Assessment of the inferior vena cava collapsibility from subcostal and trans-hepatic imaging using both M-mode or artificial intelligence: a prospective study on healthy volunteers.

Abstract

Background Assessment of the inferior vena cava (IVC) respiratory variation may be clinically useful but imaging from subcostal (SC, sagittal) region is not always feasible. It is unclear if coronal trans-hepatic (TH) IVC imaging provides interchangeable results as compared to SC imaging. In this context, the use of artificial intelligence (AI) with software for automated border detection may be clinically helpful but needs validation. Methods We conducted a prospective observational study in healthy volunteers assessing the IVC collapsibility (IVCc) in both SC and TH imaging; IVCc measures were taken in M-mode and with the AI method. We calculated the mean bias and the limits of agreement (LoA), as well as the intra-class correlation (ICC) coefficient with 95% confidence intervals. Results Sixty volunteers were included; IVC was not visualized in five of them (n = 2, both for SC and TH windows, 3.3%; n = 3 IVC not seen in TH approach, 5%). Compared with standard M-mode, the AI assessment showed good accuracy both for SC (IVCc: bias − 0.7%, LoA [-24.9;23.6]) and TH approach (IVCc: bias 3.7%, LoA [-14.9;22.3]); moreover, the ICC coefficients showed moderate reliability: 0.57 [0.36;0.73] in SC, and 0.72 [0.55;0.83] in TH. Comparing results obtained at different anatomical sites (SC vs TH), the results produced by M-mode assessment were not interchangeable (IVCc: bias 13.9%, LoA [-18.1;45.8]). When this evaluation was performed with the AI software, such difference became smaller: IVCc bias 7.7%, LoA [-19.2;34.6]. The correlation between SC and TH assessments was poor for M-mode (ICC = 0.08 [-0.18;0.34]), whilst moderate for AI (ICC = 0.69 [0.52;0.81]). Conclusions The AI software shows good accuracy when compared with the traditional M-mode IVCc assessment conducted at the same anatomical site (both at SC or TH level). There are clinically relevant differences when comparing results obtained at different sites (SC vs TH). Although AI may reduce differences between sagittal and coronal IVCc measurements, the results from different sites do not seem clinically interchangeable.

The effect of prone positioning on maternal haemodynamics and fetal wellbeing in the third trimester–A primary cohort study with a scoping review

“…This could facilitate exploration into the impact of differing abdominal girth and uteroplacental circulation on maternal haemodynamics, thereby providing insight into the mechanism linking prone position and altered maternal haemodynamics. Ultrasound assessment of the inferior vena cava was beyond the scope of this study, but could be used to confirm or refute the hypothesis of prone-induced vena caval compression in future studies, using the transhepatic view [22]. Furthermore, development of the cushion, in terms of shape and consistency, could provide insight into the effect of varying degrees of vena caval compression on maternal haemodynamics.…”

Evaluation of an artificial intelligence-based system for echocardiographic estimation of right atrial pressure

“…Instead of using traditional ML methods, recent studies [ 15 , 16 ] employed advanced deep learning (DL) methods such as long short-term memory (LSTM) for predicting fluid responsiveness in critically ill patients based on the analysis of IVC collapsibility. Other methods for automated IVC analysis can be found in [ 8 , 18 , 19 ].…”