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Rapid Computer‐Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi‐Modal Recognition

Stroke is a leading cause of mortality and disability worldwide, expected to result in 61 million disability‐adjusted life‐years in 2020. Rapid diagnostics is the core of stroke management for early prevention and medical treatment. Serum metabolic fingerprints (SMFs) reflect underlying disease prog...

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Autores principales: Xu, Wei, Lin, Jixian, Gao, Ming, Chen, Yuhan, Cao, Jing, Pu, Jun, Huang, Lin, Zhao, Jing, Qian, Kun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610260/
https://www.ncbi.nlm.nih.gov/pubmed/33173737
http://dx.doi.org/10.1002/advs.202002021
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author Xu, Wei
Lin, Jixian
Gao, Ming
Chen, Yuhan
Cao, Jing
Pu, Jun
Huang, Lin
Zhao, Jing
Qian, Kun
author_facet Xu, Wei
Lin, Jixian
Gao, Ming
Chen, Yuhan
Cao, Jing
Pu, Jun
Huang, Lin
Zhao, Jing
Qian, Kun
author_sort Xu, Wei
collection PubMed
description Stroke is a leading cause of mortality and disability worldwide, expected to result in 61 million disability‐adjusted life‐years in 2020. Rapid diagnostics is the core of stroke management for early prevention and medical treatment. Serum metabolic fingerprints (SMFs) reflect underlying disease progression, predictive of patient phenotypes. Deep learning (DL) encoding SMFs with clinical indexes outperforms single biomarkers, while posing challenges with poor prediction to interpret by feature selection. Herein, rapid computer‐aided diagnosis of stroke is performed using SMF based multi‐modal recognition by DL, to combine adaptive machine learning with a novel feature selection approach. SMFs are extracted by nano‐assisted laser desorption/ionization mass spectrometry (LDI MS), consuming 100 nL of serum in seconds. A multi‐modal recognition is constructed by integrating SMFs and clinical indexes with an enhanced area under curve (AUC) up to 0.845 for stroke screening, compared to single‐modal diagnosis by only SMFs or clinical indexes. The prediction of DL is addressed by selecting 20 key metabolite features with differential regulation through a saliency map approach, shedding light on the molecular mechanisms in stroke. The approach highlights the emerging role of DL in precision medicine and suggests an expanding utility for computational analysis of SMFs in stroke screening.
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spelling pubmed-76102602020-11-09 Rapid Computer‐Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi‐Modal Recognition Xu, Wei Lin, Jixian Gao, Ming Chen, Yuhan Cao, Jing Pu, Jun Huang, Lin Zhao, Jing Qian, Kun Adv Sci (Weinh) Communications Stroke is a leading cause of mortality and disability worldwide, expected to result in 61 million disability‐adjusted life‐years in 2020. Rapid diagnostics is the core of stroke management for early prevention and medical treatment. Serum metabolic fingerprints (SMFs) reflect underlying disease progression, predictive of patient phenotypes. Deep learning (DL) encoding SMFs with clinical indexes outperforms single biomarkers, while posing challenges with poor prediction to interpret by feature selection. Herein, rapid computer‐aided diagnosis of stroke is performed using SMF based multi‐modal recognition by DL, to combine adaptive machine learning with a novel feature selection approach. SMFs are extracted by nano‐assisted laser desorption/ionization mass spectrometry (LDI MS), consuming 100 nL of serum in seconds. A multi‐modal recognition is constructed by integrating SMFs and clinical indexes with an enhanced area under curve (AUC) up to 0.845 for stroke screening, compared to single‐modal diagnosis by only SMFs or clinical indexes. The prediction of DL is addressed by selecting 20 key metabolite features with differential regulation through a saliency map approach, shedding light on the molecular mechanisms in stroke. The approach highlights the emerging role of DL in precision medicine and suggests an expanding utility for computational analysis of SMFs in stroke screening. John Wiley and Sons Inc. 2020-09-23 /pmc/articles/PMC7610260/ /pubmed/33173737 http://dx.doi.org/10.1002/advs.202002021 Text en © 2020 The Authors. Published by Wiley‐VCH GmbH This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Communications
Xu, Wei
Lin, Jixian
Gao, Ming
Chen, Yuhan
Cao, Jing
Pu, Jun
Huang, Lin
Zhao, Jing
Qian, Kun
Rapid Computer‐Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi‐Modal Recognition
title Rapid Computer‐Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi‐Modal Recognition
title_full Rapid Computer‐Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi‐Modal Recognition
title_fullStr Rapid Computer‐Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi‐Modal Recognition
title_full_unstemmed Rapid Computer‐Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi‐Modal Recognition
title_short Rapid Computer‐Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi‐Modal Recognition
title_sort rapid computer‐aided diagnosis of stroke by serum metabolic fingerprint based multi‐modal recognition
topic Communications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610260/
https://www.ncbi.nlm.nih.gov/pubmed/33173737
http://dx.doi.org/10.1002/advs.202002021
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