appearance of intrathrombus neutrophils and macrophages in a stasis-induced deep vein thrombosis model and its application to thrombus age determination[J]. International Journal of Legal Medicine,2009,123(3):235-240. [68] JIN Q Q,SUN J H,DU Q X,et al. Integrating microRNA and messenger RNA expression profiles in a rat model of deep vein thrombosis[J]. International Journal of Molecular Medicine,2017,40(4):1019-1028. [69] CAO J,JIN Q Q,WANG G M,et al. Comparison of the serum metabolic signatures based on 1H NMR between patients and a rat model of deep vein thrombosis[J]. Scientific Reports,2018,8(1):7837. [70] 任奎,孟泉润,闫守琨,等. 人工智能模型数据泄露的攻击与防御研究综述[J]. 网络与信息安全学报,2021,7(1):1-10. [71] 肖雄,唐卓,肖斌,等. 联邦学习的隐私保护与安全防御研究综述[J]. 计算机学报,2023,46(5):1019-1044. |
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