scholarly journals Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260098
Author(s):  
Jonathan N. Thomas ◽  
Joanna Roopkumar ◽  
Tushar Patel
Keyword(s):  
Machine Learning ◽  
Portal Hypertension ◽  
Liver Disease ◽  
Hepatic Metabolism ◽  
Supervised Machine Learning ◽  
Molecular Feature ◽  
Non Invasive ◽  
Molecular Features ◽  
Signature Pattern ◽  
Ion Mobility Spectroscopy

Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds could therefore provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of using global volatolomic profiles from breath analysis in combination with supervised machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate breathomic profiles. A standardized collection protocol and analysis pipeline was used to collect samples from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88–92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes using global breath output. These studies will pave the way for the development of non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

scholarly journals Machine Learning Analysis of Volatolomic Profiles in Breath Can Identify Non-invasive Biomarkers of Liver Disease

2021 ◽  
Author(s):  
Jonathan Thomas ◽  
Joanna Roopkumar ◽  
Tushar Patel
Keyword(s):  
Machine Learning ◽  
Portal Hypertension ◽  
Liver Disease ◽  
Hepatic Metabolism ◽  
Molecular Feature ◽  
Non Invasive ◽  
Molecular Features ◽  
Signature Pattern ◽  
Ion Mobility Spectroscopy ◽  
Learning Analysis

Abstract Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds (VOC) could provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of combining global VOC (volatolomic) profiles from breath analysis and machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate volatolomic profiles. Samples were collected from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88-92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes without identifying specific compounds. These studies will pave the way in developing non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

Portal hypertension and its management in children

2017 ◽  
Vol 103 (2) ◽  
pp. 186-191 ◽  
Author(s):  
Tassos Grammatikopoulos ◽  
Patrick James McKiernan ◽  
Anil Dhawan
Keyword(s):  
Portal Hypertension ◽  
Liver Disease ◽  
Gastrointestinal Bleeding ◽  
Surgical Procedures ◽  
Vascular Occlusion ◽  
Portal System ◽  
Mortality And Morbidity ◽  
Non Invasive ◽  
Intravascular Pressure

Portal hypertension (PHT), defined as raised intravascular pressure in the portal system, is a complication of chronic liver disease or liver vascular occlusion. Advances in our ability to diagnose and monitor the condition but also predict the risk of gastrointestinal bleeding have enabled us to optimise the management of children with PHT either at a surveillance or at a postbleeding stage. A consensus among paediatric centres in the classification of varices can be beneficial in streamlining future paediatric studies. New invasive (endoscopic and surgical procedures) and non-invasive (pharmacotherapy) techniques are currently used enabling clinicians to reduce mortality and morbidity in children with PHT.

scholarly journals The prototype device for non-invasive diagnosis of arteriovenous fistula condition using machine learning methods

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Marcin Grochowina ◽  
Lucyna Leniowska ◽  
Agnieszka Gala-Błądzińska
Keyword(s):  
Machine Learning ◽  
Arteriovenous Fistula ◽  
Low Cost ◽  
Principal Component ◽  
Special Kind ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Signal Acquisition ◽  
Non Invasive ◽  
Prototype Device

Abstract Pattern recognition and automatic decision support methods provide significant advantages in the area of health protection. The aim of this work is to develop a low-cost tool for monitoring arteriovenous fistula (AVF) with the use of phono-angiography method. This article presents a developed and diagnostic device that implements classification algorithms to identify 38 patients with end stage renal disease, chronically hemodialysed using an AVF, at risk of vascular access stenosis. We report on the design, fabrication, and preliminary testing of a prototype device for non-invasive diagnosis which is very important for hemodialysed patients. The system includes three sub-modules: AVF signal acquisition, information processing and classification and a unit for presenting results. This is a non-invasive and inexpensive procedure for evaluating the sound pattern of bruit produced by AVF. With a special kind of head which has a greater sensitivity than conventional stethoscope, a sound signal from fistula was recorded. The proces of signal acquisition was performed by a dedicated software, written specifically for the purpose of our study. From the obtained phono-angiogram, 23 features were isolated for vectors used in a decision-making algorithm, including 6 features based on the waveform of time domain, and 17 features based on the frequency spectrum. Final definition of the feature vector composition was obtained by using several selection methods: the feature-class correlation, forward search, Principal Component Analysis and Joined-Pairs method. The supervised machine learning technique was then applied to develop the best classification model.

scholarly journals Supervised machine learning based liver disease prediction approach with LASSO feature selection

2021 ◽  
Vol 10 (6) ◽  
pp. 3369-3376
Author(s):  
Saima Afrin ◽  
F. M. Javed Mehedi Shamrat ◽  
Tafsirul Islam Nibir ◽  
Mst. Fahmida Muntasim ◽  
Md. Shakil Moharram ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Liver Disease ◽  
Decision Tree ◽  
Medical Science ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Prediction Approach

