Raman spectroscopy combined with machine learning algorithms for rapid detection Primary Sjögren's syndrome associated with interstitial lung disease

Wu, Xue; Chen, Chen; Chen, Xiao Mei; Luo, Cainan; Lv, Xiaoyi; Shi, Yuhang; Yang, Jie; Meng, Xin Yan; Chen, Cheng; Su, Jin Mei; Wu, Li Jun

doi:10.1016/j.pdpdt.2022.103057

Cited by 6 publications

(7 citation statements)

References 46 publications

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“…It is mainly used in solving small samples, nonlinear, and high-dimensional classification problems [73,74], which fits well with the sample characteristics of this paper. SVM has been commonly used and performs well in pattern recognition [73][74][75][76][77][78][79]. To demonstrate the performance of retardance-related parameters in discriminating skin tissues with different lesions, eight SVM classifiers with three classifications were established and evaluated according to different retardance-related parameters in conjunction with the one-against-one (OAO) method.…”

Section: Quantitative Classificationmentioning

confidence: 99%

“…Currently, many statistical methods have been developed for diagnosing and classifying several diseases, including SVM [62,73,75], k-nearest neighbors (KNNs) [76], artificial neural networks (ANNs) [84], random forest (RF) [76,85], naive Bayes classifier models [78,86], convolutional neural networks (CNNs) [87], linear discriminant analysis (LDA) [88], etc. In this paper, the SVM has provided an appealing result, which is consistent with most reported on other disease classifications [73][74][75][76]. More importantly, the results validate that vectorial retardance can visualize more useful details than scalar retardance.…”

Section: Quantitative Classificationmentioning

confidence: 99%

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Distinguishing Malignant Melanoma and Benign Nevus of Human Skin by Retardance Using Mueller Matrix Imaging Polarimeter

Wang

Chen

2023

Applied Sciences

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Malignant melanoma is considered the most serious type of skin cancer. In clinical practice, the conventional technique based on subjective visual examination has a high rate of misdiagnosis for malignant melanoma and benign nevus. Polarization imaging techniques have great potential in clinical diagnosis due to the advantages of improving sensitivity to functional structures, such as microfiber. In this paper, a set of human skin tissue sections, including 853 normal, 851 benign nevus, and 874 malignant melanoma, were analyzed and differentiated using a homemade high-fidelity Mueller matrix imaging polarimeter. The quantitative result using support vector machine algorithms confirmed that, while scalar retardance yields lower accuracy rates, vectorial retardance results in greater accuracy for both the training and testing sets. In particular, the cross-validation accuracy for the training set increased from 88.33% to 98.60%, and the prediction accuracy for the testing set increased from 87.92% to 96.19%. This tackles the limitation of the examination based on clinical experience and suggests that vectorial retardance can provide more accurate diagnostic evidence than scalar retardance. Unfortunately, it is inconvenient and time-consuming to read and analyze each component of the vectorial retardance simultaneously in the qualitative assessment. To address this clinical challenge, a color-encoded vectorial retardance imaging method was implemented. This method can provide superior tissue-specific contrast and more fiber details than scalar retardance. The anisotropic microfiber variation among different skin lesions, including the orientation and distribution, can be clearly highlighted. We believe that this work will not only enable early and rapid diagnosis of skin cancer but also provide a good observation and analysis of the state of cancer progression.

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Section: Quantitative Classificationmentioning

confidence: 99%

Section: Quantitative Classificationmentioning

confidence: 99%

Distinguishing Malignant Melanoma and Benign Nevus of Human Skin by Retardance Using Mueller Matrix Imaging Polarimeter

Wang

Chen

2023

Applied Sciences

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“…Raman spectroscopy (RS) is a technique based on inelastic light scattering 19 , which probes the total (bio)chemical composition of the sample 20 . Recent scientific literature has demonstrated the potential of integrating Raman spectroscopy with machine learning techniques to distinguish individuals with Sjögren's disease from healthy individuals, utilizing human blood samples 21 , 22 . Saliva is an “ultra-filtrate” of blood and can reflect many pathological states 23 .…”

