Assessment of vascular size and of its phasic changes by ultrasound is important for the management of many clinical conditions. For example, a dilated and stiff inferior vena cava reflects increased intravascular volume and identifies patients with heart failure at greater risk of an early death. However, lack of standardization and sub-optimal intra- and inter- operator reproducibility limit the use of these techniques. To overcome these limitations, we developed two image-processing algorithms that quantify phasic vascular deformation by tracking wall movements, either in long or in short axis. Prospective studies will verify the clinical applicability and utility of these methods in different settings, vessels and medical conditions.

The non-invasive estimation of right atrial pressure (RAP) would be a key advancement in several clinical scenarios, in which the knowledge of central venous filling pressure is vital for patients’ management. The echocardiographic estimation of RAP proposed by Guidelines, based on inferior vena cava (IVC) size and respirophasic collapsibility, is exposed to operator and patient dependent variability. We propose novel methods, based on semi-automated edge-tracking of IVC size and cardiac collapsibility (cardiac caval index—CCI), tested in a monocentric retrospective cohort of patients undergoing echocardiography and right heart catheterization (RHC) within 24 h in condition of clinical and therapeutic stability (170 patients, age 64 ± 14, male 45%, with pulmonary arterial hypertension, heart failure, valvular heart disease, dyspnea, or other pathologies). IVC size and CCI were integrated with other standard echocardiographic features, selected by backward feature selection and included in a linear model (LM) and a support vector machine (SVM), which were cross-validated. Three RAP classes (low < 5 mmHg, intermediate 5–10 mmHg and high > 10 mmHg) were generated and RHC values used as comparator. LM and SVM showed a higher accuracy than Guidelines (63%, 71%, and 61% for LM, SVM, and Guidelines, respectively), promoting the integration of IVC and echocardiographic features for an improved non-invasive estimation of RAP.

The influence of large vessels on near infrared spectroscopy (NIRS) measurement is generally considered negligible. Aim of this study is to test the hypothesis that changes in the vessel size, by varying the amount of absorbed NIR light, could profoundly affect NIRS blood volume indexes. Changes in haemoglobin concentration (tHb) and in tissue haemoglobin index (THI) were monitored over the basilic vein (BV) and over the biceps muscle belly, in 11 subjects (7 M – 4 F; age 31 ± 8 year) with simultaneous ultrasound monitoring of BV size. The arm was subjected to venous occlusion, according to two pressure profiles: slow (from 0 to 60 mmHg in 135 s) and rapid (0 to 40 mmHg maintained for 30 s). Both tHb and THI detected a larger blood volume increase (1.7 to 4 fold; p < 0.01) and exhibited a faster increase and a greater convexity on the BV than on the muscle. In addition, NIRS signals from BV exhibited higher correlation with changes in BV size than from muscle (r = 0.91 vs 0.55, p < 0.001 for THI). A collection of individual relevant recordings is also included. These results challenge the long-standing belief that the NIRS measurement is unaffected by large vessels and support the concept that large veins may be a major determinant of blood volume changes in multiple experimental conditions.

Objectives-Inferior vena cava (IVC) pulsatility quantified by the Caval Index (CI) is characterized by poor reliability, also due to the irregular magnitude of spontaneous respiratory activity generating the major pulsatile component. The aim of this study was to test whether the IVC cardiac oscillatory component could provide a more stable index (Cardiac CI-CCI) compared to CI or respiratory CI (RCI).Methods-Nine healthy volunteers underwent long-term monitoring in supine position of IVC, followed by 3 minutes passive leg raising (PLR). CI, RCI, and CCI were extracted from video recordings by automated edge-tracking and CCI was averaged over each respiratory cycle (aCCI). Cardiac output (CO), mean arterial pressure (MAP) and heart rate (HR) were also recorded during baseline (1 minutes prior to PLR) and PLR (first minute).Results-In response to PLR, all IVC indices decreased (P < .01), CO increased by 4 AE 4% (P = .055) while HR and MAP did not vary. The Coefficient of Variation (CoV) of aCCI (13 AE 5%) was lower than that of CI (17 AE 5%, P < .01), RCI (26 AE 7%, P < .001) and CCI (25 AE 7%, P < .001). The mutual correlations in time of the indices were 0.81 (CI-RCI), 0.49 (CI-aCCI) and 0.2 (RCI-aCCI).Conclusions-Long-term IVC monitoring by automated edge-tracking allowed us to evidence that 1) respiratory and averaged cardiac pulsatility components are uncorrelated and thus carry different information and 2) the new index aCCI, exhibiting the lowest CoV while maintaining good sensitivity to blood volume changes, may overcome the poor reliability of CI and RCI.

Ultrasound (US) scans of inferior vena cava (IVC) are widely adopted by healthcare providers to assess patients’ volume status. Unfortunately, this technique is extremely operator dependent. Recently, new techniques have been introduced to extract stable and objective information from US images by automatic IVC edge tracking. However, these methods require prior interaction with the operator, which leads to a waste of time and still makes the technique partially subjective. In this paper, two deep learning methods, YOLO (You only look once) v4 and YOLO v4 tiny networks, commonly used for fast object detection, are applied to identify the location of the IVC and to recognise the either long or short axis view of the US scan. The output of these algorithms can be used to remove operator dependency, to reduce the time required to start an IVC analysis, and to automatically recover the vein if it is lost for a few frames during acquisition. The two networks were trained with frames extracted from 18 subjects, labeled by 4 operators. In addition, they were also trained on a linear combination of two frames that extracted information on both tissue anatomy and movement. We observed similar accuracy of the two models in preliminary tests on the entire dataset, so that YOLO v4 tiny (showing much lower computational cost) was selected for additional cross-validation in which training and test frames were taken from different subjects. The classification accuracy was approximately 88% when using original frames, but it reached 95% when pairs of frames were processed to also include information on tissue movements, indicating the importance of accounting for tissue motion to improve the accuracy of our IVC detector.