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Li D, Zhang J, Guo W, Ma K, Qin Z, Zhang J, Chen L, Xiong L, Huang J, Wan C, Huang P. A diagnostic strategy for pulmonary fat embolism based on routine H&E staining using computational pathology. Int J Legal Med 2024; 138:849-858. [PMID: 37999766 DOI: 10.1007/s00414-023-03136-5] [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: 07/29/2023] [Accepted: 11/11/2023] [Indexed: 11/25/2023]
Abstract
Pulmonary fat embolism (PFE) as a cause of death often occurs in trauma cases such as fractures and soft tissue contusions. Traditional PFE diagnosis relies on subjective methods and special stains like oil red O. This study utilizes computational pathology, combining digital pathology and deep learning algorithms, to precisely quantify fat emboli in whole slide images using conventional hematoxylin-eosin (H&E) staining. The results demonstrate deep learning's ability to identify fat droplet morphology in lung microvessels, achieving an area under the receiver operating characteristic (ROC) curve (AUC) of 0.98. The AI-quantified fat globules generally matched the Falzi scoring system with oil red O staining. The relative quantity of fat emboli against lung area was calculated by the algorithm, determining a diagnostic threshold of 8.275% for fatal PFE. A diagnostic strategy based on this threshold achieved a high AUC of 0.984, similar to manual identification with special stains but surpassing H&E staining. This demonstrates computational pathology's potential as an affordable, rapid, and precise method for fatal PFE diagnosis in forensic practice.
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Affiliation(s)
- Dechan Li
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China
| | - Ji Zhang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China
| | - Wenqing Guo
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China
- Department of Forensic Pathology, Shanxi Medical University, Taiyuan, China
| | - Kaijun Ma
- Shanghai Key Laboratory of Crime Scene Evidence, Institute of Criminal Science and Technology, Shanghai Municipal Public Security Bureau, Shanghai, China
| | - Zhiqiang Qin
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China
| | - Jianhua Zhang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China
| | - Liqin Chen
- Department of Forensic Medicine, Inner Mongolia Medical University, Hohhot, China
| | - Ling Xiong
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jiang Huang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China.
| | - Changwu Wan
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China.
| | - Ping Huang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China.
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China.
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2
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Wu Y, Luo L, Li Y, Sun Y, Huang X, Zhou Y, Wang Y, Wang Y, Zeng D, Yun L. A comparative assessment of time-consuming and laborious diatom analysis:Brief experimentation with suggestion of automatic identification. Forensic Sci Int 2024; 355:111939. [PMID: 38246065 DOI: 10.1016/j.forsciint.2024.111939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 11/02/2023] [Accepted: 01/14/2024] [Indexed: 01/23/2024]
Abstract
Diatom testing is considered a useful method for providing supportive evidence for the diagnosis of drowning in forensic pathology. However, various factors remain controversial for recognizing diatoms, such as being time-consuming and laborious and influencing the consistency of the results. Given the absence of precise and well-defined studies on this subject, this study aimed to determine the relationship between the ability to identify diatoms and researchers with different technical backgrounds. A total of 55 samples from 18 cases, including water, lungs, liver, and kidneys, were treated using the microwave digestion-vacuum filtration-automated scanning electron microscopy (MD-VF-Auto SEM), which was used to compare diatom analyses among three groups of well-trained forensic pathologists (FPs), trained junior employees (JEs), and new trainees (TEs). In addition to achieving similar accuracy of positive findings from drowning cases, counting efficiency was evaluated based on taxonomy records and counting time after viewing more than 5500 diatom images. In contrast to the higher counting efficiency of the JE group than that of the TE group, we observed a statistically significant difference (p < 0.05) in the diatom classification between these two groups. Based on our experiments, an efficient analysis for automatically identifying and classifying diatoms is urgently required.
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Affiliation(s)
- Yuhang Wu
- Department of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, People' s Republic of China
| | - Lisiteng Luo
- Department of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, People' s Republic of China
| | - Yuyang Li
- Department of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, People' s Republic of China
| | - Yuntao Sun
- Department of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, People' s Republic of China
| | - Xinyu Huang
- Department of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, People' s Republic of China
| | - Yuchi Zhou
- Criminal Investigation Department of Sichuan Provincial Public Security Bureau, NO.36, Wenmiaohoujie Street, Qingyang district, Chengdu 610015, People's Republic of China
| | - Yi Wang
- Criminal Investigation Department of Sichuan Provincial Public Security Bureau, NO.36, Wenmiaohoujie Street, Qingyang district, Chengdu 610015, People's Republic of China
| | - Yongqing Wang
- Criminal Science and Technology Division, Criminal Investigation Bureau, Chengdu Public Security Bureau, Chengdu, Sichuan 610031, People's Republic of China
| | - Debing Zeng
- Criminal Science and Technology Division, Criminal Investigation Bureau, Chengdu Public Security Bureau, Chengdu, Sichuan 610031, People's Republic of China
| | - Libing Yun
- Department of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, People' s Republic of China.
