Diagnostic accuracy of artificial intelligence for identifying systolic and diastolic cardiac dysfunction in the...

Cardiac point-of-care ultrasound (POCUS) can evaluate for systolic and diastolic dysfunction to inform care in the Emergency Department (ED). However, accurate assessment can be limited by user experience. Artificial intelligence (AI) has been proposed as a model to increase the accuracy of cardiac POCUS. However, there is limited evidence of the accuracy of AI in the clinical environment. The objective of this study was to determine the diagnostic accuracy of AI for identifying systolic and diastolic dysfunction compared with expert reviewers.

This was a prospective, observational study of adult ED patients aged ≥45 years with risk factors for systolic and diastolic dysfunction. Ultrasound fellowship-trained physicians used an ultrasound machine with existing AI software and obtained parasternal long axis, parasternal short axis, and apical 4-chamber views of the heart. Systolic dysfunction was defined as ejection fraction (EF) < 50 % in at least two views using visual assessment or E-point septal separation >10 mm. Diastolic dysfunction was defined as an E:A < 0.8, or ≥ 2 of the following: septal e' < 7 cm/s or lateral e' < 10 cm/s, E:e' > 14, or left atrial volume > 34 mL/m². AI was subsequently used to measure EF, E, A, septal e', and lateral e' velocities. The gold standard was systolic or diastolic dysfunction as assessed by two independent physicians with discordance resolved via consensus. We performed descriptive statistics (mean ± standard deviation) and calculated the sensitivity, specificity, positive likelihood ratio (LR+), and negative likelihood ratio (LR-) of the AI in determining systolic and diastolic dysfunction with 95 % confidence interval (CI). Subgroup analyses were performed by body mass index (BMI).

We enrolled 220 patients, with 11 being excluded due to inadequate images, resulting in 209 patients being included in the study. Mean age was 60 ± 9 years, 51.7 % were women, and the mean BMI was 31 ± 8.1 mg/kg². For assessing systolic dysfunction, AI was 85.7 % (95 %CI 57.2 % to 98.2 %) sensitive and 94.8 % (95 %CI 90.6 % to 97.5 %) specific with a LR+ of 16.4 (95 %CI 8.6 to 31.1) and LR- of 0.15 (95 % CI 0.04 to 0.54). For assessing diastolic dysfunction, AI was 91.9 % (95 %CI 85.6 % to 96.0 %) sensitive and 94.2 % (95 %CI 87.0 % to 98.1 %) specific with a LR+ of 15.8 (95 %CI 6.7 to 37.1) and a LR- of 0.09 (0.05 to 0.16). When analyzed by BMI, results were similar except for lower sensitivity in the BMI ≥ 30 vs BMI < 30 (100 % vs 80 %).

When compared with expert assessment, AI had high sensitivity and specificity for diagnosing both systolic and diastolic dysfunction.