A multicenter, international study in the May issue of The Journal of Nuclear Medicine has demonstrated for the first time that the diagnosis of obstructive coronary artery disease (CAD) can be improved by using deep learning analysis of upright and supine SPECT myocardial perfusion imaging (MPI).
SPECT MPI, which is widely used for its diagnosis, shows how well the heart muscle is pumping and examines blood flow through the heart during exercise and at rest. On new cameras with a patient imaged in sitting position, two positions (semi-upright and supine) are routinely used to mitigate attenuation artifacts. The current quantitative standard for analyzing MPI data is to calculate the combined total perfusion deficit (TPD) from these two positions. Visually, physicians need to reconcile information available from two views.
Deep convolutional neural networks, often referred to as deep learning (DL), go beyond machine learning using algorithms. They directly analyze visual data, learn from them, and make intelligent findings based on the image information.
For this study, DL analysis of data from the two-position stress MPI was compared with the standard TPD analysis of 1,160 patients without known coronary artery disease. Patients underwent stress MPI with the nuclear medicine radiotracer technetium sestamibi. New-generation solid-state SPECT scanners in four different centers were used, and images were quantified at the Cedars-Sinai Medical Center in Los Angeles, Calif. All patients had on-site clinical reads and invasive coronary angiography correlations within six months of MPI.
Obstructive disease was defined as at least 70% narrowing of the three major coronary arteries and at least 50% for the left main coronary artery. During the validation procedure, four different DL models were trained (each using data from three centers) and then were evaluated on the one center left aside. Predictions for four centers were merged to have an overall estimation of the multicenter performance.
The study revealed that 718 (62%) patients and 1,272 of 3,480 (37%) arteries had obstructive disease. Per-patient sensitivity improved from 61.8% with TPD to 65.6% with DL, and per-vessel sensitivity improved from 54.6% with TPD to 59.1% with DL. In addition, DL had a sensitivity of 84.8%, versus 82.6% for an on-site clinical read.
The results clearly show that DL improves MPI interpretation over current methods. “These findings were demonstrated for the first time in a rigorous, repeated external validation,” points out Piotr J. Slomka, PhD, at Cedars-Sinai Medical Center, affirming that “the latest developments in artificial intelligence can be efficiently leveraged to enhance the accuracy of existing nuclear medicine techniques.”
The focus is always on advancing patient care, and Slomka says, “Patients will benefit from increased diagnostic accuracy and reproducibility of SPECT myocardial perfusion imaging when such systems are deployed clinically.”
Featured image: Prediction of obstructive CAD from upright and supine stress MPI. Short/long axis views, polar maps depicting normalized radiotracer count distribution and perfusion defects (top), and predictions by cTPD and DL (bottom) are shown for two patients with obstructive CAD. (A) In 79-year-old man (85% proximal LAD stenosis) quantified with normal cTPD (per-patient cTPD, 3% and per-vessel cTPD, 1%), DL correctly identified LAD disease. Patient had body mass index of 30 kg/m2 and diabetes and underwent exercise stress MPI. (B) In 62-year-old woman (70% mid LAD stenosis, 95% proximal LCX stenosis, and 80% proximal RCA stenosis) with cTPD abnormal for one vessel only, DL correctly identified triple-vessel disease. Patient had body mass index of 25 kg/m2, dyslipidemia, and family history of cardiac disease and underwent exercise stress MPI. BMI = body mass index. Courtesy of Society of Nuclear Medicine and Molecular Imaging.