Can echocardiographic assessment of diastolic function be automated?

Abstract

Echocardiographic evaluation of left ventricular diastolic function relies on a multi-pronged algorithm, which incorporates Doppler-based and volumetric parameters. Integration of clinical data in diastolic assessment is recommended, though not clearly outlined. We sought to develop an automated tool for diastolic function, compare its performance to human-generated diagnoses and identify the common sources of error. Our software tool is based on the 2016 diastolic guidelines algorithm, which uses 8 parameters as input, with 10 conditions as the logic and 5 possible outputs as final diagnoses. Initially, we prospectively studied 563 patients whose diastolic function was independently evaluated by an expert echocardiographer and by the automated tool. Incongruent cases were further analyzed, after which features of myocardial disease were integrated into a refined version of the software that was tested in an independent cohort of 1106 patients. In the initial analysis, 202/563 grades (36%) were incongruent between the automated and human reads, with the highest rate of discordance for mild and indeterminate categories. In 17% of cases, human diagnoses differed from that dictated by the algorithm due to integration of clinical factors. Follow-up analysis using the refined automated tool did not improve the discordance rate (440/1106; 40%). There was more discordance in cases of: age > 40years, impaired mitral inflow patterns (E/A < 0.8) and reduced mitral e' values. Further analysis revealed differences in how readers interpreted the interaction between these factors and diastolic function, which could not be incorporated into the automated tool. In conclusion,although assessment of diastolic function relies on an algorithm that can be automated, this algorithm does not include clear guidance on how to incorporate age, or age-related changes in Doppler-based parameters, often resulting in discordant diagnoses. Standardized interpretation of these factors is needed to improve the reproducibility of diastolic function grading by human readers and the accuracy of the automated classification.