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Wu Z, Guo Y, Hayakawa M, Yang W, Lu Y, Ma J, Li L, Li C, Liu Y, Niu J. Artificial intelligence-driven microbiome data analysis for estimation of postmortem interval and crime location. Front Microbiol 2024; 15:1334703. [PMID: 38314433 PMCID: PMC10834752 DOI: 10.3389/fmicb.2024.1334703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/08/2024] [Indexed: 02/06/2024] Open
Abstract
Microbial communities, demonstrating dynamic changes in cadavers and the surroundings, provide invaluable insights for forensic investigations. Conventional methodologies for microbiome sequencing data analysis face obstacles due to subjectivity and inefficiency. Artificial Intelligence (AI) presents an efficient and accurate tool, with the ability to autonomously process and analyze high-throughput data, and assimilate multi-omics data, encompassing metagenomics, transcriptomics, and proteomics. This facilitates accurate and efficient estimation of the postmortem interval (PMI), detection of crime location, and elucidation of microbial functionalities. This review presents an overview of microorganisms from cadavers and crime scenes, emphasizes the importance of microbiome, and summarizes the application of AI in high-throughput microbiome data processing in forensic microbiology.
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Affiliation(s)
- Ze Wu
- Department of Dermatology, General Hospital of Northern Theater Command, Shenyang, China
| | - Yaoxing Guo
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, Shenyang, China
- National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Miren Hayakawa
- Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Wei Yang
- Department of Dermatology, General Hospital of Northern Theater Command, Shenyang, China
| | - Yansong Lu
- Department of Dermatology, General Hospital of Northern Theater Command, Shenyang, China
| | - Jingyi Ma
- Department of Dermatology, General Hospital of Northern Theater Command, Shenyang, China
| | - Linghui Li
- Department of Dermatology, General Hospital of Northern Theater Command, Shenyang, China
| | - Chuntao Li
- Department of Dermatology, General Hospital of Northern Theater Command, Shenyang, China
| | - Yingchun Liu
- Department of Dermatology, General Hospital of Northern Theater Command, Shenyang, China
| | - Jun Niu
- Department of Dermatology, General Hospital of Northern Theater Command, Shenyang, China
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Su Q, Yang C, Chen L, She Y, Xu Q, Zhao J, Liu C, Sun H. Inference of drowning sites using bacterial composition and random forest algorithm. Front Microbiol 2023; 14:1213271. [PMID: 37440892 PMCID: PMC10335767 DOI: 10.3389/fmicb.2023.1213271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 05/26/2023] [Indexed: 07/15/2023] Open
Abstract
Diagnosing the drowning site is a major challenge in forensic practice, particularly when corpses are recovered from flowing rivers. Recently, forensic experts have focused on aquatic microorganisms, including bacteria, which can enter the bloodstream during drowning and may proliferate in corpses. The emergence of 16S ribosomal RNA gene (16S rDNA) amplicon sequencing has provided a new method for analyzing bacterial composition and has facilitated the development of forensic microbiology. We propose that 16S rDNA amplicon sequencing could be a useful tool for inferring drowning sites. Our study found significant differences in bacterial composition in different regions of the Guangzhou section of the Pearl River, which led to differences in bacteria of drowned rabbit lungs at different drowning sites. Using the genus level of bacteria in the lung tissue of drowned rabbits, we constructed a random forest model that accurately predicted the drowning site in a test set with 100% accuracy. Furthermore, we discovered that bacterial species endemic to the water were not always present in the corresponding drowned lung tissue. Our findings demonstrate the potential of a random forest model based on bacterial genus and composition in drowned lung tissues for inferring drowning sites.
