CathAI: Fully Automated Interpretation of Coronary Angiograms Using Neural Networks

06/14/2021
by   Robert Avram, et al.
1

Coronary heart disease (CHD) is the leading cause of adult death in the United States and worldwide, and for which the coronary angiography procedure is the primary gateway for diagnosis and clinical management decisions. The standard-of-care for interpretation of coronary angiograms depends upon ad-hoc visual assessment by the physician operator. However, ad-hoc visual interpretation of angiograms is poorly reproducible, highly variable and bias prone. Here we show for the first time that fully-automated angiogram interpretation to estimate coronary artery stenosis is possible using a sequence of deep neural network algorithms. The algorithmic pipeline we developed–called CathAI–achieves state-of-the art performance across the sequence of tasks required to accomplish automated interpretation of unselected, real-world angiograms. CathAI (Algorithms 1-2) demonstrated positive predictive value, sensitivity and F1 score of >=90 projection angle overall and >=93 detection, the primary anatomic structures of interest. To predict obstructive coronary artery stenosis (>=70 area under the receiver operating characteristic curve (AUC) of 0.862 (95 0.843-0.880). When externally validated in a healthcare system in another country, CathAI AUC was 0.869 (95 coronary artery stenosis. Our results demonstrate that multiple purpose-built neural networks can function in sequence to accomplish the complex series of tasks required for automated analysis of real-world angiograms. Deployment of CathAI may serve to increase standardization and reproducibility in coronary stenosis assessment, while providing a robust foundation to accomplish future tasks for algorithmic angiographic interpretation.

READ FULL TEXT

Authors

page 8

page 15

page 19

page 20

02/08/2021

An Update of a Progressively Expanded Database for Automated Lung Sound Analysis

A continuous real-time respiratory sound automated analysis system is ne...
06/30/2021

Automated Onychomycosis Detection Using Deep Neural Networks

Clinical dermatology, still relies heavily on manual introspection of fu...
03/21/2021

Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation

Optimal performance is critical for decision-making tasks from medicine ...
11/17/2017

Detecting hip fractures with radiologist-level performance using deep neural networks

We developed an automated deep learning system to detect hip fractures f...
07/11/2018

Deepwound: Automated Postoperative Wound Assessment and Surgical Site Surveillance through Convolutional Neural Networks

Postoperative wound complications are a significant cause of expense for...
11/03/2021

Salt-based autopeering for DLT-networks

The security of any Distributed Ledger Technology (DLT) depends on the s...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.