Comparison between trans-vaginal and trans-abdominal ultrasound examination of the cervix in the second trimester of pregnancy: a prospective study.

OBJECTIVES: To compare transvaginal (TV) and trans-abdominal (TA) ultrasound assessment of cervical length (CL) at the time of the second-trimester scan for fetal anomalies. METHODS: This was a prospective study including consecutive pregnant women attending the low-risk ultrasound clinic of two fetal medicine centres in Italy. The inclusion criteria were women between 19 + 0 and 22 + 0 weeks of gestation, attending the prenatal ultrasound clinic for the routine second trimester screening for fetal anomalies. The primary outcome was to compare the CL measurement obtained at TV compared to TA ultrasound; the secondary outcome was to report the inter and intra-observer variability of CL measured with the two different approaches. All women underwent TV and TA assessment of the cervix performed by two experienced certified operators, blinded to each other. Intra-class correlation coefficients (ICC) and Bland-Altman analyses were used to analyse the data. RESULTS: Two hundred and fifty women were included in the analysis. All women had anteverted uterus. The mean gestational age at ultrasound was 20.7 ± 0.7 weeks; 1.2 % (3/250) scans were performed at 19 weeks, 49.2 % (123/250) at 20 weeks, 44.8 % (112/250) at 21 weeks and 4.8 % (12/250) at 22 weeks of gestations. Identification of the major landmarks of CL at TA ultrasound was achieved in all the included cases. There was good reliability between CL measured at TA (ICC 0.95, 95 % CI 0.93-0.97 for observer 1 and 0.92 %, 95 % CI 0.89-0.94 for observer 2) and TV ultrasound 0.97, 95 % CI 0.96-0.98 for observer 1 and 0.96, 95 % CI 0.95-0.97 for observer 2). There was also good reliability between the two observers for both the TA and TV assessment of the CL. Mean TA CL was 41.4 ± 5.5 for observer 1 and 40.5 ± 4.8 for observer 2 with no significant differences between the two measurements (mean difference 0.92 mm, 95 % CI -9.7 to 11.2). Likewise, there was no difference between the CL measured at TV ultrasound between the two observers (mean difference -0.83 mm, 95 % CI -5.97 to 4.30). Finally, there was no difference in the mean CL measured at TA compared to TV, either considering the overall population of women (mean difference: -0.43, 955 CI -8.65 to 7.79), or when stratifying the analysis according to the parity status and the operator. CONCLUSIONS: Among experienced operators, there was no difference between TV and TA ultrasound assessment of the CL at the time of the routine anomaly scan for fetal anomaly.

AI and Blockchain as New Triggers in the Education Arena.

Several scholars have examined the potential use of AI and Blockchain in education, primarily focusing on the contributions of such technologies with a goal to improve learning possibilities and outcomes for students [...].

The role of artificial intelligence in healthcare: a structured literature review.

BACKGROUND/INTRODUCTION: Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. METHODS: The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. RESULTS: The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. CONCLUSIONS: The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

An Evaluation Testbed for Locomotion in Virtual Reality.

A common operation performed in Virtual Reality (VR) environments is locomotion. Although real walking can represent a natural and intuitive way to manage displacements in such environments, its use is generally limited by the size of the area tracked by the VR system (typically, the size of a room) or requires expensive technologies to cover particularly extended settings. A number of approaches have been proposed to enable effective explorations in VR, each characterized by different hardware requirements and costs, and capable to provide different levels of usability and performance. However, the lack of a well-defined methodology for assessing and comparing available approaches makes it difficult to identify, among the various alternatives, the best solutions for selected application domains. To deal with this issue, this article introduces a novel evaluation testbed which, by building on the outcomes of many separate works reported in the literature, aims to support a comprehensive analysis of the considered design space. An experimental protocol for collecting objective and subjective measures is proposed, together with a scoring system able to rank locomotion approaches based on a weighted set of requirements. Testbed usage is illustrated in a use case requesting to select the technique to adopt in a given application scenario.