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Affiliation(s)
- Qin Su
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- Guangzhou Forensic Science Institute, Guangzhou, China
| | - Chengliang Yang
- School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Ling Chen
- School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Yiqing She
- Guangzhou Municipal Public Security Bureau, Guangzhou, China
| | - Quyi Xu
- Guangzhou Forensic Science Institute, Guangzhou, China
| | - Jian Zhao
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- Guangzhou Forensic Science Institute, Guangzhou, China
| | - Chao Liu
- School of Forensic Medicine, Southern Medical University, Guangzhou, China
- National Anti-Drug Laboratory Guangdong Regional Center, Guangzhou, China
| | - Hongyu Sun
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou, China
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Zhang J, Vieira DN, Cheng Q, Zhu Y, Deng K, Zhang J, Qin Z, Sun Q, Zhang T, Ma K, Zhang X, Huang P. DiatomNet v1.0: A novel approach for automatic diatom testing for drowning diagnosis in forensically biomedical application. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 232:107434. [PMID: 36871544 DOI: 10.1016/j.cmpb.2023.107434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 09/11/2022] [Accepted: 02/18/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Diatom testing is supportive for drowning diagnosis in forensic medicine. However, it is very time-consuming and labor-intensive for technicians to identify microscopically a handful of diatoms in sample smears, especially under complex observable backgrounds. Recently, we successfully developed a software, named DiatomNet v1.0 intended to automatically identify diatom frustules in a whole slide under a clear background. Here, we introduced this new software and performed a validation study to elucidate how DiatomNet v1.0 improved its performance with the influence of visible impurities. METHODS DiatomNet v1.0 has an intuitive, user-friendly and easy-to-learn graphical user interface (GUI) built in the Drupal and its core architecture for slide analysis including a convolutional neural network (CNN) is written in Python language. The build-in CNN model was evaluated for diatom identification under very complex observable backgrounds with mixtures of common impurities, including carbon pigments and sand sediments. Compared to the original model, the enhanced model following optimization with limited new datasets was evaluated systematically by independent testing and random control trials (RCTs). RESULTS In independent testing, the original DiatomNet v1.0 was moderately affected, especially when higher densities of impurities existed, and achieved a low recall of 0.817 and F1 score of 0.858 but good precision of 0.905. Following transfer learning with limited new datasets, the enhanced version had better results, with recall and F1 score values of 0.968. A comparative study on real slides showed that the upgraded DiatomNet v1.0 obtained F1 scores of 0.86 and 0.84 for carbon pigment and sand sediment, respectively, slightly worse than manual identification (carbon pigment: 0.91; sand sediment: 0.86), but much less time was needed. CONCLUSIONS The study verified that forensic diatom testing with aid of DiatomNet v1.0 is much more efficient than traditionally manual identification even under complex observable backgrounds. In terms of forensic diatom testing, we proposed a suggested standard on build-in model optimization and evaluation to strengthen the software's generalization in potentially complex conditions.
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Affiliation(s)
- Ji Zhang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, P.R. China
| | - Duarte Nuno Vieira
- Department of Forensic Medicine, Ethics and Medical Law, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Qi Cheng
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, P.R. China
| | - Yongzheng Zhu
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, P.R. China
| | - Kaifei Deng
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, P.R. China
| | - Jianhua Zhang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, P.R. China
| | - Zhiqiang Qin
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, P.R. China
| | - Qiran Sun
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, P.R. China
| | - Tianye Zhang
- Shanghai Key Laboratory of Crime Scene Evidence, Institute of Criminal Science and Technology, Shanghai Municipal Public Security Bureau, Shanghai, P.R. China
| | - Kaijun Ma
- Shanghai Key Laboratory of Crime Scene Evidence, Institute of Criminal Science and Technology, Shanghai Municipal Public Security Bureau, Shanghai, P.R. China.
| | - Xiaofeng Zhang
- School of Medicine, Shanghai University, Shanghai, P.R. China.
| | - Ping Huang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, P.R. China.
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Hagen D, Pittner S, Zhao J, Obermayer A, Stoiber W, Steinbacher P, Monticelli FC, Gotsmy W. Validation and optimization of the diatom L/D ratio as a diagnostic marker for drowning. Int J Legal Med 2023; 137:939-948. [PMID: 36869250 PMCID: PMC10085902 DOI: 10.1007/s00414-023-02970-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 02/08/2023] [Indexed: 03/05/2023]
Abstract
If a dead body is discovered in water, it nearly always raises the question about the cause of death, often associated with the persistent problem to differentiate between a drowning incident and post-mortem immersion. In numerous cases, a reliable confirmation of death by drowning is often only possible by a combination of diagnoses obtained from autopsy and additional investigations. As to the latter, the use of diatoms has been suggested (and debated) since decades. Based on the consideration that diatoms are present in almost every natural waterbody and are unavoidably incorporated when water is inhaled, their presence in the lung and other tissues can provide evidence of drowning. However, the traditional diatom test methods are still subject of controversial discussion and suspected of erroneous outcome, predominantly through contamination. A promising alternative to minimize the risk of erroneous outcome seems to be disclosed by the recently suggested MD-VF-Auto SEM technique. Especially the establishment of a new diagnostic marker (L/D ratio), which represents the factorial proportion between the diatom concentration in lung tissue and the drowning medium, allows for clearer distinction of drowning and post-mortal immersion and is largely robust to contamination. However, this highly elaborated technique requires specific devices which are frequently unavailable. We therefore developed a modified method of SEM-based diatom testing to enable the use on more routinely available equipment. Process steps such as digestion, filtration, and image acquisition were thoroughly broken down, optimized, and ultimately validated in five confirmed drowning cases. Taking certain limitations into consideration, L/D ratio analysis provided promising results, even in cases of advanced decomposition. We conclude that our modified protocol indeed opens a way for a broader use of the method in forensic drowning investigation.
