The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy
Bartosz Krzowski1,2, Jakub Rokicki1,2, Renata Główczyńska1
11st Department of Cardiology, Medical University of Warsaw, 02-097 Warsaw, Poland.
Insights
Artificial intelligence (AI) algorithms can effectively detect inadequate cardiac resynchronization therapy using ECG analysis. This AI approach achieved high sensitivity and precision, aiding in patient treatment assessment.
Area of Science:
- Cardiology
- Medical Informatics
- Artificial Intelligence
Background:
- Cardiovascular disease is a leading cause of death globally.
- Heart failure management is challenging, with cardiac resynchronization therapy (CRT) widely adopted.
- Not all patients respond effectively to CRT, necessitating improved assessment methods.
Purpose of the Study:
- To evaluate the efficacy of artificial intelligence (AI) algorithms in identifying ineffective cardiac resynchronization therapy.
- To explore AI's potential in improving the assessment of CRT effectiveness.
Main Methods:
- Analysis of 1241 ECG tracings from 547 patients.
- Manual classification of ECG signals (QRS-complex, rhythm) by multiple cardiologists.
- Training AI algorithms on 80% of data and testing on the remaining 20%.
Main Results:
- AI algorithms achieved a detection sensitivity of 99.2% for effective CRT stimulation.
- The precision of the AI algorithms in detecting effective CRT was 92.4%.
Conclusions:
- AI algorithms demonstrate significant potential as a tool for assessing CRT effectiveness.
- AI can aid clinicians in identifying patients who may not benefit from resynchronization therapy.
Background:
Cardiovascular disease remains the leading cause of death in the European Union and worldwide. Constant improvement in cardiac care is leading to an increased number of patients with heart failure, which is a challenging condition in terms of clinical management. Cardiac resynchronization therapy is becoming more popular because of its grounded position in guidelines and clinical practice. However, some patients do not respond to treatment as expected. One way of assessing cardiac resynchronization therapy is with ECG analysis. Artificial intelligence is increasing in terms of everyday usability due to the possibility of everyday workflow improvement and, as a result, shortens the time required for diagnosis. A special area of artificial intelligence is machine learning. AI algorithms learn on their own based on implemented data. The aim of this study was to evaluate using artificial intelligence algorithms for detecting inadequate resynchronization therapy.
Methods:
A total of 1241 ECG tracings were collected from 547 cardiac department patients. All ECG signals were analyzed by three independent cardiologists. Every signal event (QRS-complex) and rhythm was manually classified by the medical team and fully reviewed by additional cardiologists. The results were divided into two parts: 80% of the results were used to train the algorithm, and 20% were used for the test (Cardiomatics, Cracow, Poland).
Results:
The required level of detection sensitivity of effective cardiac resynchronization therapy stimulation was achieved: 99.2% with a precision of 92.4%.
Conclusions:
Artificial intelligence algorithms can be a useful tool in assessing the effectiveness of resynchronization therapy.
More Related Videos
10:17Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
Published on: April 11, 2025
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018