Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis During CCTA Evaluation of Chest Pain in the Emergency Department: Preparing an Application for Real-world Use

Abstract

Coronary computed tomography angiography (CCTA) evaluation of chest pain patients in an emergency department (ED) is considered appropriate. While a “negative” CCTA interpretation supports direct patient discharge from an ED, labor-intensive analyses are required, with accuracy in jeopardy from distractions. We describe the development of an artificial intelligence (AI) algorithm and workflow for assisting qualified interpreting physicians in CCTA screening for total absence of coronary atherosclerosis. The two-phase approach consisted of (1) phase 1—development and preliminary testing of an algorithm for vessel-centerline extraction classification in a balanced study population (n = 500 with 50% disease prevalence) derived by retrospective random case selection, and (2) phase 2—simulated clinical Trialing of developed algorithm on a per-case (entire coronary artery tree) basis in a more “real-world” study population (n = 100 with 28% disease prevalence) from an ED chest pain series. This allowed pre-deployment evaluation of the AI-based CCTA screening application which provides vessel-by-vessel graphic display of algorithm inference results integrated into a clinically capable viewer. Algorithm performance evaluation used area under the receiver operating characteristic curve (AUC-ROC); confusion matrices reflected ground truth vs AI determinations. The vessel-based algorithm demonstrated strong performance with AUC-ROC = 0.96. In both phase 1 and phase 2, independent of disease prevalence differences, negative predictive values at the case level were very high at 95%. The rate of completion of the algorithm workflow process (96% with inference results in 55–80 s) in phase 2 depended on adequate image quality. There is potential for this AI application to assist in CCTA interpretation to help extricate atherosclerosis from chest pain presentations.

Appendices

Appendix 1

Because all vessel-centerline extractions originated at the transition from the aortic root to either the left main coronary artery or right coronary artery (standard in commercial coronary artery image-processing systems), the sharing of an “up-stream” arterial segment between “downstream” segments of the same primary artery or its branches is common. In this study, while the identification of each vessel extraction was based on the vessel of termination, its classification as “plaque-annotated” or “atherosclerosis-free” was established by the presence or absence, respectively, of atherosclerosis anywhere along the vessel extraction, even when atherosclerosis was located only before the true origin of a branch (Fig. 5).

Fig. 5

Classification of vessel-centerline extractions. Significant luminal narrowing (arrows) of the left anterior descending (LAD) coronary artery is demonstrated on both a volume-rendered image (above, with automatically extracted centerlines indicated on right) and curved multi-planar images along the vessel centerlines (below). The “upstream” atherosclerotic proximal LAD dictates the classification of both the “downstream” atherosclerosis-free mid-distal LAD and first diagonal branch (D1) as “plaque-annotated.” Hence, D1 is considered a diseased vessel course due to the proximal LAD atherosclerosis; for it to be considered atherosclerosis-free requires all segments proximal to the branch to also be free of atherosclerosis. On the other hand, the left circumflex coronary artery (LCx) and its first obtuse marginal branch (OM1) are entirely atherosclerosis-free along their vessel extractions (D2 = second diagonal branch; RI = ramus intermedius branch)

Appendix 2

The GUI-based manual annotation process involved regional color-coding of basic stenosis grade (i.e., non-obstructive vs obstructive) along the vessel courses as shown on rotatable 3D branching coronary artery trees, representing the combined display of the multiple vessel-centerline extractions per case [27] (Fig. 6).

Fig. 6

GUI for ground truth annotation of coronary arteries and branches. The same case as represented in Fig. 5 helps to demonstrate the subsequent GUI-based image-data curation with ground truth annotation of coronary artery atherosclerotic plaque extent. This process involves regional color-coding of basic stenosis grade of the previously described significant stenosis (arrow) of the proximal left anterior descending (LAD) coronary artery; both non-obstructive (< 50% luminal narrowing [light pink]) and obstructive (≥ 50% luminal narrowing [dark pink]) portions of the plaque are shown as segmental coatings on the 3D branching arterial tree (D1 = first diagonal branch; LCx = left circumflex coronary artery; RI = ramus intermedius branch)

The annotated vessel extractions (i.e., plaque-annotated) were then converted to 2D mosaic projection view (MPV) displays in order to facilitate data augmentation (DA) of the size of the atherosclerotic-vessel component for Training in algorithm development [30]. DA is accomplished by re-ordering projections, thereby creating new MPVs per vessel used to augment the Training subset (Fig. 7). Through its improvements to both modeling and diseased vs non-diseased balance within the Training subset [37], this DA methodology enhanced AI algorithm development, especially when combined with transfer learning (TL) using pre-trained weighting [30, 37,38,39,40,41].

