Determination of causes of death via spectrochemical analysis of forensic autopsies‐based pulmonary edema fluid samples with deep learning algorithm

Lin, Hancheng; Luo, Yang; Sun, Qiran; Deng, Kaifei; Chen, Yijiu; Wang, Zhenyuan; Huang, Ping

doi:10.1002/jbio.201960144

Cited by 14 publications

(6 citation statements)

References 42 publications

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“…Yue et al [47] compared the multilayer perceptron (MLP), long short-term memory network and CNN deep learning models regarding the diagnosis of abnormal thyroid function, and the results revealed that the CNN performed best with an accuracy rate of 96.5%. Lin et al [48] proved that a CNN-based deep learning framework outperformed other machine learning algorithms in establishing classifiers to determine causes of death by analyzing pulmonary edema fluid from forensic autopsies. However, according to investigation, most of the application studies based on CNN deep learning models focused on 1D vibrational spectroscopy [49][50][51][52].…”

Section: Discussionmentioning

confidence: 99%

Grade classification of human glioma using a convolutional neural network based on mid‐infrared spectroscopy mapping

Peng

Chen²,

Kong

et al. 2022

Journal of Biophotonics

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This study proposes a convolutional neural network (CNN)‐based computer‐aided diagnosis (CAD) system for the grade classification of human glioma by using mid‐infrared (MIR) spectroscopic mappings. Through data augmentation of pixels recombination, the mappings in the training set increased almost 161 times relative to the original mappings. The pixels of the recombined mappings in the training set came from all of the one‐dimensional (1D) vibrational spectroscopy of 62 (almost 80% of all 77 patients) patients at specific bands. Compared with the performance of the CNN‐CAD system based on the 1D vibrational spectroscopy, we found that the mean diagnostic accuracy of the recombined MIR spectroscopic mappings at peaks of 2917 cm−1, 1539 cm−1 and 1234 cm−1 on the test set performed higher and the model also had more stable patterns. This research demonstrates that two‐dimensional MIR mapping at a single frequency can be used by the CNN‐CAD system for diagnosis and the research also gives a prompt that the mapping collection process can be replaced by a single‐frequency IR imaging system, which is cheaper and more portable than a Fourier transform infrared microscopy and thus may be widely utilized in hospitals to provide meaningful assistance for pathologists in clinics.

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Section: Discussionmentioning

confidence: 99%

Grade classification of human glioma using a convolutional neural network based on mid‐infrared spectroscopy mapping

Peng

Chen²,

Kong

et al. 2022

Journal of Biophotonics

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“…Given a dataset of N centered observations in a d-dimensional space PCA diagonalizes the covariance matrix where C is a covariance matrix, To solve the Eq. ( 5) using eigen values ( 1) Lin et al [82] investigated the diagnosis and progression of illnesses to integrate bio-fluid-based infrared spectroscopy into the clinical area. The authors looked at using Fourier transform infrared micro spectroscopy to detect abrupt cardiac death.…”

Section: Role Of Ai To Predict Pulmonary Edema Pulmonary Embolism Cov...mentioning

confidence: 99%

“…Lin et al [ 82 ] investigated the diagnosis and progression of illnesses to integrate bio-fluid-based infrared spectroscopy into the clinical area. The authors looked at using Fourier transform infrared micro spectroscopy to detect abrupt cardiac death.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Koul

Bawa²,

Kumar³

2022

Arch Computat Methods Eng

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Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.

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“…The resulting classifier is then used to assign class labels to test instances for which predictor feature values are known but class label values are unknown. Accordingly, in forensic medicine field, machine-learning algorithms have also been applied to determine the cause of death, infer skeletal age, and estimate the postmortem interval with vitreous humour, drowning fluid, limb long bone, and other biological specimens [ 16–21 ]. Despite numerous advances in methodology in the past years, forensic medicine research scientists continue to rely heavily on parametric models (e.g.…”

Section: Introductionmentioning

confidence: 99%

Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy

Cao

et al. 2023

Forensic Sciences Research

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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 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 post-contusion (n = 9). Then, the samples were analyzed using ultra-performance 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 (MLP) algorithm. As a result, all muscle samples were eventually divided into the following subgroups: 4 h, 8 h, 12 h, 16–20 h, 24–32 h, 36–40 h, 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 MLP-MLP tandem machine-learning model based on metabolomics data can be used as a novel strategy for wound age estimation in future forensic casework.

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Determination of causes of death via spectrochemical analysis of forensic autopsies‐based pulmonary edema fluid samples with deep learning algorithm

Cited by 14 publications

References 42 publications

Grade classification of human glioma using a convolutional neural network based on mid‐infrared spectroscopy mapping

Grade classification of human glioma using a convolutional neural network based on mid‐infrared spectroscopy mapping

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy

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