Standardization of normal values for cardiac chamber size in echocardiography

Miyoshi, Tatsuya; Tanaka, Hidekazu

doi:10.1007/s10396-021-01147-6

Cited by 3 publications

(4 citation statements)

References 87 publications

(186 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(B) For radiomics features, we selected a normalization method for feature processing, aiming to eliminate dimensional differences between different features, avoid possible deviation in the model training, improve the convenience of data processing, and speed up the model convergence ( 40 , 41 ). Normalization helps to ensure dimensional unity between different features, thus improving the robustness and generalizability of the model ( 42 , 43 ). Normalization refers to scaling the data so that it falls within a specific interval.…”

Section: Methodsmentioning

confidence: 99%

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features

Chen

et al. 2023

Front. Neurol.

View full text Add to dashboard Cite

BackgroundEarly stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction.MethodsThe research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method.ResultsAmong the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies.ConclusionThe OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.

show abstract

Section: Methodsmentioning

confidence: 99%

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features

Chen

et al. 2023

Front. Neurol.

View full text Add to dashboard Cite

show abstract

“…Considerable geographic variations in RA size and function were identified. Asian individuals of both sexes had significantly lower BSA than non-Asian individuals, and their 2D and three-dimensional (3D) end-systolic RA volumes were significantly smaller even after BSA indexing [16] , [17] .In addition to sex and age, multivariable regression identified body mass index, coronary artery disease, chronic heart failure and atrial fibrillation (AF) as independent key correlates of the RA volume in both sexes [15] .…”

Section: Ra Sizementioning

confidence: 99%

Echocardiographic evaluation of the right atrial size and function: Relevance for clinical practice

Sun

et al. 2023

American Heart Journal Plus: Cardiology Research and Practice

View full text Add to dashboard Cite

“…5 The role of CT over 2D echocardiography for the creation of normal reference ranges also can be argued, but echocardiography remains the most widely used tool for cardiac imaging. 7 Along with sex, current normal ranges also account for age and body surface area (BSA). 4,8 In general, aortic root and ascending aorta dimensions increase with increasing age and BSA.…”

mentioning

confidence: 99%

“…Normal values came from small samples in which the majority of subjects were White individuals in America or Europe. 5,7,12 Outside of echocardiography, racial differences in medicine already have been acknowledged when it comes to reference intervals for common laboratory tests. 13 Similarly, risks of adverse events due to a common medical problem, such as hypertension, vary with race.…”

mentioning

confidence: 99%