Fig. 7

Mosaic projection view (MPV) representations of disease-annotated vessel extractions Three MPVs (unique 9 × 2 matrices of arranged 2D projections—original version [30]) of coronary vessel atherosclerosis with variable stenosis are displayed as color maps (right); corresponding plaques are shown on curved CCTA reconstructions (left) (LAD = left anterior descending, LCx = left circumflex, RCA = right coronary artery)

Appendix 3

Inception-V3 (https://cloud.google.com/tpu/docs/inception-v3-advanced) served as the base convolutional neural network. Model weights pre-trained on ImageNet (http://www.image-net.org/) were used for TL during algorithm Training [30, 38,39,40,41]. To refine modeling for CCTA datasets, the final Inception-V3 layer was replaced by a fully connected 1024-node layer with a rectified linear unit (25% dropout to avoid overfitting), followed by sigmoid output function for binary classification [30] (Fig. 8).

Training utilized the Keras library (https://keras.io/) with a TensorFlow-1.8 backend (https://www.tensorflow.org/). Initial learning rate was 0.001 on a stochastic gradient descent optimizer (decay factor 1e−6, with momentum 0.900 and mini-batch size 8); re-training was terminated at 120 epochs. A binary cross-entropy loss function was monitored during Training, and the resulting model was saved only if there was improvement in the Validation accuracy [30].

Fig. 8

Modified Inception-V3 convolutional neural network used for algorithm development

Appendix 4

For phase 2, Clara software stack (https://developer.nvidia.com/clara-medical-imaging) was used because of its support of (1) an interface to Digital Imaging and Communications in Medicine (DICOM) standards (www.dicomstandard.org), (2) a durable deployment of Kubernetes (https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/#clusters-containing-different-types-of-nvidia-gpus), and (3) seamless modular integration with existing image-visualization/analysis tools, including a PACS or a viewer.

The integrated workflow for pre-deployment of the algorithm developed in phase 1 included the following steps: (1) the interpreting physician opened the desired CCTA image-data in a commercial clinical viewer (https://www.siemens-healthineers.com/en-us/medical-imaging-it/advanced-visualization-solutions/syngovia) in order to select the most diagnostically optimal cardiac phase to undergo evaluation by the algorithm; (2) the selected volume was manually forwarded to the DICOM server from where it was sent to the server supporting the GUI (https://www.mevislab.de/) for coronary artery image-processing (including vessel-centerline extraction followed by production of straightened multi-planar reformations (MPRs); (3) following conversion, straightened-MPRs were sent to a Clara Deploy DICOM Adapter (https://docs.nvidia.com/clara/deploy/ngc/DicomAdapter.html) which monitors incoming DICOM images and initiates classification by the algorithm; (4) Clara Deploy platform, hosting a TensorRT Inference Server (https://docs.nvidia.com/deeplearning/sdk/triton-inference-server-guide/docs/index.html), is prompted to make predictions; (5) inference results are updated in the database (https://www.mongodb.com/), signaling readiness for feedback to the interpreting physician; and (6) inference probability values exceeding the threshold (0.5) are graphically displayed as overlays on the coronary artery tree displayed by the GUI.

Using a continuous model integration strategy, an additional model can be evaluated by updating the CLARA Deploy server, via the backend server without workflow disruption, for future selection by the interpreting physician. The application described in phase 2 and pre-deployed for simulated clinical use is illustrated below (Fig. 9).

Fig. 9

Algorithm pre-deployment architecture; while blue components reflect a traditional radiology workflow, orange components are enhancements for AI algorithm deployment with graphic inference feedback to the interpreting physician via the GUI