| Abstrakt: |
While prenatal congenital heart disease (CHD) screening has improved, accuracy remains as low as 30 percent. Standard fetal biometrics—cardiac axis (CA), cardiothoracic ratio (CTR), RV fractional area change (FAC), LV FAC, RA:LA area ratio, RV:LV area ratio—are available from screening imaging and can each aid in CHD screening, but can be cumbersome to measure. Combinations of biometrics may offer further utility but are challenging to integrate at the point of care. We tested whether using these biometrics in combination has utility in CHD screening (normal vs. abnormal). Further, we tested whether automatically predicted biometrics could function similarly to manually-labeled biometrics for this purpose. We included 105 fetal echocardiograms (20 normal, 85 abnormal across 12 different CHD lesions). We manually calculated the six biometrics above, performed dimensionality reduction using principal component analysis, and then clustered the resulting data by K-means. A previously developed deep learning model (Arnaout et al Nature 2021) was also used to automatically predict biometrics for normal, tetralogy of Fallot, and hypoplastic left heart syndrome hearts and plotted on the above cluster map. Optimal number of clusters was four, with RV:LV ratio and CTR as the most important features distinguishing clusters. Cluster 1 was predominantly normal hearts with cluster 2-4 largely abnormal hearts (Figure 1). The sensitivity and specificity for predicting abnormal hearts (e.g. CHD) was 86% and 75%, respectively. Model-predicted biometrics landed in the same clusters as the manually labeled lesions (Figure 1). To our knowledge, this is the first use of clustering to provide visualization of multiple fetal cardiac biometrics at once and reveal diagnostic utility. Once tested in screening ultrasounds on a larger scale, clustering of automated biometrics may be clinically useful at the screening point of care to augment scalable population-based screening. |
