Diseased heart photos
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Summary
9 papers analyzed
These studies suggest that various deep learning models and imaging techniques, including fundus photographs, facial photos, retinal images, ultrasound, ECG images, and ensemble learning models, effectively detect and assess heart diseases, offering potential for early and accurate diagnosis.
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Advances in Heart Disease Detection Using Photos and Imaging Techniques
Deep Learning Models for Coronary Heart Disease Detection from Retinal Fundus Photos
Recent advancements in deep learning have enabled the development of models that can accurately identify coronary heart disease (CHD) using retinal fundus photographs. A study involving 51,765 fundus images from 25,222 subjects demonstrated that a convolutional neural network (CNN) could predict CHD with an area under the receiver operating characteristic curve (AUC) of 0.885 using binocular fundus photographs. The model also showed high accuracy in predicting cardiovascular risk factors such as age and sex.
Facial Photos for Coronary Artery Disease Detection
Another innovative approach involves using facial photos to detect coronary artery disease (CAD). A deep learning algorithm trained on facial images from 5,796 patients achieved an AUC of 0.730, outperforming traditional models like the Diamond-Forrester model and the CAD consortium clinical score. This method shows promise for pre-test CAD probability assessment in outpatient clinics and community screenings.
Multi-Modal Deep Learning for Cardiovascular Disease Diagnosis
Combining retinal images with dual-energy X-ray absorptiometry (DXA) data, researchers have developed a multi-modal deep learning model to diagnose cardiovascular diseases (CVD). This model achieved an accuracy of 78.3% in distinguishing between CVD patients and healthy controls. The model highlighted areas in retinal images indicative of CVD, such as hemorrhages, and identified higher bone mineral density and fat content in CVD patients.
Artistic Representations of Congenital Heart Disease
Artistic projects have also played a role in representing heart disease. Exhibits by artists like Kirsty Anderson and Sofie Layton have used photography and participatory arts to depict the experiences of patients with congenital heart disease (CHD). These works incorporate patient narratives and visual elements to convey the emotional and physical impacts of CHD.
Automated Detection of Coronary Artery Disease Using Ultrasound Images
A novel computer-aided diagnosis system using echocardiography images has been developed to detect coronary artery disease (CAD). This system employs double density-dual tree discrete wavelet transform (DD-DTDWT) and achieved an accuracy of 96.05%. The proposed coronary artery disease risk index (CADRI) further aids in categorizing diseased subjects from normal ones, making it a valuable tool for clinical settings.
Vision-Based Transformer Models for Heart Disease Detection from ECG Images
Cutting-edge vision transformer models, such as Google-Vit and Microsoft-Beit, have been applied to ECG images for heart disease detection. These models have shown remarkable classification results, demonstrating the potential of advanced machine learning techniques in improving diagnostic accuracy for heart diseases.
Segmentation of Myocardial Ischemia in Heart Photos
A segmentation method using Fuzzy Clustering and image processing techniques has been developed to identify myocardial ischemia in heart photos. This method achieved a mean accuracy of 83.24% and has been validated on both animal and human heart images, showing its versatility in cardiomyopathology studies.
3D Printing and AI for Structural Heart Disease
The integration of 3D printing, computational modeling, and artificial intelligence is revolutionizing the field of structural heart disease (SHD). These technologies enhance procedural planning, device development, and physician training, ultimately improving patient-centric care and outcomes in transcatheter SHD interventions.
Neural Networks for Prenatal Detection of Congenital Heart Disease
An ensemble of neural networks has been trained to detect complex congenital heart disease (CHD) from fetal echocardiograms and screening ultrasounds. This model achieved an AUC of 0.99 and demonstrated high sensitivity and specificity, comparable to that of clinicians. The model's ability to identify clinically relevant features makes it a valuable tool for improving prenatal CHD detection.
Conclusion
The use of advanced imaging techniques and deep learning models has significantly improved the detection and diagnosis of various heart diseases. From retinal and facial photos to ECG and ultrasound images, these innovative approaches offer non-invasive, accurate, and efficient methods for early diagnosis and risk assessment, ultimately enhancing patient care and outcomes.
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