Citations

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.

Abstract Rationale Current guidelines recognize the utility of provocative maneuvers during right heart catheterization to aid the diagnosis of pulmonary hypertension. Few studies have compared the performance of different provocation maneuvers. Objectives To assess the hemodynamic correlation among three provocative maneuvers, including their effect on pulmonary hypertension classification. Methods This prospective trial was conducted between October 2016 and May 2018. Adult patients underwent three provocative maneuvers during right heart catheterization: passive leg raise (PLR), load-targeted supine bicycle exercise, and rapid crystalloid fluid infusion. Patients were classified as follows: no pulmonary hypertension, precapillary pulmonary hypertension, isolated postcapillary pulmonary hypertension, combined pre- and postcapillary pulmonary hypertension, and uncategorized pulmonary hypertension. We assessed the hemodynamic changes associated with each maneuver. We also assessed whether provocative maneuvers led to hemodynamic reclassification of the patient to either postcapillary pulmonary hypertension with provocation or exercise pulmonary hypertension. Results Eighty-five patients (mean age 62 ± 12 years, 53% women) were included. Correlation between exercise and fluid challenge was moderate to strong (0.49–0.82; P < 0.001) for changes in right atrial pressure, mean pulmonary arterial pressure, pulmonary arterial wedge pressure, and cardiac index from baseline. Correlation between PLR and exercise (0.4–0.65; P < 0.001) and between PLR and fluid challenge (0.45–0.6; P < 0.001) was moderate for changes in right atrial pressure, mean pulmonary arterial pressure, pulmonary arterial wedge pressure, pulmonary vascular resistance, and cardiac index. Hemodynamic correlation between other provocative maneuvers was poor. Depending on provocative maneuver and classification criteria, there was significant variation in the number of patients reclassified as having exercise pulmonary hypertension (3–50%) or postcapillary pulmonary hypertension with provocation (11–48%). Conclusions Hemodynamic determinations during exercise and fluid challenge showed moderate to strong hemodynamic correlation. Moderate hemodynamic correlation was seen between PLR and exercise or fluid challenge. Although some provocative maneuvers demonstrate good hemodynamic correlation, there is inconsistency when using these maneuvers to identify patients with postcapillary or exercise pulmonary hypertension.