Deep Learning Pulmonary Vein CT Image Segmentation for Atrial Fibrillation Trigger Prediction
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Solution Overview
Problem
Current methods for determining trigger origins in paroxysmal atrial fibrillation patients are invasive and lack accuracy, necessitating a more efficient and non-invasive approach for predicting non-pulmonary vein trigger origins before catheter ablation procedures.
Innovation Solution
A deep learning-based method for processing pulmonary vein computed tomography (PVCT) images, utilizing convolutional neural networks to classify and segment images from the upper border of the left atrium to the bottom of the heart, determining the presence of non-pulmonary vein trigger origins by analyzing image probabilities and predicting trigger origins with high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive electrophysiological examination is performed to determine trigger origins, then diagnostic accuracy is improved, but patient comfort and procedural complexity worsen
Solution Approach 1:
The patent replaces the invasive mechanical electrophysiological examination with a non-invasive deep learning-based image processing system. The convolutional neural network analyzes PVCT images to predict trigger origins, eliminating the need for invasive catheter insertion and real-time electrical mapping while maintaining diagnostic accuracy.
Solution Approach 2:
The patent introduces deep learning models as an intermediary between the available PVCT images and the clinical need for trigger origin identification. The convolutional neural network acts as a mediator that extracts meaningful patterns from imaging data to predict trigger origins without requiring direct invasive measurement.
2Measurement precision
If deep learning-based image processing is used to predict trigger origins, then non-invasive accuracy is improved, but computational complexity and data processing requirements worsen
Solution Approach 1:
The patent segments the PVCT imaging data into multiple input images from the upper border of left atrium to the bottom of the heart. The deep learning model processes these segmented images individually through convolutional layers, pooling operations, and classification layers, breaking down the complex analysis task into manageable computational steps.
Solution Approach 2:
The patent performs preliminary image processing and feature extraction during the training phase of the convolutional neural network. The model learns to identify patterns and characteristics of trigger origins from training data, preparing computational rules in advance that can be applied efficiently to new patient images during clinical use.
Data Source
AI summary
The present disclosure relates to methods, apparatuses, and computer programs for processing computed tomography images. Precise segmentation of the left atrium (LA) in computed tomography (CT) images constitutes a crucial preparatory step for catheter ablation in atrial fibrillation (AF). We aim to apply deep convolutional neural networks (DCNNs) to automate the LA detection/segmentation procedure and create a three-dimensional (3D) geometries. The deep learning provides an efficient and accurate way for automatic contouring and LA volume calculation based on the construction of the 3D LA geometry. Non-pulmonary vein (NPV) trigger has been reported as an important predictor of recurrence post atrial fibrillation (AF) ablation. Elimination of NPV triggers can reduce the post-ablation AF recurrence. The deep learning was applied in pre-ablation pulmonary vein computed tomography (PVCT) geometric slices to create a prediction model for NPV triggers in patients with paroxysmal atrial fibrillation (PAF). The deep learning model using pre-ablation PVCT can be applied to predict the trigger origins in PAF patients receiving catheter ablation. The application of this model may identify patients with a high risk of NPV trigger before ablation.