Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review

Banchhor, Sumit K.; Londhe, Narendra D.; Araki, Tadashi; Saba, Luca; Radeva, Petia; Khanna, Narendra N.; Suri, Jasjit S.

doi:10.1016/j.compbiomed.2018.08.017

Cited by 40 publications

(28 citation statements)

References 128 publications

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“…LASSO works by shrinking the estimates of the regression coefficients and prevent overfitting due to collinearity of the covariates, which combines the advantages of selection process (easy to explain) and expression (robust), which is particularly useful in large data sets requiring efficient and fast algorithms ( 47 – 49 ). ML algorithms' outstanding performance in the field of processing complex data structures and big data makes it dominant in the field of healthcare and medical imaging, and compared with other machine learning methods, the performance of XGBoost can be improved more than 10 times ( 25 , 50 – 53 ).…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Based Framework to Identify and Classify Non-alcoholic Fatty Liver Disease in a Large-Scale Population

Xue

Zhang

et al. 2022

Front. Public Health

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Non-alcoholic fatty liver disease (NAFLD) is a common serious health problem worldwide, which lacks efficient medical treatment. We aimed to develop and validate the machine learning (ML) models which could be used to the accurate screening of large number of people. This paper included 304,145 adults who have joined in the national physical examination and used their questionnaire and physical measurement parameters as model's candidate covariates. Absolute shrinkage and selection operator (LASSO) was used to feature selection from candidate covariates, then four ML algorithms were used to build the screening model for NAFLD, used a classifier with the best performance to output the importance score of the covariate in NAFLD. Among the four ML algorithms, XGBoost owned the best performance (accuracy = 0.880, precision = 0.801, recall = 0.894, F-1 = 0.882, and AUC = 0.951), and the importance ranking of covariates is accordingly BMI, age, waist circumference, gender, type 2 diabetes, gallbladder disease, smoking, hypertension, dietary status, physical activity, oil-loving and salt-loving. ML classifiers could help medical agencies achieve the early identification and classification of NAFLD, which is particularly useful for areas with poor economy, and the covariates' importance degree will be helpful to the prevention and treatment of NAFLD.

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Section: Discussionmentioning

confidence: 99%

A Machine Learning Based Framework to Identify and Classify Non-alcoholic Fatty Liver Disease in a Large-Scale Population

Xue

Zhang

et al. 2022

Front. Public Health

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“…The analysis of diabetes data is a challenging issue because most of the medical data are nonlinear, nonnormal, correlation structured, and complex in nature [ 11 ]. The machine learning (ML) algorithms have dominated in the field of medical healthcare [ 11 – 15 ] and medical imaging for diseases such as stroke, coronary artery disease, and cancer [ 16 – 20 ]. A decision tree (DT) is one of the classical algorithms of ML.…”

Section: Introductionmentioning

confidence: 99%

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

Xue

et al. 2020

Journal of Diabetes Research

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Background. An estimated 425 million people globally have diabetes, accounting for 12% of the world’s health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas. Methods. A total of 584,168 adult subjects who have participated in the national physical examination were enrolled in this study. The risk factors for type II diabetes mellitus (T2DM) were identified by p values and odds ratio, using logistic regression (LR) based on variables of physical measurement and a questionnaire. Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables’ importance scores of T2DM. Results. The results indicated that XGBoost had the best performance (accuracy=0.906, precision=0.910, recall=0.902, F‐1=0.906, and AUC=0.968). The degree of variables’ importance scores in XGBoost showed that BMI was the most significant feature, followed by age, waist circumference, systolic pressure, ethnicity, smoking amount, fatty liver, hypertension, physical activity, drinking status, dietary ratio (meat to vegetables), drink amount, smoking status, and diet habit (oil loving). Conclusions. We proposed a classifier based on LR-XGBoost which used fourteen variables of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of T2DM. The classifier can accurately screen the risk of diabetes in the early phrase, and the degree of variables’ importance scores gives a clue to prevent diabetes occurrence.

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“…The ImgTracer is a user-friendly system and is adapted in several studies around the world. 44,47,48,51 –56 Several papers have been published with applications for lung cancer, 57,58 carotid CT imaging, 19,22,44,59,60 carotid MR imaging, 61 skin cancer imaging, 62,63 coronary studies, 43,48,52,53,64 –66 and CUS. 19,50,51,67 –71 The above manual tracing procedure was repeated by several neurologists over the course of time.…”

Section: Methodsmentioning

confidence: 99%

Morphological Carotid Plaque Area Is Associated With Glomerular Filtration Rate: A Study of South Asian Indian Patients With Diabetes and Chronic Kidney Disease

Puvvula¹,

Jamthikar

Gupta

et al. 2020

Angiology

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We evaluated the association between automatically measured carotid total plaque area (TPA) and the estimated glomerular filtration rate (eGFR), a biomarker of chronic kidney disease (CKD). Automated average carotid intima–media thickness (cIMTave) and TPA measurements in carotid ultrasound (CUS) were performed using AtheroEdge (AtheroPoint). Pearson correlation coefficient (CC) was then computed between the TPA and eGFR for (1) males versus females, (2) diabetic versus nondiabetic patients, and (3) between the left and right carotid artery. Overall, 339 South Asian Indian patients with either type 2 diabetes mellitus (T2DM) or CKD, or hypertension (stage 1 or stage 2) were retrospectively analyzed by acquiring cIMTave and TPA measurements of their left and right common carotid arteries (CCA; total CUS: 678, mean age: 54.2 ± 9.8 years; 75.2% males; 93.5% with T2DM). The CC between TPA and eGFR for different scenarios were (1) for males and females −0.25 ( P < .001) and −0.35 ( P < .001), respectively; (2) for T2DM and non-T2DM −0.26 ( P < .001) and −0.49 ( P = .02), respectively, and (3) for left and right CCA −0.25 ( P < .001) and −0.23 ( P < .001), respectively. Automated TPA is an equally reliable biomarker compared with cIMTave for patients with CKD (with or without T2DM) with subclinical atherosclerosis.

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Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review

Cited by 40 publications

References 128 publications

A Machine Learning Based Framework to Identify and Classify Non-alcoholic Fatty Liver Disease in a Large-Scale Population

A Machine Learning Based Framework to Identify and Classify Non-alcoholic Fatty Liver Disease in a Large-Scale Population

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

Morphological Carotid Plaque Area Is Associated With Glomerular Filtration Rate: A Study of South Asian Indian Patients With Diabetes and Chronic Kidney Disease

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