Comment on “Serum Raman spectroscopy combined with multiple classification models for rapid diagnosis of breast cancer”

Bratchenko, Ivan А.; Bratchenko, Lyudmila A.

doi:10.1016/j.pdpdt.2022.103215

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…Some recent papers [19] lack the description of the rule applied to selecting the optimal number of LVs or PCs (principal components) at all. In other manuscripts [20][21][22][23][24], the methodology of selecting the correct number of PCs/LVs raises doubts and leads to a significant overestimation of the classification models.…”

Section: Pls-da Analysis Of the Calibration Set And Cross-validationmentioning

confidence: 99%

“…Chemometrics methods have become widespread for RS data analysis, which is clearly reflected in a large number of manuscripts in the last few years [17,18]. However, several studies demonstrated the results that are characterized by significant overestimation of the proposed approaches based on the RS clinical effectiveness due to some flaws of applying the chemometric methods to analyze the RS data [17,[19][20][21][22][23][24][25]. In particular, using an unreasonably large number of principal components is the most common flaw in PLS that usually leads to overoptimistic results, for example, when a portable RS tool could provide the diagnostic result with an excellent accuracy [22,26].…”

Section: Introductionmentioning

confidence: 99%

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Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis

Khristoforova

Bratchenko

Skuratova

et al. 2023

Journal of Biophotonics

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This work aims at studying Raman spectroscopy in combination with chemometrics as an alternative fast noninvasive method to detect chronic heart failure (CHF) cases. Optical analysis is focused on the changes in the spectral features associated with the biochemical composition changes of skin tissues. A portable spectroscopy setup with the 785 nm excitation wavelength was used to record skin Raman features. In this in vivo study, 127 patients and 57 healthy volunteers were involved in measuring skin spectral features by Raman spectroscopy. The spectral data were analyzed with a projection on the latent structures and discriminant analysis. 202 skin spectra of patients with CHF and 90 skin spectra of healthy volunteers were classified with 0.888 ROC AUC for the 10-fold cross validated algorithm. To identify CHF cases, the performance of the proposed classifier was verified by means of a new test set that is equal to 0.917 ROC AUC.

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Section: Pls-da Analysis Of the Calibration Set And Cross-validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis

Khristoforova

Bratchenko

Skuratova

et al. 2023

Journal of Biophotonics

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“…The high variability and low reproducibility of the spectral features in different studies for the same clinical task (Table 2) raise questions regarding the correctness of their definition. Moreover, many studies demonstrated a combination of Raman data and chemometrics [110][111][112][113] that might contain unsupported or invalid results due to typical errors related to their application. If we want to obtain reliable and reproducible results by using recognition and diagnostic technologies based on the methods of statistic modeling, our research projects and studies should follow certain requirements for statistical analysis application, particularly the requirements to avoid overfitted models and testing models built on unseen independent data, as well as the requirement to increase the experimental sample size to ensure significant and reliable results.…”

Section: Critical Evaluation and Future Perspectivesmentioning

confidence: 99%

Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review

Khristoforova,

Bratchenko,

Bratchenko

2023

IJMS

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Raman spectroscopy is a widely developing approach for noninvasive analysis that can provide information on chemical composition and molecular structure. High chemical specificity calls for developing different medical diagnostic applications based on Raman spectroscopy. This review focuses on the Raman-based techniques used in medical diagnostics and provides an overview of such techniques, possible areas of their application, and current limitations. We have reviewed recent studies proposing conventional Raman spectroscopy and surface-enhanced Raman spectroscopy for rapid measuring of specific biomarkers of such diseases as cardiovascular disease, cancer, neurogenerative disease, and coronavirus disease (COVID-19). As a result, we have discovered several most promising Raman-based applications to identify affected persons by detecting some significant spectral features. We have analyzed these approaches in terms of their potentially diagnostic power and highlighted the remaining challenges and limitations preventing their translation into clinical settings.

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“…Due to the diverse molecular structures of different substances, Raman spectra exhibit unique characteristics. Its advantages, including convenience, speed, and non-destructiveness, have led to widespread applications and high acclaim in disciplines such as medicine [4,5], chemistry [6][7][8], and biology [9,10]. It is noteworthy that peak information plays a crucial role in spectral data, containing key details about the molecular structure and chemical composition of the sample.…”

Section: Introductionmentioning

confidence: 99%

RepDwNet: Lightweight Deep Learning Model for Special Biological Blood Raman Spectra Analysis

He,

Zhou,

Ren

et al. 2024

Chemosensors

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The Raman spectroscopy analysis technique has found extensive applications across various disciplines due to its exceptional convenience and efficiency, facilitating the analysis and identification of diverse substances. In recent years, owing to the escalating demand for high-efficiency analytical methods, deep learning models have progressively been introduced into the realm of Raman spectroscopy. However, the application of these models to portable Raman spectrometers has posed a series of challenges due to the computational intensity inherent to deep learning approaches. This paper proposes a lightweight classification model, named RepDwNet, for identifying 28 different types of biological blood. The model integrates advanced techniques such as multi-scale convolutional kernels, depth-wise separable convolutions, and residual connections. These innovations enable the model to capture features at different scales while preserving the coherence of feature data to the maximum extent. The experimental results demonstrate that the average recognition accuracy of the model on the reflective Raman blood dataset and the transmissive Raman blood dataset are 97.31% and 97.10%, respectively. Furthermore, by applying structural reparameterization to compress the well-trained model, it maintains high classification accuracy while significantly reducing the parameter size, thereby enhancing the speed of classification inference. This makes the model more suitable for deployment in portable and mobile devices. Additionally, the proposed model can be extended to various Raman spectroscopy classification scenarios.

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Comment on “Serum Raman spectroscopy combined with multiple classification models for rapid diagnosis of breast cancer”

Cited by 5 publications

References 8 publications

Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis

Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis

Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review

RepDwNet: Lightweight Deep Learning Model for Special Biological Blood Raman Spectra Analysis

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