Coronary angiography is the primary procedure for diagnosis and management decisions in coronary artery disease (CAD), but ad-hoc visual assessment of angiograms has high variability. Here we report a Show more
Coronary angiography is the primary procedure for diagnosis and management decisions in coronary artery disease (CAD), but ad-hoc visual assessment of angiograms has high variability. Here we report a fully automated approach to interpret angiographic coronary artery stenosis from standard coronary angiograms. Using 13,843 angiographic studies from 11,972 adult patients at University of California, San Francisco (UCSF), between April 1, 2008 and December 31, 2019, we train neural networks to accomplish four sequential necessary tasks for automatic coronary artery stenosis localization and estimation. Algorithms are internally validated against criterion-standard labels for each task in hold-out test datasets. Algorithms are then externally validated in real-world angiograms from the University of Ottawa Heart Institute (UOHI) and also retrained using quantitative coronary angiography (QCA) data from the Montreal Heart Institute (MHI) core lab. The CathAI system achieves state-of-the-art performance across all tasks on unselected, real-world angiograms. Positive predictive value, sensitivity and F1 score are all ≥90% to identify projection angle and ≥93% for left/right coronary artery angiogram detection. To predict obstructive CAD stenosis (≥70%), CathAI exhibits an AUC of 0.862 (95% CI: 0.843-0.880). In UOHI external validation, CathAI achieves AUC 0.869 (95% CI: 0.830-0.907) to predict obstructive CAD. In the MHI QCA dataset, CathAI achieves an AUC of 0.775 (95%. CI: 0.594-0.955) after retraining. In conclusion, multiple purpose-built neural networks can function in sequence to accomplish automated analysis of real-world angiograms, which could increase standardization and reproducibility in angiographic coronary stenosis assessment. Show less
Imaging of gene-expression patterns in live animals is difficult to achieve with fluorescent proteins because tissues are opaque to visible light. Imaging of transgene expression with magnetic resonan Show more
Imaging of gene-expression patterns in live animals is difficult to achieve with fluorescent proteins because tissues are opaque to visible light. Imaging of transgene expression with magnetic resonance imaging (MRI), which penetrates to deep tissues, has been limited by single reporter visualization capabilities. Moreover, the low-throughput capacity of MRI limits large-scale mutagenesis strategies to improve existing reporters. Here we develop an MRI system, called GeneREFORM, comprising orthogonal reporters for two-color imaging of transgene expression in deep tissues. Starting from two promiscuous deoxyribonucleoside kinases, we computationally designed highly active, orthogonal enzymes ('reporter genes') that specifically phosphorylate two MRI-detectable synthetic deoxyribonucleosides ('reporter probes'). Systemically administered reporter probes exclusively accumulate in cells expressing the designed reporter genes, and their distribution is displayed as pseudo-colored MRI maps based on dynamic proton exchange for noninvasive visualization of transgene expression. We envision that future extensions of GeneREFORM will pave the way to multiplexed deep-tissue mapping of gene expression in live animals. Show less