scholarly journals Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification

Kidney360 ◽  
2021 ◽  
pp. 10.34067/KID.0005102021
Author(s):  
Matthew Nicholas Basso ◽  
Moumita Barua ◽  
Rohan John ◽  
April Khademi
Keyword(s):  
Machine Learning ◽  
Biomarker Discovery ◽  
Texture Features ◽  
Diagnostic Value ◽  
Disease Classification ◽  
Diagnostic Features ◽  
Linear Discriminant ◽  
Thin Basement Membrane Nephropathy ◽  
Patient Level ◽  
Glomerular Tuft

Background Pathologists use multiple microscopy modalities to assess renal biopsies. Besides usual diagnostic features, some changes are too subtle to be properly defined. Computational approaches have the potential to systematically quantitate subvisual clues, provide pathogenetic insight, and link to clinical outcomes. To this end, a proof of principle study is presented demonstrating that explainable biomarkers through machine learning can distinguish between glomerular disorders at the light microscopy level. Methods The proposed system employed image analysis techniques and extracted 233 explainable biomarkers related to color, morphology, and microstructural texture. Traditional machine learning was then used to classify minimal change disease (MCD), membranous nephropathy (MN), and thin-basement membrane nephropathy (TBMN) diseases on a glomerular and patient-level basis. Results The final model combined the Gini feature importance set and Linear Discriminant Analysis classifier. Six morphological (nuclei-to-glomerular tuft area, nuclei-to-glomerular area, glomerular tuft thickness > 10, glomerular tuft thickness > 3, total glomerular tuft thickness, and glomerular circularity) and four microstructural texture features (luminal contrast using wavelets, nuclei energy using wavelets, nuclei variance using color vector LBP, and glomerular correlation using GLCM) were together the best performing biomarkers. Accuracies of 76.86% and 86.67% were obtained for glomerular and patient-level classification, respectively. Conclusion Computational methods using explainable glomerular biomarkers have diagnostic value and are compatible with our existing knowledge of disease pathogenesis. Furthermore, this algorithm can be applied to clinical datasets for novel prognostic and mechanistic biomarker discovery.

scholarly journals Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

2021 ◽  
Author(s):  
Ziyi Li ◽  
Xiaoqian Jiang ◽  
Yizhuo Wang ◽  
Yejin Kim
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Biomarker Discovery ◽  
Treatment Strategies ◽  
Drug Repurposing ◽  
Modern Technology ◽  
Disease Classification ◽  
Future Research ◽  
New Biomarkers

Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.

scholarly journals Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexios S. Antonopoulos ◽  
Maria Boutsikou ◽  
Spyridon Simantiris ◽  
Andreas Angelopoulos ◽  
George Lazaros ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Cardiac Disease ◽  
T1 Mapping ◽  
Diagnostic Value ◽  
Disease Type ◽  
Disease Classification ◽  
Cardiac Amyloid ◽  
Native T1

AbstractWe explored whether radiomic features from T1 maps by cardiac magnetic resonance (CMR) could enhance the diagnostic value of T1 mapping in distinguishing health from disease and classifying cardiac disease phenotypes. A total of 149 patients (n = 30 with no heart disease, n = 30 with LVH, n = 61 with hypertrophic cardiomyopathy (HCM) and n = 28 with cardiac amyloidosis) undergoing a CMR scan were included in this study. We extracted a total of 850 radiomic features and explored their value in disease classification. We applied principal component analysis and unsupervised clustering in exploratory analysis, and then machine learning for feature selection of the best radiomic features that maximized the diagnostic value for cardiac disease classification. The first three principal components of the T1 radiomics were distinctively correlated with cardiac disease type. Unsupervised hierarchical clustering of the population by myocardial T1 radiomics was significantly associated with myocardial disease type (chi2 = 55.98, p < 0.0001). After feature selection, internal validation and external testing, a model of T1 radiomics had good diagnostic performance (AUC 0.753) for multinomial classification of disease phenotype (normal vs. LVH vs. HCM vs. cardiac amyloid). A subset of six radiomic features outperformed mean native T1 values for classification between myocardial health vs. disease and HCM phenocopies (AUC of T1 vs. radiomics model, for normal: 0.549 vs. 0.888; for LVH: 0.645 vs. 0.790; for HCM 0.541 vs. 0.638; and for cardiac amyloid 0.769 vs. 0.840). We show that myocardial texture assessed by native T1 maps is linked to features of cardiac disease. Myocardial radiomic phenotyping could enhance the diagnostic yield of T1 mapping for myocardial disease detection and classification.

