VideoCAD: An Uncertainty-Driven Neural Network for Coronary Artery Disease Screening From Facial Videos

Liu, Xuenan; Yang, Xuezhi; Song, Rencheng; Zhang, Jie; Li, Longwei

doi:10.1109/tim.2022.3229704

Cited by 7 publications

(3 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Azha Alvin literature [10] et al proposed using HRV features of PPG signals for CAD detection, and the HRV feature values obtained from short-term measured PPG signals are easily affected by the uncertainty of the pulse wave, which is the main reason for the low detection results. Xuenan Liu [19] et al used the video-PPG signal of the face and facial features as drivers to predict CAD by a deep learning model. In this paper, a deep learning model of the video-PPG signal channel is used for PPG signal detection of CAD.…”

Section: Comparison Of Existing Methods With the Methods In This Papermentioning

confidence: 99%

A multi-model weighted voting-based method for detecting coronary artery disease

Li¹,

Yang

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

View full text Add to dashboard Cite

Photoplethysmography (PPG) signal is an effective way for early screening of coronary artery disease (CAD). However, opposite test results of CAD may be drawn from PPG signal ambiguity (e.g., obscure tidal waves and missing repetitive waves). A multi-model weighted voting CAD detection model is proposed in this work to address this problem. Its main processes are as follows. First, the PPG and its second derivative photoplethysmography (SDPPG) features are extracted. Then, feature subsets and training models are obtained by recursive feature elimination algorithms for three base models: support vector machine (SVM), random forest (RF), and logistic regression (LR). The weights of each model are calculated based on the feature subsets and prior knowledge. Finally, each model outputs the classification results according to the weighted voting method. The proposed method evaluates the PPG signals of 130 inpatients and 115 normal subjects collected since, and the accuracy, sensitivity, and specificity of the obtained CAD detection models are 88.64%, 85.32%, and 91.24%. This shows that the multi-model weight voting method can reduce the negative impact of pulse wave uncertainty on a single model for detecting CAD.

show abstract

Section: Comparison Of Existing Methods With the Methods In This Papermentioning

confidence: 99%

A multi-model weighted voting-based method for detecting coronary artery disease

Li¹,

Yang

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

View full text Add to dashboard Cite

show abstract

“…As we discussed various methods for predicting CAD, here is another method for detecting CAD or CHD in less time as well as in low price budget i.e., predict CAD from facial photos. With the help of facial photos from different angles [1], it helps to predict CAD in efficient manner. It is done by taking photos from four distinct perspectives (top, front, 60 degrees right and 60 degrees left).…”

Section: G Technology Methods 1) Coronary Artery Prediction Usingmentioning

confidence: 99%

“…With the help of different machine learning algorithms and deep learning concept we can efficiently predict the CAD in the early stage. RESULT ANALYSIS In this paper [1], the authors employed facial recordings to predict Coronary Artery Disease because they focused on face traits as well as changes in skin tones, which may be a major cause of CAD. They recorded 1200 videos of 500 samples, half of which were CAD positive and the other half were healthy.…”

Section: G Technology Methods 1) Coronary Artery Prediction Usingmentioning

confidence: 99%

Coronary Artery Disease Prediction: A Systematic Review

Patil,

Jagtap,

Bhoje

et al. 2023

IJRASET

View full text Add to dashboard Cite

Coronary artery disease (CAD) continues to be the primary cause of mortality and chronic impairment in cardiovascular illnesses around the globe, hence coronary artery disease prediction is becoming more important in today's society. As we know, more individuals died as a result of late detection of cardiac disease, thus anticipating it in the early stages is critical. As there are more techniques to predict heart disease but, they are either time consuming or more expensive. So, our goal is to create a system that can predict the outcome fast, cheaply, and more precisely from our house, from our smart phone camera. We know that facial recognition plays a significant part in human-computer interaction, thus we want to integrate this approach with the medical profession in order to anticipate a condition that contributes significantly to the world population's mortality rate. The goal of this review paper is to compare these approaches to the current state-of-the-art and to discuss the challenges that must be addressed. It also includes recommendations for further study.

show abstract

RETRACTED ARTICLE: Design of an efficient multimodal engine for preemption and post-treatment recommendations for skin diseases via a deep learning-based hybrid bioinspired process

Shivadekar

Kataria²,

Limkar³

et al. 2023

Soft Comput

View full text Add to dashboard Cite

VideoCAD: An Uncertainty-Driven Neural Network for Coronary Artery Disease Screening From Facial Videos

Cited by 7 publications

References 38 publications

A multi-model weighted voting-based method for detecting coronary artery disease

A multi-model weighted voting-based method for detecting coronary artery disease

Coronary Artery Disease Prediction: A Systematic Review

RETRACTED ARTICLE: Design of an efficient multimodal engine for preemption and post-treatment recommendations for skin diseases via a deep learning-based hybrid bioinspired process

Contact Info

Product

Resources

About