High Throughput Blood Analysis Based on Deep Learning Algorithm and Self-Positioning Super-Hydrophobic SERS Platform for Non-Invasive Multi-Disease Screening - Details

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Abstract：

Blood　analysis　is　crucial　for　early　cancer　screening　and　improving　patient　survival　rates.　However,　developing　an　effective　strategy　for　early　cancer　detection　using　high-throughput　blood　analysis　is　still　challenging.　Herein,　a　novel　automatic　super-hydrophobic　platform　is　developed　together　with　a　deep　learning　(DL)-based　label-free　serum　and　surface-enhanced　Raman　scattering　(SERS),　along　with　an　automatic　high-throughput　Raman　spectrometer　to　build　an　effective　point-of-care　diagnosis　system.　A　total　of　695　high-quality　serum　SERS　spectra　are　obtained　from　203　healthy　volunteers,　77　leukemia　M5,　94　hepatitis　B　virus,　and　321　breast　cancer　patients.　Serum　SERS　signals　from　the　normal　(n =　183)　and　patient　(n =　443)　groups　are　used　to　assess　the　DL　model,　which　classify　them　with　a　maximum　accuracy　of　100%.　Furthermore,　when　SERS　is　combined　with　DL,　it　exhibits　excellent　diagnostic　accuracy　(98.6%)　for　the　external　held-out　test　set,　indicating　that　this　method　can　be　used　to　develop　a　high　throughput,　rapid,　and　label-free　tool　for　screening　diseases.　©　2021　Wiley-VCH　GmbH

Keyword：

Blood Deep learning Diagnosis Diseases Hydrophobicity Learning algorithms Raman scattering Raman spectroscopy Surface scattering Viruses

Community：

[ 1 ] [Lin, Xueliang]Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Digital Fujian Internet-of-Things Laboratory of Environment Monitoring, Fujian Normal University, Fuzhou; 350007, China
[ 2 ] [Lin, Duo]Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Digital Fujian Internet-of-Things Laboratory of Environment Monitoring, Fujian Normal University, Fuzhou; 350007, China
[ 3 ] [Chen, Yang]Department of Laboratory Medicine, Fujian Medical University, Fuzhou; 350007, China
[ 4 ] [Lin, Jincheng]The School of Mathematics and Computer Science, MOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou; 350116, China
[ 5 ] [Weng, Shuyun]Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Digital Fujian Internet-of-Things Laboratory of Environment Monitoring, Fujian Normal University, Fuzhou; 350007, China
[ 6 ] [Song, Jibin]The School of Mathematics and Computer Science, MOE Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou; 350116, China
[ 7 ] [Feng, Shangyuan]Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Digital Fujian Internet-of-Things Laboratory of Environment Monitoring, Fujian Normal University, Fuzhou; 350007, China

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Source ：

Advanced Functional Materials

ISSN： 1616-301X

Year： 2021

Issue： 51

Volume： 31

1 9 . 9 2 4

JCR@2021

1 8 . 5 0 0

JCR@2023

ESI HC Threshold：142

JCR Journal Grade：1

CAS Journal Grade：1

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30 Days PV： 0

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