Rapid detection of cholecystitis by serum fluorescence spectroscopy combined with machine learning

Dou, Jingrui; Dawuti, Wubulitalifu; Zhou, Jing; Li, Jintian; Zhang, Rui; Zheng, Xiangxiang; Lin, Renyong; Lü, Guodong

doi:10.1002/jbio.202200354

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…Pipetted 100 μL serum samples transfer into a 96‐well plate, and used SkanIt software (version 2.4.5) to read the fluorescence data of each serum sample on a fluorescence scanning multimode reader (Variouskan Flash, Thermo Scientific). Measuring condition: excitation wavelength was 405 nm; wavelength range was 430–750 nm [33]. The width of the emission was 5 nm; the width of excitation slits was 5 nm; the integration duration was 100 ms, while the acquisition interval was 1 nm.…”

Section: Methodsmentioning

confidence: 99%

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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Section: Methodsmentioning

confidence: 99%

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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“…Unlike PCA, PLS-DA is a supervised method that effectively reduces the dimensionality of the data while considering class labels, rendering it suitable for classification tasks. During the experiment, we employed PCA-LDA, PCA-SVM (support vector machine), and PLS-DA methods, all of which utilized the complete VIS-NIR spectrum [29,30].…”

Section: Reference Methodsmentioning

confidence: 99%

Rapid Discrimination of Organic and Non-Organic Leafy Vegetables (Water Spinach, Amaranth, Lettuce, and Pakchoi) Using VIS-NIR Spectroscopy, Selective Wavelengths, and Linear Discriminant Analysis

Wu,

et al. 2023

Applied Sciences

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Organic leafy vegetables face challenges related to potential substitution with non-organic products and vulnerability to dehydration and deterioration. To address these concerns, visible and near-infrared spectroscopy (VIS-NIR) combined with linear discriminant analysis (LDA) was employed in this study to rapidly distinguish between organic and non-organic leafy vegetables. The organic category includes organic water spinach (Ipomoea aquatica Forsskal), amaranth (Amaranthus tricolor L.), lettuce (Lactuca sativa var. ramosa Hort.), and pakchoi (Brassica rapa var. chinensis (Linnaeus) Kitamura), while the non-organic category consists of their four non-organic counterparts. Binary classification was performed on the reflectance spectra of these vegetables’ leaves and stems, respectively. Given the broad range of the VIS-NIR spectrum, stability selection (SS), random forest (RF), and analysis of variance (ANOVA) were used to evaluate the importance of the wavelengths selected by genetic algorithm (GA). According to the GA-selected wavelengths and their SS-evaluated values and locations, the significant bands for leaf spectra classification were identified as 550–910 nm and 1380–1500 nm, while 750–900 nm and 1700–1820 nm were important for stem spectra classification. Using these selected bands in the LDA classification, classification accuracies of over 95% were achieved, showcasing the effectiveness of utilizing the proposed method to rapidly identify organic leafy vegetables and the feasibility and potential of using a cost-effective spectrometer that only contains necessary bands for authenticating.

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Acute cholecystitis diagnosis in the emergency department: an artificial intelligence-based approach

Saboorifar,

Rahimi,

Babaahmadi

et al. 2024

Langenbecks Arch Surg

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Rapid detection of cholecystitis by serum fluorescence spectroscopy combined with machine learning

Abstract: While cholecystitis is a critical public health problem, the conventional diagnostic methods for its detection are time consuming, expensive and insufficiently sensi-

Cited by 4 publications

References 37 publications

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

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

Rapid Discrimination of Organic and Non-Organic Leafy Vegetables (Water Spinach, Amaranth, Lettuce, and Pakchoi) Using VIS-NIR Spectroscopy, Selective Wavelengths, and Linear Discriminant Analysis

Acute cholecystitis diagnosis in the emergency department: an artificial intelligence-based approach

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