Publications by authors named "Asuka Makimoto"
Article Synopsis
- Recent studies show AI's potential in detecting heart issues through ECGs, but specific waveforms are still unclear.
- This research analyzed data from over 17,000 cases in Japan and Germany, focusing on using deep learning for diagnosing left ventricular dysfunction.
- The study found that two-beat ECGs were the most effective for identifying heart problems, with the QRS to T-wave segments providing the best insights, especially using limb leads I and aVR.
Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment. This study assessed the impact of combining these sensor-based procedures with machine learning on diagnosing valvular abnormalities and ventricular dysfunction. Data from auscultation at three distinct locations and 12-lead ECGs were collected from 1052 patients undergoing echocardiography.
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- Researchers created a smartphone app using artificial intelligence (AI) to help doctors check for a serious heart condition called aortic valve stenosis (AS), especially in older people.
- They tested different AI methods to improve how accurately the app can detect AS by listening to heart sounds from various parts of the body.
- The app performed really well, detecting AS with high accuracy, and it could help doctors learn more about heart sounds and give remote help to patients.
Artificial intelligence (AI) is developing rapidly in the medical technology field, particularly in image analysis. ECG-diagnosis is an image analysis in the sense that cardiologists assess the waveforms presented in a 2-dimensional image. We hypothesized that an AI using a convolutional neural network (CNN) may also recognize ECG images and patterns accurately.
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