Key result
Machine learning achieves ~99% accuracy in distinguishing HLHS from healthy controls.
Why the study?
Few studies have evaluated machine-learning-based classification of complex congenital heart disease using cardiovascular magnetic resonance.
Does a machine learning-based classification system accurately detect anatomic landmarks and identify HLHS from CMR images in patients with Fontan circulation compared to healthy controls?
Observational (n=79)
Does a machine learning-based classification system accurately detect anatomic landmarks and identify HLHS from CMR images in patients with Fontan circulation compared to healthy controls?
A tailor-made neural network combined with a linear SVM can accurately detect anatomic landmarks in CMR images and distinguish HLHS patients from healthy controls with 98.7% accuracy.
May support automated CMR landmark detection in HLHS; leaves open external validation before clinical adoption.
OBJECTIVE: The prospect of being able to gain relevant information from cardiovascular magnetic resonance (CMR) image analysis automatically opens up new potential to assist the evaluating physician. For machine-learning-based classification of complex congenital heart disease, only few studies have used CMR. MATERIALS AND METHODS: This study presents a tailor-made neural network architecture for detection of 7 distinctive anatomic landmarks in CMR images of patients with hypoplastic left heart syndrome (HLHS) in Fontan circulation or healthy controls and demonstrates the potential of the spatial arrangement of the landmarks to identify HLHS. The method was applied to the axial SSFP CMR scans of 46 patients with HLHS and 33 healthy controls. RESULTS: The displacement between predicted and annotated landmark had a standard deviation of 8-17 mm and was larger than the interobserver variability by a factor of 1.1-2.0. A high overall classification accuracy of 98.7% was achieved. DISCUSSION: Decoupling the identification of clinically meaningful anatomic landmarks from the actual classification improved transparency of classification results. Information from such automated analysis could be used to quickly jump to anatomic positions and guide the physician more efficiently through the analysis depending on the detected condition, which may ultimately improve work flow and save analysis time.
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Gabbert et al. (2024) conducted an observational in Hypoplastic left heart syndrome (HLHS) (n=79). Machine learning-based classification using a convolutional neural network and support vector machine vs. Healthy controls was evaluated on Overall classification accuracy for distinguishing HLHS patients from healthy controls. A machine learning approach using a convolutional neural network and support vector machine achieved an overall classification accuracy of 98.7% in distinguishing patients with hypoplastic left heart syndrome from healthy controls.
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