Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning
Objective The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice. Methods ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cas...
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| Veröffentlicht in: | International journal of legal medicine 2024-05, Vol.138 (3), p.1139-1148 |
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| container_issue | 3 |
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| container_title | International journal of legal medicine |
| container_volume | 138 |
| creator | Zhang, Xiangyan Xiao, Jiao Yang, Fengqin Qu, Hongke Ye, Chengxin Chen, Sile Guo, Yadong |
| description | Objective
The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice.
Methods
ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cases, combined with pathological changes. Mixed datasets were analyzed using ANN, KNN, RF, and SVM algorithms. Evaluation metrics such as accuracy, precision, recall, F1-score and confusion matrix were used to select the optimal algorithm and construct the postmortem diagnosis model for SCD.
Results
A total of 77 cases were collected, including 43 cases in the SCD group and 34 cases in the non-SCD group. A total of 693 spectrogram were obtained. Compared to other algorithms, the SVM algorithm demonstrated the highest accuracy, reaching 95.83% based on spectral biomarkers. Furthermore, by combing spectral biomarkers with age, gender, and cardiac histopathological changes, the accuracy of the SVM model could get 100%.
Conclusion
Integrating artificial intelligence technology, pathology, and physical chemistry analysis of blood components can serve as an effective auxiliary method for postmortem diagnosis of SCD. |
| doi_str_mv | 10.1007/s00414-023-03118-7 |
| format | Article |
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The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice.
Methods
ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cases, combined with pathological changes. Mixed datasets were analyzed using ANN, KNN, RF, and SVM algorithms. Evaluation metrics such as accuracy, precision, recall, F1-score and confusion matrix were used to select the optimal algorithm and construct the postmortem diagnosis model for SCD.
Results
A total of 77 cases were collected, including 43 cases in the SCD group and 34 cases in the non-SCD group. A total of 693 spectrogram were obtained. Compared to other algorithms, the SVM algorithm demonstrated the highest accuracy, reaching 95.83% based on spectral biomarkers. Furthermore, by combing spectral biomarkers with age, gender, and cardiac histopathological changes, the accuracy of the SVM model could get 100%.
Conclusion
Integrating artificial intelligence technology, pathology, and physical chemistry analysis of blood components can serve as an effective auxiliary method for postmortem diagnosis of SCD.</description><identifier>ISSN: 0937-9827</identifier><identifier>EISSN: 1437-1596</identifier><identifier>DOI: 10.1007/s00414-023-03118-7</identifier><identifier>PMID: 38047927</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accuracy ; Algorithms ; Artificial Intelligence ; Ataxia Telangiectasia Mutated Proteins ; Biomarkers ; Blood ; Death, Sudden, Cardiac ; Diagnosis ; Forensic chemistry ; Forensic Medicine ; Forensic pathology ; Fourier transforms ; Humans ; Infrared spectroscopy ; Machine Learning ; Medical Law ; Medicine ; Medicine & Public Health ; Original Article ; Physical chemistry ; Spectroscopy, Fourier Transform Infrared - methods ; Support vector machines</subject><ispartof>International journal of legal medicine, 2024-05, Vol.138 (3), p.1139-1148</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c326t-bb06f30be6114d72192714143adb1a3723525f1ca30c41f28185dd97f302da8c3</cites><orcidid>0000-0001-7468-8061</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00414-023-03118-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00414-023-03118-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38047927$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Xiangyan</creatorcontrib><creatorcontrib>Xiao, Jiao</creatorcontrib><creatorcontrib>Yang, Fengqin</creatorcontrib><creatorcontrib>Qu, Hongke</creatorcontrib><creatorcontrib>Ye, Chengxin</creatorcontrib><creatorcontrib>Chen, Sile</creatorcontrib><creatorcontrib>Guo, Yadong</creatorcontrib><title>Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning</title><title>International journal of legal medicine</title><addtitle>Int J Legal Med</addtitle><addtitle>Int J Legal Med</addtitle><description>Objective
The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice.
Methods
ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cases, combined with pathological changes. Mixed datasets were analyzed using ANN, KNN, RF, and SVM algorithms. Evaluation metrics such as accuracy, precision, recall, F1-score and confusion matrix were used to select the optimal algorithm and construct the postmortem diagnosis model for SCD.
Results
A total of 77 cases were collected, including 43 cases in the SCD group and 34 cases in the non-SCD group. A total of 693 spectrogram were obtained. Compared to other algorithms, the SVM algorithm demonstrated the highest accuracy, reaching 95.83% based on spectral biomarkers. Furthermore, by combing spectral biomarkers with age, gender, and cardiac histopathological changes, the accuracy of the SVM model could get 100%.
