Evidence map›Paper›PMID 38047927›Full record

ArticleInternational journal of legal medicine2024

Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning.

Xiangyan Zhang, Jiao Xiao, Fengqin Yang, Hongke Qu, Chengxin Ye, Sile Chen, Yadong Guo

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Article in International journal of legal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.1field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 6 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Xiangyan ZhangDepartment of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China.
Jiao XiaoDepartment of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China.
Fengqin YangDepartment of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China.
Hongke QuKey Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute and School of Basic Medicine Sciences, Central South University, Changsha, Hunan, China.
Chengxin YeDepartment of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China.
Sile ChenDepartment of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China.
Yadong GuoDepartment of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha, China. gdy82@126.com.ORCID http://orcid.org/0000-0001-7468-8061
Central South University · CN

Funding

Fundamental Research Funds for Central Universities of the Central South University 2023ZZTS0546National Natural Science Foundation of China 82072114
6 · The paper itself

Abstract

objectiveThe 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.

methodsATR-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.

resultsA 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%.

conclusionIntegrating artificial intelligence technology, pathology, and physical chemistry analysis of blood components can serve as an effective auxiliary method for postmortem diagnosis of SCD.

Indexed as

AlgorithmsArtificial IntelligenceAtaxia Telangiectasia Mutated ProteinsBiomarkersDeath, Sudden, CardiacHumansMachine LearningSpectroscopy, Fourier Transform InfraredAtaxia Telangiectasia Mutated ProteinsATR protein, humanBiomarkersATR-FTIR spectroscopyMachine learningPostmortem diagnosisSCD

Identifiers

PMID38047927
OpenAlexW4389299036

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.