scholarly journals Diagnostic Value of Machine Learning-Based Quantitative Texture Analysis in Differentiating Benign and Malignant Thyroid Nodules

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Bulent Colakoglu ◽  
Deniz Alis ◽  
Mert Yergin
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Texture Analysis ◽  
Thyroid Nodules ◽  
Intraclass Correlation ◽  
Texture Features ◽  
Diagnostic Value ◽  
External Data ◽  
Wrapper Method ◽  
Sensitivity Specificity

Aim. The aim of this study is to evaluate the diagnostic value of machine learning- (ML-) based quantitative texture analysis in the differentiation of benign and malignant thyroid nodules. Materials and methods. A sum of 306 quantitative textural features of 235 thyroid nodules (102 malignant, 43.4%; 133 benign, 56.4%) of a total of 198 patients were investigated using the random forest ML classifier. Feature selection and dimension reduction were conducted using reproducibility testing and a wrapper method. The diagnostic accuracy, sensitivity, specificity, and area under curve (AUC) of the proposed method were compared with the histopathological or cytopathological findings as reference methods. Results. Of the 306 initial texture features, 284 (92.2%) showed good reproducibility (intraclass correlation ≥0.80). The random forest classifier accurately identified 87 out of 102 malignant thyroid nodules and 117 out of 133 benign thyroid nodules, which is a diagnostic sensitivity of 85.2%, specificity of 87.9%, and accuracy of 86.8%. The AUC of the model was 0.92. Conclusions. Quantitative textural analysis of thyroid nodules using ML classification can accurately discriminate benign and malignant thyroid nodules. Our findings should be validated by multicenter prospective studies using completely independent external data.

scholarly journals A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA

2021 ◽  
Author(s):  
Bhagya Patil ◽  
Vishwanath Barkpalli
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
Learning Algorithms ◽  
Texture Features ◽  
Machine Learning Algorithms ◽  
Disease Classification ◽  
Support Vector ◽  
Cotton Leaf ◽  
Perspective View ◽  
Color Features

Abstract Cotton is one of the major crops in India where 23% of cotton gets exported to other countries. Hence, the cotton yield depends on the crop growth, and it gets affected because of diseases. In this paper, cotton disease classification is performed using different machine learning algorithms. For this research, the cotton database was created by capturing images in the field under controlled conditions. The same database is used for segmenting the images using modified factorization-based active contour. The color and texture features are extracted from segmented images and later its fed to the machine learning algorithms like Multilayer perceptron, Support vector machine, Naïve Bayes, Random forest, Ada Boost, K nearest neighbor. The performance of the classifiers is better when color features are extracted than texture features extraction. The color features are enough to classify the healthy and unhealthy cotton leaf images. Among the different classifiers, Multilayer perceptron gives nearly 96.69% which is greater than other classifiers.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

scholarly journals Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning

2021 ◽  
Vol 09 (02) ◽  
pp. E233-E238
Author(s):  
Rajesh N. Keswani ◽  
Daniel Byrd ◽  
Florencia Garcia Vicente ◽  
J. Alex Heller ◽  
Matthew Klug ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Medical Center ◽  
Academic Medical Center ◽  
Procedure Time ◽  
Image Capture ◽  
Common Procedure ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

Abstract Background and study aims Storage of full-length endoscopic procedures is becoming increasingly popular. To facilitate large-scale machine learning (ML) focused on clinical outcomes, these videos must be merged with the patient-level data in the electronic health record (EHR). Our aim was to present a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos. Methods This study was conducted at a single academic medical center. Most procedure videos are automatically uploaded to the cloud server but are identified only by procedure time and procedure room. We developed and then tested an algorithm to match recorded videos with corresponding exams in the EHR based upon procedure time and room and subsequently extract frames of interest. Results Among 28,611 total colonoscopies performed over the study period, 21,170 colonoscopy videos in 20,420 unique patients (54.2 % male, median age 58) were matched to EHR data. Of 100 randomly sampled videos, appropriate matching was manually confirmed in all. In total, these videos represented 489,721 minutes of colonoscopy performed by 50 endoscopists (median 214 colonoscopies per endoscopist). The most common procedure indications were polyp screening (47.3 %), surveillance (28.9 %) and inflammatory bowel disease (9.4 %). From these videos, we extracted procedure highlights (identified by image capture; mean 8.5 per colonoscopy) and surrounding frames. Conclusions We report the successful merging of a large database of endoscopy videos stored with limited identifiers to rich patient-level data in a highly accurate manner. This technique facilitates the development of ML algorithms based upon relevant patient outcomes.

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