P08.04: Novel technique for measurement of fetal right myocardial performance index using synchronised images of right ventricular inflow and outflow

Abstract

Methods: 2378 frames from 51 normal fetal cardiac screening movies at 18-20 weeks, were used as a training dataset for machine learning. 701 frames from 28 normal fetal cardiac screening movie used as test data. The movie included the hole images of fetal heart from stomach to base of heart. Correct positions of the heart and peripheral organs were annotated. The positions were following crux, ventricular septum, right atrium, tricuspid valve, right ventricle, left atrium, mitral valve, left ventricle, pulmonary artery, ascending aorta, superior vena cava, descending aorta, stomach spine, umbilical vein, inferior vena cava, pulmonary vein, ductus arteriosus. We calculated the accuracy of each parts of normal fetal heart ultrasound screening movie. Results: The recall ratio of crux, ventricular septum, left ventricle and atrium, right ventricle and atrium, ascending aorta, pulmonary artery, stmach and spine were 97.1%, 69.3%, 96.6% and 90.6%, 84.8% and 96.9%, 61.9%, 100%, 100% and 100%, respectively. Conclusions: A novel system of machine learning automatically detected parts of normal fetal heart at high level. This system can provide us useful information of segmental diagnosis and be a great power for prenatal detection of CHD.

How Automated Techniques Ease Functional Assessment of the Fetal Heart: Applicability of MPI+™ for Direct Quantification of the Modified Myocardial Performance Index

“…Of course, a basic theoretical understanding of fetal anatomy, its technical application and its sonomorphological representation is extremely helpful for measuring the RV-Mod-MPI, but is no longer a mandatory requirement to assess the fetal RV-Mod-MPI due to step-by-step instructions provided by the software. Nevertheless, this understanding, as well as knowledge of the white paper, can reduce common pitfalls and optimize value acquisition [16]. However, extensive medical expertise is required for the evaluation, classification and interpretation of its values in a clinical context.…”

“…The acquisition of images for the calculation of the RV-Mod-MPI should be ideally performed without fetal physical or respiratory movements. Knowing the white paper and following its aspects are worth emphasizing in particular [16]. The angle of insonation should be as small as possible, ideally <15 • .…”

“…The MPI has been developed continuously since (Mod-MPI) by using valve clicks as landmarks to improve inter-and intra-observer agreements and, thus, make the measurement more reliable and reproducible [11][12][13][14][15]. The MPI is the ratio of the isovolumetric to ejection time of cardiac intervals [4,16]. As a non-invasive, pulsed-wave (PW) Doppler-derived measure of global myocardial function, it is currently used to evaluate fetal cardiac anatomy, hemodynamics and function [1].…”

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics – an Assessment of the State of Play

“…6 ). Beyond this, AI methods to analyse Doppler-based cardiac function (modified myocardial performance index (Mod-MPI), previously termed the Tei index) have been developed in recent years and are now commercially available 65 , 66 .…”

“…6 ). Des Weiteren sind in den letzten Jahren KI-Methoden zur dopplerbasierten kardialen Funktionsanalyse (modifizierter myokardialer Performanceindex; Mod-MPI o. Tei-Index) entwickelt worden und mittlerweile auch kommerziell verfügbar 65 , 66 .…”