Cardiac Phase Space Tomography: A novel method of assessing coronary artery disease utilizing machine learning

Abstract

BackgroundArtificial intelligence (AI) techniques are increasingly applied to cardiovascular (CV) medicine in arenas ranging from genomics to cardiac imaging analysis. Cardiac Phase Space Tomography Analysis (cPSTA), employing machine-learned linear models from an elastic net method optimized by a genetic algorithm, analyzes thoracic phase signals to identify unique mathematical and tomographic features associated with the presence of flow-limiting coronary artery disease (CAD). This novel approach does not require radiation, contrast media, exercise, or pharmacological stress. The objective of this trial was to determine the diagnostic performance of cPSTA in assessing CAD in patients presenting with chest pain who had been referred by their physician for coronary angiography.MethodsThis prospective, multicenter, non-significant risk study was designed to: 1) develop machine-learned algorithms to assess the presence of CAD (defined as one or more ≥ 70% stenosis, or fractional flow reserve ≤ 0.80) and 2) test the accuracy of these algorithms prospectively in a naïve verification cohort. This report is an analysis of phase signals acquired from 606 subjects at rest just prior to angiography. From the collective phase signal data, features were extracted and paired with the known angiographic results. A development set, consisting of signals from 512 subjects, was used for machine learning to determine an algorithm that correlated with significant CAD. Verification testing of the algorithm was performed utilizing previously untested phase signals from 94 subjects.ResultsThe machine-learned algorithm had a sensitivity of 92% (95% CI: 74%-100%) and specificity of 62% (95% CI: 51%-74%) on blind testing in the verification cohort. The negative predictive value (NPV) was 96% (95% CI: 85%-100%).ConclusionsThese initial multicenter results suggest that resting cPSTA may have comparable diagnostic utility to functional tests currently used to assess CAD without requiring cardiac stress (exercise or pharmacological) or exposure of the patient to radioactivity.

Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence

“…The machine-learned algorithm cohort consisted of phase signals from 512 patients with 94 patients serving as the verification cohort. Blindly testing the cPSA System in the naïve verification cohort demonstrated a sensitivity of 92% (95% CI: 74%-100%) and specificity of 62% (95% CI: 51%-74%) for the assessment of significant CAD, which is comparable to commonly performed standard of care functional testing ( Table 2 ) [ 7 – 9 ]. The negative predictive value (NPV) was 96% (95% CI: 85%-100%), and the PPV was 46% (95% CI: 33%-62%) [ 7 ].…”

“…The primary objective of the Coronary Artery Disease Learning and Algorithm Development (CADLAD) trial was designed to collect resting phase signals from eligible subjects using the PSR prior to ICA to machine learn and test an algorithm for detecting the presence of significant CAD in symptomatic patients [ 7 ]. In addition, machine-learned algorithms were developed and tested to identify the location of significant CAD.…”

“…The bridge of artificial intelligence to cardiovascular medicine has opened up new avenues for novel cardiovascular diagnostics that may significantly enhance the care of patients [ 4 – 6 ]. Unlike traditional imaging modalities to assess for CAD, cardiac phase space analysis (cPSA) is a dynamic assessment that captures data related to electrical signals over consecutive cardiac cycles which is unique to a given individual [ 7 ]. The resultant thoracic phase signals are analyzed by cPSA to quantify physiological and mathematical features associated with coronary stenosis without the need for a change in physiologic status such as stress-induced vasodilation.…”

La inteligencia artificial en la educación médica y la predicción en salud

“…En este estudio, los autores utilizaron esta herramienta para evaluar a pacientes con enfermedad coronaria y dolor en el pecho que fueron remitidos por el médico para una angiografía. Se estudiaron 606 pacientes y los resultados mostraron 92% de sensibilidad, 62% de especificidad y 96% de valor predictivo de enfermedad coronaria 15,16,17,18 . La ecocardiografía es actualmente uno de los métodos de imagen más utilizados en cardiología, la ecografía también tiene ventajas en cuanto a su portabilidad, rapidez y accesibilidad.…”

Addressing bias: artificial intelligence in cardiovascular medicine

“…These variabilities pose a challenge to the AI system, in which their learned ground truth is dependent on these human interpretations. 1 Current studies using AI in cardiac imaging and diagnostics show great promise; [2][3][4] however, these studies might be limited to sample sizes at institutional or regionally distributed levels, or have not yet been externally validated by operators outside of the study or used on a separate population. 1 Second, in machine learning models in which algorithms are designed to recognise patterns, there is a tendency to overfit the dataset because of an inability to distinguish a true contributing factor from noise.…”