From Syntactic to Semantic Interoperability Using a Hyperontology in the Oncology Domain.

Interoperability is crucial to overcoming various challenges of data integration in the healthcare domain. While OMOP and FHIR data standards handle syntactic heterogeneity among heterogeneous data sources, ontologies support semantic interoperability to overcome the complexity and disparity of healthcare data. This study proposes an ontological approach in the context of the EUCAIM project to support semantic interoperability among distributed big data repositories that have applied heterogeneous cancer image data models using a semantically well-founded Hyperontology for the oncology domain.

External validation of models to estimate gestational age in the second and third trimester using ultrasound: A prospective multicentre observational study.

Objectives: Accurate assessment of gestational age (GA) is important at both individual and population levels. The most accurate way to estimate GA in women who book late in pregnancy is unknown. The aim of this study was to externally validate the accuracy of equations for GA estimation in late pregnancy and to identify the best equation for estimating GA in women who do not receive an ultrasound scan until the second or third trimester.

Prenatal diagnosis of pontocerebellar hypoplasia with postnatal follow-up.

Objective: To describe the MR features enabling prenatal diagnosis of pontocerebellar hypoplasia (PCH).

Method: This was a retrospective single monocentre study. The inclusion criteria were decreased cerebellar biometry on dedicated neurosonography and available fetal Magnetic Resonance Imaging (MRI) with PCH diagnosis later confirmed either genetically or clinically on post-natal MRI or by autopsy.

Prior-Guided Attribution of Deep Neural Networks for Obstetrics and Gynecology.

Obstetrics and gynecology (OB/GYN) are areas of medicine that specialize in the care of women during pregnancy and childbirth and in the diagnosis of diseases of the female reproductive system. Ultrasound scanning has become ubiquitous in these branches of medicine, as breast or fetal ultrasound images can lead the sonographer and guide him through his diagnosis. However, ultrasound scan images require a lot of resources to annotate and are often unavailable for training purposes because of confidentiality reasons, which explains why deep learning methods are still not as commonly used to solve OB/GYN tasks as in other computer vision tasks.