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Affiliation(s)
- Dominik Hagen
- Department of Ecology and Evolution, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Stefan Pittner
- Department of Forensic Medicine and Forensic Psychiatry, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Jian Zhao
- Guangzhou Forensic Science Institute & Key Laboratory of Forensic Pathology, Ministry of Public Security, Guangzhou, China
| | - Astrid Obermayer
- Department of Ecology and Evolution, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Walter Stoiber
- Department of Ecology and Evolution, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Peter Steinbacher
- Department of Ecology and Evolution, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Fabio C Monticelli
- Department of Forensic Medicine and Forensic Psychiatry, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Walther Gotsmy
- Department of Forensic Medicine and Forensic Psychiatry, Paris-Lodron University of Salzburg, Salzburg, Austria.
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Yu W, Xiang Q, Hu Y, Du Y, Kang X, Zheng D, Shi H, Xu Q, Li Z, Niu Y, Liu C, Zhao J. An improved automated diatom detection method based on YOLOv5 framework and its preliminary study for taxonomy recognition in the forensic diatom test. Front Microbiol 2022; 13:963059. [PMID: 36060761 PMCID: PMC9437702 DOI: 10.3389/fmicb.2022.963059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
The diatom test is a forensic technique that can provide supportive evidence in the diagnosis of drowning but requires the laborious observation and counting of diatoms using a microscopy with too much effort, and therefore it is promising to introduce artificial intelligence (AI) to make the test process automatic. In this article, we propose an artificial intelligence solution based on the YOLOv5 framework for the automatic detection and recognition of the diatom genera. To evaluate the performance of this AI solution in different scenarios, we collected five lab-grown diatom genera and samples of some organic tissues from drowning cases to investigate the potential upper/lower limits of the capability in detecting the diatoms and recognizing their genera. Based on the study of the article, a recall score of 0.95 together with the corresponding precision score of 0.9 were achieved on the samples of the five lab-grown diatom genera via cross-validation, and the accuracy of the evaluation in the cases of kidney and liver is above 0.85 based on the precision and recall scores, which demonstrate the effectiveness of the AI solution to be used in drowning forensic routine.
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Affiliation(s)
- Weimin Yu
- Jiangsu JITRI Sioux Technologies Co., Ltd., Suzhou, China
| | - Qingqing Xiang
- School of Forensic Medicine, Kunming Medical University, Kunming, China
| | - Yingchao Hu
- LabWorld (Suzhou) Intelligent Technology Co., Ltd., Suzhou, China
| | - Yukun Du
- School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Xiaodong Kang
- Key Laboratory of Forensic Pathology, Guangzhou Forensic Science Institute, Ministry of Public Security, Guangzhou, China
| | - Dongyun Zheng
- Key Laboratory of Forensic Pathology, Guangzhou Forensic Science Institute, Ministry of Public Security, Guangzhou, China
| | - He Shi
- Key Laboratory of Forensic Pathology, Guangzhou Forensic Science Institute, Ministry of Public Security, Guangzhou, China
| | - Quyi Xu
- Key Laboratory of Forensic Pathology, Guangzhou Forensic Science Institute, Ministry of Public Security, Guangzhou, China
| | - Zhigang Li
- Key Laboratory of Forensic Pathology, Guangzhou Forensic Science Institute, Ministry of Public Security, Guangzhou, China
| | - Yong Niu
- Section of Forensic Sciences, Department of Criminal Investigation, Ministry of Public Security, Beijing, China
- *Correspondence: Yong Niu
| | - Chao Liu
- Key Laboratory of Forensic Pathology, Guangzhou Forensic Science Institute, Ministry of Public Security, Guangzhou, China
- Chao Liu
| | - Jian Zhao
- Key Laboratory of Forensic Pathology, Guangzhou Forensic Science Institute, Ministry of Public Security, Guangzhou, China
- Jian Zhao
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Han M, Du S, Ge Y, Zhang D, Chi Y, Long H, Yang J, Yang Y, Xin J, Chen T, Zheng N, Guo YC. With or without human interference for precise age estimation based on machine learning? Int J Legal Med 2022; 136:821-831. [DOI: 10.1007/s00414-022-02796-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 02/08/2022] [Indexed: 11/29/2022]
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