Conclusion
Integrating artificial intelligence technology, pathology, and physical chemistry analysis of blood components can serve as an effective auxiliary method for postmortem diagnosis of SCD.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Ataxia Telangiectasia Mutated Proteins</subject><subject>Biomarkers</subject><subject>Blood</subject><subject>Death, Sudden, Cardiac</subject><subject>Diagnosis</subject><subject>Forensic chemistry</subject><subject>Forensic Medicine</subject><subject>Forensic pathology</subject><subject>Fourier transforms</subject><subject>Humans</subject><subject>Infrared spectroscopy</subject><subject>Machine Learning</subject><subject>Medical Law</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Original Article</subject><subject>Physical chemistry</subject><subject>Spectroscopy, Fourier Transform Infrared - methods</subject><subject>Support vector machines</subject><issn>0937-9827</issn><issn>1437-1596</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kUFv3CAQhVHVqNkk_QM9VEi99OJmBrDBxyjKNitFqhRtzggDzjqyYQv2If8-pLttpRxyAjHfezPDI-QLwg8EkJcZQKCogPEKOKKq5AeyQsFlhXXbfCQraMu9VUyekrOcnwBQNrL-RE65AiFbJldkt3E-zEM_WDMPMdDY07y48katSW4wljpv5h3tU5zobplMoN0Yo6NLHsIjvdreV-vt5p7mvbdzitnG_TM1wdHJ2N0QPB29SaGgF-SkN2P2n4_nOXlY32yvb6u7Xz8311d3leWsmauug6bn0PkGUTjJsEyJZUtuXIeGS8ZrVvdoDQcrsGcKVe1cK4uGOaMsPyffD777FH8vPs96GrL142iCj0vWTLVSqAZrKOi3N-hTXFIo02kOXCiBrFaFYgfKlvVy8r3ep2Ey6Vkj6Ncc9CEHXXLQf3LQsoi-Hq2XbvLun-TvxxeAH4BcSuHRp_-937F9AXqzkdg</recordid><startdate>20240501</startdate><enddate>20240501</enddate><creator>Zhang, Xiangyan</creator><creator>Xiao, Jiao</creator><creator>Yang, Fengqin</creator><creator>Qu, Hongke</creator><creator>Ye, Chengxin</creator><creator>Chen, Sile</creator><creator>Guo, Yadong</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K7.</scope><scope>K9.</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-7468-8061</orcidid></search><sort><creationdate>20240501</creationdate><title>Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning</title><author>Zhang, Xiangyan ; Xiao, Jiao ; Yang, Fengqin ; Qu, Hongke ; Ye, Chengxin ; Chen, Sile ; Guo, Yadong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c326t-bb06f30be6114d72192714143adb1a3723525f1ca30c41f28185dd97f302da8c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Ataxia Telangiectasia Mutated Proteins</topic><topic>Biomarkers</topic><topic>Blood</topic><topic>Death, Sudden, Cardiac</topic><topic>Diagnosis</topic><topic>Forensic chemistry</topic><topic>Forensic Medicine</topic><topic>Forensic pathology</topic><topic>Fourier transforms</topic><topic>Humans</topic><topic>Infrared spectroscopy</topic><topic>Machine Learning</topic><topic>Medical Law</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Original Article</topic><topic>Physical chemistry</topic><topic>Spectroscopy, Fourier Transform Infrared - methods</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Xiangyan</creatorcontrib><creatorcontrib>Xiao, Jiao</creatorcontrib><creatorcontrib>Yang, Fengqin</creatorcontrib><creatorcontrib>Qu, Hongke</creatorcontrib><creatorcontrib>Ye, Chengxin</creatorcontrib><creatorcontrib>Chen, Sile</creatorcontrib><creatorcontrib>Guo, Yadong</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Criminal Justice (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>International journal of legal medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Xiangyan</au><au>Xiao, Jiao</au><au>Yang, Fengqin</au><au>Qu, Hongke</au><au>Ye, Chengxin</au><au>Chen, Sile</au><au>Guo, Yadong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning</atitle><jtitle>International journal of legal medicine</jtitle><stitle>Int J Legal Med</stitle><addtitle>Int J Legal Med</addtitle><date>2024-05-01</date><risdate>2024</risdate><volume>138</volume><issue>3</issue><spage>1139</spage><epage>1148</epage><pages>1139-1148</pages><issn>0937-9827</issn><eissn>1437-1596</eissn><abstract>Objective
The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice.
Methods
ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cases, combined with pathological changes. Mixed datasets were analyzed using ANN, KNN, RF, and SVM algorithms. Evaluation metrics such as accuracy, precision, recall, F1-score and confusion matrix were used to select the optimal algorithm and construct the postmortem diagnosis model for SCD.
Results
A total of 77 cases were collected, including 43 cases in the SCD group and 34 cases in the non-SCD group. A total of 693 spectrogram were obtained. Compared to other algorithms, the SVM algorithm demonstrated the highest accuracy, reaching 95.83% based on spectral biomarkers. Furthermore, by combing spectral biomarkers with age, gender, and cardiac histopathological changes, the accuracy of the SVM model could get 100%.
Conclusion
Integrating artificial intelligence technology, pathology, and physical chemistry analysis of blood components can serve as an effective auxiliary method for postmortem diagnosis of SCD.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>38047927</pmid><doi>10.1007/s00414-023-03118-7</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-7468-8061</orcidid></addata></record> |
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| subjects | Accuracy Algorithms Artificial Intelligence Ataxia Telangiectasia Mutated Proteins Biomarkers Blood Death, Sudden, Cardiac Diagnosis Forensic chemistry Forensic Medicine Forensic pathology Fourier transforms Humans Infrared spectroscopy Machine Learning Medical Law Medicine Medicine & Public Health Original Article Physical chemistry Spectroscopy, Fourier Transform Infrared - methods Support vector machines |
| title | Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning |
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