Section: Introductionmentioning

confidence: 99%

Raman hyperspectroscopy of saliva and machine learning for Sjögren’s disease diagnostics

Vyas,

Khatiashvili,

Galati

et al. 2024

Sci Rep

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Sjögren's disease is an autoimmune disorder affecting exocrine glands, causing dry eyes and mouth and other morbidities. Polypharmacy or a history of radiation to the head and neck can also lead to dry mouth. Sjogren's disease is often underdiagnosed due to its non-specific symptoms, limited awareness among healthcare professionals, and the complexity of diagnostic criteria, limiting the ability to provide therapy early. Current diagnostic methods suffer from limitations including the variation in individuals, the absence of a single diagnostic marker, and the low sensitivity and specificity, high cost, complexity, and invasiveness of current procedures. Here we utilized Raman hyperspectroscopy combined with machine learning to develop a novel screening test for Sjögren's disease. The method effectively distinguished Sjögren's disease patients from healthy controls and radiation patients. This technique shows potential for development of a single non-invasive, efficient, rapid, and inexpensive medical screening test for Sjögren's disease using a Raman hyper-spectral signature.

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“…Among the most frequent pulmonary function tests (PFTs) include spirometry, pulmonary volume calculation, and diffusion ability evaluations. 5 These tests have two primary goals: (1) to describe/categorise physiological anomalies in an individual and (2) to measure the extent of the anomaly.…”

Section: Introductionmentioning

confidence: 99%

“…Future studies in this sector should concentrate on the development of technology with suitable algorithms that are ideal for low-care environments, 11 medical schedules, and the health of the general public. 5 Second, the accuracy of the currently published approaches is heavily influenced by the original signal quality and the surroundings. 12 Third, most existing approaches have the same technological limitations in terms of motion artefacts and a small dynamic range.…”

Section: Introductionmentioning

confidence: 99%

3D mask based lung function monitoring system using machine learning for early identification of lung disorders

Bhansali,

Rekha,

Shahapure

et al. 2024

Int J Imaging Syst Tech

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Early identification of illness can aid in lowering the death rate related to lung illnesses. Asthma, Chronic Obstructive Pulmonary Disease (COPD), and bronchiectasis are all chronic respiratory illnesses that cause irritation and oedema of the airway due to increased mucus discharge. Monitoring the asthmatic patient's physiological state is vital to avoiding dangerous circumstances. This study offers a regular lung function monitoring system that employs Machine Learning (ML) approach to aids in the prompt detection of symptoms of illness and the prevention of significant epidemics of the lung condition. A collection of sensors are coupled to the microcontroller in a 3D mask created using 3D printing technology. When a person wearing a face mask breathes in and out, the sensor values are instantly retrieved. The sensor data is sent to the cloud via a Wi‐Fi module for additional evaluation, and categorisation is performed using genetic algorithms, Support Vector Machine (SVM), and Principal Component Analysis (PCA). The GA, SWM, and PCA algorithms identify lung sickness using data from sensors obtained from the 3D masks through the web interface. There were 250 participants in total, comprising persons from all ages, smoker and those who do not smoke as well as asthmatics. The classifiers are trained utilising a set of pretrained values obtained from freely accessible datasets. Furthermore, patients are alerted when physiological indicators deviate from normal and when favourable atmospheric circumstances change.

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Raman spectroscopy combined with machine learning algorithms for rapid detection Primary Sjögren's syndrome associated with interstitial lung disease

Cited by 6 publications

References 46 publications

Distinguishing Malignant Melanoma and Benign Nevus of Human Skin by Retardance Using Mueller Matrix Imaging Polarimeter

Distinguishing Malignant Melanoma and Benign Nevus of Human Skin by Retardance Using Mueller Matrix Imaging Polarimeter

Raman hyperspectroscopy of saliva and machine learning for Sjögren’s disease diagnostics

3D mask based lung function monitoring system using machine learning for early identification of lung disorders

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