Stomach tissue classification using autofluorescence spectroscopy and machine learning

Lim, Soo Yeong; Yoon, Hong Man; Kook, Myeong–Cherl; Jang, Jin Il; So, Peter T. C.; Kang, Jeon Woong; Kim, Hyung Min

doi:10.1007/s00464-023-10053-6

Surg Endosc

2023

DOI: 10.1007/s00464-023-10053-6

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Stomach tissue classification using autofluorescence spectroscopy and machine learning

Soo Yeong Lim

Hong Man Yoon

Myeong–Cherl Kook

et al.

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2024

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Cited by 2 publications

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Label‐Free Assessment of Key Biological Autofluorophores: Material Characteristics and Opportunities for Clinical Applications

Campbell,

Gosnell,

Agha

et al. 2024

Advanced Materials

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Autofluorophores are endogenous fluorescent compounds that naturally occur in the intra and extracellular spaces of all tissues and organs. Most have vital biological functions – like the metabolic cofactors NAD(P)H and FAD+, as well as the structural protein collagen. Others have been considered to be waste products – like lipofuscin and advanced glycation end products – which accumulate with age and are associated with cellular dysfunction. Due to their natural fluorescence these materials have great utility for enabling non‐invasive, label‐free assays with direct ties to biological function. Numerous technologies, with different advantages and drawbacks, have been applied to their assessment, including fluorescence lifetime imaging microscopy, hyperspectral microscopy, and flow cytometry. Here, we review the applications of label‐free autofluorophore assessment for clinical and health‐research applications, with specific attention to biomaterials, disease detection, surgical guidance, treatment monitoring and tissue assessment – fields which greatly benefit from non‐invasive methodologies capable of continuous, in vivo characterisation.This article is protected by copyright. All rights reserved

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Label‐Free Assessment of Key Biological Autofluorophores: Material Characteristics and Opportunities for Clinical Applications

Campbell,

Gosnell,

Agha

et al. 2024

Advanced Materials

View full text Add to dashboard Cite

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Rapid and non‐invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms

Xu,

Dawuti,

Maimaitiaili

et al. 2024

Journal of Biophotonics

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Cystic echinococcosis (CE) is a grievous zoonotic parasitic disease. Currently, the traditional technology of screening CE is laborious and expensive, developing an innovative technology is urgent. In this study, we combined serum fluorescence spectroscopy with machine learning algorithms to develop an innovative screening technique to diagnose CE in sheep. Serum fluorescence spectra of Echinococcus granulosus sensu stricto‐infected group (n = 63) and uninfected E. granulosus s.s. group (n = 60) under excitation at 405 nm were recorded. The linear support vector machine (Linear SVM), Quadratic SVM, medium radial basis function (RBF) SVM, K‐nearest neighbor (KNN), and principal component analysis‐linear discriminant analysis (PCA‐LDA) were used to analyze the spectra data. The results showed that Quadratic SVM had the great classification capacity, its sensitivity, specificity, and accuracy were 85.0%, 93.8%, and 88.9%, respectively. In short, serum fluorescence spectroscopy combined with Quadratic SVM algorithm has great potential in the innovative diagnosis of CE in sheep.

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Stomach tissue classification using autofluorescence spectroscopy and machine learning

Cited by 2 publications

References 24 publications

Label‐Free Assessment of Key Biological Autofluorophores: Material Characteristics and Opportunities for Clinical Applications

Label‐Free Assessment of Key Biological Autofluorophores: Material Characteristics and Opportunities for Clinical Applications

Rapid and non‐invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms

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