A New Machine Learning Algorithm for Weather Visibility and Food Recognition

Abstract Background Checking appropriateness of blood transfusion for quality assurance required enormous usage of time and human resources from the healthcare system. We report here a new machine learning algorithm for checking blood transfusion quality. Materials and methods The multilayer perceptron neural network (MLPNN) was designed to learn an expert’s judgement from 4946 clinical cases. The accuracy in predicting the blood transfusion was then reported. Results We achieved a 96.8% overall accuracy rate, with a 99% match rate to the experts’ judgement on those appropriate cases and 90.9% on the inappropriate cases. Conclusions Machine learning algorithm can accurately match to human judgement by feeding in pre-surgical information and key laboratory variables.

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Deep Learning–Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution

Circulation Cardiovascular Imaging ◽

10.1161/circimaging.120.012293 ◽

2021 ◽

Author(s):

Federico M. Asch ◽

Victor Mor-Avi ◽

David Rubenson ◽

Steven Goldstein ◽

Muhamed Saric ◽

...

Keyword(s):

Machine Learning ◽

Ejection Fraction ◽

Learning Algorithm ◽

Point Of Care ◽

Intraclass Correlation ◽

Left Ventricular ◽

Left Ventricular Ejection ◽

Lv Function ◽

Machine Learning Algorithm ◽

New Machine

Background: We have recently tested an automated machine-learning algorithm that quantifies left ventricular (LV) ejection fraction (EF) from guidelines-recommended apical views. However, in the point-of-care (POC) setting, apical 2-chamber views are often difficult to obtain, limiting the usefulness of this approach. Since most POC physicians often rely on visual assessment of apical 4-chamber and parasternal long-axis views, our algorithm was adapted to use either one of these 3 views or any combination. This study aimed to (1) test the accuracy of these automated estimates; (2) determine whether they could be used to accurately classify LV function. Methods: Reference EF was obtained using conventional biplane measurements by experienced echocardiographers. In protocol 1, we used echocardiographic images from 166 clinical examinations. Both automated and reference EF values were used to categorize LV function as hyperdynamic (EF>73%), normal (53%–73%), mildly-to-moderately (30%–52%), or severely reduced (<30%). Additionally, LV function was visually estimated for each view by 10 experienced physicians. Accuracy of the detection of reduced LV function (EF<53%) by the automated classification and physicians’ interpretation was assessed against the reference classification. In protocol 2, we tested the new machine-learning algorithm in the POC setting on images acquired by nurses using a portable imaging system. Results: Protocol 1: the agreement with the reference EF values was good (intraclass correlation, 0.86–0.95), with biases <2%. Machine-learning classification of LV function showed similar accuracy to that by physicians in most views, with only 10% to 15% cases where it was less accurate. Protocol 2: the agreement with the reference values was excellent (intraclass correlation=0.84) with a minimal bias of 2.5±6.4%. Conclusions: The new machine-learning algorithm allows accurate automated evaluation of LV function from echocardiographic views commonly used in the POC setting. This approach will enable more POC personnel to accurately assess LV function.

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