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Objective To evaluate prospectively the performance of Fetal Intelligent Navigation Echocardiography (FINE) applied to spatiotemporal image correlation (STIC) volume datasets of the normal fetal heart. Methods In all women between 19 and 30 weeks of gestation with a normal fetal heart, an attempt was made to acquire STIC volume datasets of the apical four-chamber view if the following criteria were met: 1) fetal spine located between the 5- and 7-o’clock positions; 2) minimal or absent shadowing (including a clearly visible transverse aortic arch); 3) absence of fetal breathing, hiccups, or movement; and 4) adequate image quality. Each STIC volume successfully acquired was evaluated by STICLoop™ to determine its appropriateness before applying the FINE method. Visualization rates of fetal echocardiography views using diagnostic planes and/or Virtual Intelligent Sonographer Assistance (VIS-Assistance®) were calculated. Results One or more STIC volumes (365 in total) were successfully obtained in 72.5% (150/207) of women undergoing ultrasound examination. Of the 365 volumes evaluated by STICLoop, 351 (96.2%) were considered to be appropriate. From the 351 STIC volumes, only one STIC volume per patient (n=150) was analyzed using the FINE method; consequently, nine fetal echocardiography views were generated in 76–100% of cases using diagnostic planes only; in 98–100% of cases using VIS-Assistance only; and in 98–100% of cases when using a combination of diagnostic planes and/or VIS-Assistance. Conclusions In women between 19 and 30 weeks of gestation with a normal fetal heart undergoing prospective sonographic examination, STIC volumes can be successfully obtained in 72.5% of cases. The FINE method can be applied to generate nine standard fetal echocardiography views in 98–100% of these cases using a combination of diagnostic planes and or VIS-Assistance. This suggests that FINE could be implemented in fetal cardiac screening programs.

Whole-genome sequencing (WGS) is a fundamental technology for research to advance precision medicine, but the limited availability of portable and user-friendly workflows for WGS analyses poses a major challenge for many research groups and hampers scientific progress. Here we present Sarek, an open-source workflow to detect germline variants and somatic mutations based on sequencing data from WGS, whole-exome sequencing (WES), or gene panels. Sarek features (i) easy installation, (ii) robust portability across different computer environments, (iii) comprehensive documentation, (iv) transparent and easy-to-read code, and (v) extensive quality metrics reporting. Sarek is implemented in the Nextflow workflow language and supports both Docker and Singularity containers as well as Conda environments, making it ideal for easy deployment on any POSIX-compatible computers and cloud compute environments. Sarek follows the GATK best-practice recommendations for read alignment and pre-processing, and includes a wide range of software for the identification and annotation of germline and somatic single-nucleotide variants, insertion and deletion variants, structural variants, tumour sample purity, and variations in ploidy and copy number. Sarek offers easy, efficient, and reproducible WGS analyses, and can readily be used both as a production workflow at sequencing facilities and as a powerful stand-alone tool for individual research groups. The Sarek source code, documentation and installation instructions are freely available at https://github.com/nf-core/sarek and at https://nf-co.re/sarek/.