Artificial intelligence (AI) versus expert: A comparison of left ventricular outflow tract velocity time integral (LVOT-VTI) assessment between ICU doctors and an AI tool

人工智能 (AI) 与专家:ICU 医生与 AI 工具在左心室流出道速度时间积分 (LVOT-VTI) 评估方面的比较

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Abstract

PURPOSE: The application of point of care ultrasound (PoCUS) in medical education is a relatively new course. There are still great differences in the existence, quantity, provision, and depth of bedside ultrasound education. The left ventricular outflow tract velocity time integral (LVOT-VTI) has been successfully used in several studies as a parameter for hemodynamic management of critically ill patients, especially in the evaluation of fluid responsiveness. While LVOT-VTI has been broadly used, valuable applications using artificial intelligence (AI) in PoCUS is still limited. We aimed to identify the degree of correlation between auto LVOT-VTI and the manual LVOT-VTI acquired by PoCUS trained ICU doctors. METHODS: Among the 58 ICU doctors who attended PoCUS training from 1 September 2019 to 30 November 2020, 46 ICU doctors who trained for more than 3 months were enrolled. At the end of PoCUS training, each of the enrolled ICU doctors acquired echocardiography parameters of a new ICU patient in 2 h after new patient was admitted. One of the two bedside expert sonographers would take standard echocardiogram of new ICU patients within 24 h. For ICU doctors, manual LVOT-VTI was obtained for reference and auto LVOT-VTI was calculated instantly by using an AI software tool. Based on the image quality of the auto LVOT-VTI, ICU patients was separated into ideal group (n = 31) and average group (n = 15). RESULTS: Left ventricular end-diastolic dimension (LVEDd, p = 0.1028), left ventricular ejection fraction (LVEF, p = 0.3251), left atrial dimension (LA-d, p = 0.0962), left ventricular E/A ratio (p = 0.160), left ventricular wall motion (p = 0.317) and pericardial effusion (p = 1) had no significant difference between trained ICU doctors and expert sonographer. ICU patients in average group had greater sequential organ failure assessment (SOFA) score (7.33 ± 1.58 vs. 4.09 ± 0.57, p = 0.022) and lactic acid (3.67 ± 0.86 mmol/L vs. 1.46 ± 0.12 mmol/L, p = 0.0009) with greater value of LVEDd (51.93 ± 1.07 vs. 47.57 ± 0.89, p = 0.0053), LA-d (39.06 ± 1.47 vs. 35.22 ± 0.98, p = 0.0334) and percentage of decreased wall motion (p = 0.0166) than ideal group. There were no significant differences of δLVOT-VTI (|manual LVOT-VTI - auto LVOT-VTI|/manual VTI*100%) between the two groups (8.8% ± 1.3% vs. 10% ± 2%, p = 0.6517). Statistically, significant correlations between manual LVOT-VTI and auto LVOT-VTI were present in the ideal group (R(2)  = 0.815, p = 0.00) and average group (R(2)  = 0.741, p = 0.00). CONCLUSIONS: ICU doctors could achieve the satisfied level of expertise as expert sonographers after 3 months of PoCUS training. Nearly two thirds of the enrolled ICU doctors could obtain the ideal view and one third of them could acquire the average view. ICU patients with higher SOFA scores and lactic acid were less likely to acquire the ideal view. Manual and auto LVOT-VTI had statistically significant agreement in both ideal and average groups. Auto LVOT-VTI in ideal view was more relevant with the manual LVOT-VTI than the average view. AI might provide real-time guidance among novice operators who lack expertise to acquire the ideal standard view.

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