ECG Machine Learning Prediction of Pulmonary Vein Reconnection
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Solution Overview
Problem
Current methods for confirming pulmonary vein isolation during ablation procedures do not effectively predict long-term success, leading to recurrent atrial fibrillation and a lack of effective strategies for determining the need for repeat ablation procedures.
Innovation Solution
An apparatus and method utilizing an electrocardiogram device and machine-learning models to analyze electrocardiogram data, generating ablation evaluation data and predicting the likelihood of pulmonary vein reconnection, thereby informing personalized treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods of confirming vein isolation during ablation procedures are used, then the procedure can be completed, but long-term success cannot be effectively predicted
Solution Approach 1:
The system performs preliminary analysis of ECG data before final treatment decisions are made. By training machine learning models on historical ECG data correlated with ablation outcomes, the system can predict long-term success probabilities in advance, allowing clinicians to adjust treatment plans before proceeding with repeat ablation procedures.
Solution Approach 2:
The patent introduces an intermediary machine learning evaluation system between the ablation procedure and the outcome assessment. This intermediary system processes ECG data through trained models to generate predictive metrics, serving as a bridge between current procedural confirmation methods and long-term success prediction, thereby improving measurement precision without compromising reliability.
2Reliability
If repeat ablation procedures are performed without effective prediction methods, then vein reconnection can be addressed, but unnecessary procedures are performed
Solution Approach 1:
The system implements a feedback mechanism where ECG data is continuously analyzed by the machine learning model to provide predictive feedback on likely treatment outcomes. This feedback loop allows clinicians to make informed decisions about whether repeat ablation procedures are likely to succeed, avoiding unnecessary procedures while ensuring effective treatment when predicted to work.
Solution Approach 2:
By performing preliminary prediction analysis using the trained machine learning model on available ECG data, the system determines in advance whether a repeat ablation procedure is likely to be beneficial. This preliminary assessment prevents loss of time by avoiding unnecessary procedures while ensuring that effective treatments are not missed.
3Reliability
If personalized treatment recommendations are implemented, then success rate increases, but device and process complexity increases
Solution Approach 1:
The machine learning evaluation system serves multiple functions: it analyzes ECG data, predicts long-term success probabilities, guides treatment decisions, and reduces unnecessary procedures. By consolidating these multiple functions into a single multi-functional system, the patent achieves personalized treatment recommendations that increase success rates while managing overall system complexity through functional integration rather than adding separate complex subsystems.
Data Source
AI summary
An apparatus and method for prediction of pulmonary vein reconnection is disclosed. The apparatus includes an electrocardiogram device, at least a processor, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to generate ablation evaluation training data, train an ablation evaluation machine-learning model using the ablation evaluation training data, receive, from the electrocardiogram device, the electrocardiogram data, and generate, using the ablation evaluation machine-learning model, ablation evaluation data of the patient, wherein generating the ablation evaluation data of the patient includes inputting, into the ablation evaluation machine-learning model, the electrocardiogram data and receiving as output, from the ablation evaluation machine-learning model, the ablation evaluation data of the patient.