In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system. 

c-Cbl expression as a novel predictive marker of survival in patients with metastatic colorectal cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15090-e15090
Author(s):  
Shin Yin Lee ◽  
Vijaya B. Kolachalama ◽  
Umit Tapan ◽  
Janice Weinberg ◽  
Jean M. Francis ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Medical Center ◽  
Area Under The Curve ◽  
Predictive Marker ◽  
Learning Model ◽  
Negative Regulator ◽  
Supervised Machine Learning ◽  
Molecular Features ◽  
Machine Learning Model

e15090 Background: Aberrant hyperactive Wnt/ß-catenin signaling is critical in colorectal cancer (CRC) tumorigenesis. Casitas B-lineage Lymphoma (c-Cbl) is a negative regulator of Wnt signaling, and functions as a tumor suppressor. The objective of this study was to evaluate c-Cbl expression as a predictive marker of survival in patients with metastatic CRC (mCRC). Methods: Patients with mCRC treated at Boston University Medical Center between 2004 and 2014 were analyzed. c-Cbl and nuclear ß-catenin expression was quantified in explanted biopsies using a customized color-based image segmentation pipeline. Quantification was normalized to the total tumor area in an image, and deemed ‘low’ or ‘high’ according to the mean normalized values of the cohort. A supervised machine-learning model based on bootstrap aggregating was constructed with c-Cbl expression as the input feature and 3-year survival as output. Results: Of the 72 subjects with mCRC, 52.78% had high and 47.22% had low c-Cbl expression. Patients with high c-Cbl had significantly better median overall survival than those with low c-Cbl expression (3.7 years vs. 1.8 years; p = 0.0026), and experienced superior 3-year survival (47.37% vs 20.59%; p = 0.017). Intriguingly, nuclear ß-catenin expression did not correlate with survival. No significant differences were detected between high and low c-Cbl groups in baseline characteristics (demographics, comorbidities), tumor-related parameters (primary tumor location, number of metastasis, molecular features) or therapy received (surgery, chemotherapy regimen). A 5-fold cross-validated machine-learning model associated with 3-year survival demonstrated an area under the curve of 0.729, supporting c-Cbl expression as a predictor of mCRC survival. Conclusions: Our results show that c-Cbl expression is associated with and predicts mCRC survival. Demonstration of these findings despite the small cohort size underscores the power of quantitative histology and machine-learning application. While further work is needed to validate c-Cbl as a novel biomarker of mCRC survival, this study supports c-Cbl as a regulator of Wnt/ß-catenin signaling and a suppressor of other oncogenes in CRC tumorigenesis.

scholarly journals First report of rapid, non-invasive, and reagent-free detection of malaria through the skin of patients with a beam of infrared light

2022 ◽  
Author(s):  
Gabriela Garcia ◽  
Tharanga Kariyawasam ◽  
Anton Lord ◽  
Cristiano Costa ◽  
Lana Chaves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Predictive Accuracy ◽  
Human Subjects ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Infrared Light ◽  
Non Invasive ◽  
Full Capacity

Abstract We describe the first application of the Near-infrared spectroscopy (NIRS) technique to detect Plasmodium falciparum and P. vivax malaria parasites through the skin of malaria positive and negative human subjects. NIRS is a rapid, non-invasive and reagent free technique which involves rapid interaction of a beam of light with a biological sample to produce diagnostic signatures in seconds. We used a handheld, miniaturized spectrometer to shine NIRS light on the ear, arm and finger of P. falciparum (n=7) and P. vivax (n=20) positive people and malaria negative individuals (n=33) in a malaria endemic setting in Brazil. Supervised machine learning algorithms for predicting the presence of malaria were applied to predict malaria infection status in independent individuals (n=12). Separate machine learning algorithms for differentiating P. falciparum from P. vivax infected subjects were developed using spectra from the arm and ear of P. falciparum and P. vivax (n=108) and the resultant model predicted infection in spectra of their fingers (n=54).NIRS non-invasively detected malaria positive and negative individuals that were excluded from the model with 100% sensitivity, 83% specificity and 92% accuracy (n=12) with spectra collected from the arm. Moreover, NIRS also correctly differentiated P. vivax from P. falciparum positive individuals with a predictive accuracy of 93% (n=54). These findings are promising but further work on a larger scale is needed to address several gaps in knowledge and establish the full capacity of NIRS as a non-invasive diagnostic tool for malaria. It is recommended that the tool is further evaluated in multiple epidemiological and demographic settings where other factors such as age, mixed infection and skin colour can be incorporated into predictive algorithms to produce more robust models for universal diagnosis of malaria.

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