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Coronary Artery Brightness on Echocardiography as a Novel Marker in Kawasaki Disease - Machine Learning-Based Cluster
Daisuke Masui1, Satoru Iwashima2, Yoshifumi Miyagi3
1Department of Pediatrics, Hamamatsu University School of Medicine Shizuoka Japan.
Insights
Coronary artery brightness (CAB) in Kawasaki disease (KD) can be quantified using machine learning. This method identifies patient subgroups with distinct coronary profiles and predicts treatment resistance.
Area of Science:
- Cardiology
- Medical Imaging
- Machine Learning
Background:
- Coronary artery brightness (CAB) is observed in acute Kawasaki disease (KD).
- The clinical significance of CAB in KD remains unclear.
- This study quantifies CAB and assesses its clinical relevance in acute KD.
Purpose of the Study:
- To quantify Coronary Artery Brightness (CAB) in acute Kawasaki disease (KD).
- To evaluate the clinical significance of CAB using unsupervised machine learning (ML).
- To stratify KD patients into subgroups based on CAB and predict treatment outcomes.
Main Methods:
- Analysis of echocardiographic images from 89 acute-phase KD patients.
- Extraction and standardization of coronary artery (CA) pixel values as Z-scores.
- K-means clustering applied to CA Z-scores to stratify patients.
Main Results:
- Unsupervised ML stratified KD patients into two clusters based on CAB.
- Cluster 1 showed significantly greater CA diameters and Z-scores.
- Higher levels of total bilirubin and pentraxin 3 (IVIG resistance predictors) were found in Cluster 1.
Conclusions:
- Quantitative CAB analysis with ML effectively stratifies KD patients.
- This approach may serve as a novel non-invasive tool for assessing KD severity.
- The method shows potential for predicting intravenous immunoglobulin (IVIG) resistance in acute KD.
Background:
Coronary artery brightness (CAB) on echocardiography has been observed during the acute phase of Kawasaki disease (KD), but its clinical relevance remains unclear. This study aimed to quantify CAB and evaluate its clinical significance using unsupervised machine learning (ML).
Methods And Results:
Echocardiographic still images from 89 patients with acute-phase KD were analyzed. Pixel values of the coronary arteries (CAs) were extracted and standardized as Z-scores using brightness around the right coronary cusp as a reference. Mean and median pixel intensity (Z-scores) within the coronary artery region were calculated for each major CA branch. Based on these parameters, K-means clustering stratified patients into 2 clusters. Cluster 1 had significantly greater CA diameters and Z-scores in all 3 major coronary branches, with a higher proportion of patients with a maximum CA Z-score ≥2.5. In addition, levels of total bilirubin and pentraxin 3, both known predictors of intravenous immune globulin (IVIG) resistance, were significantly higher in Cluster 1.
Conclusions:
Quantitative CAB analysis combined with unsupervised ML effectively stratified KD patients into subgroups with distinct coronary and biomarker profiles. This method may serve as a novel non-invasive tool to evaluate disease severity and predict IVIG resistance in acute-phase KD.
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