Objectives To investigate the interobserver and intraobserver variability and corresponding learning curve in a semiautomatic approach for a standardized assessment of the fetal heart (fetal intelligent navigation echocardiography [FINE]). Methods A total of 30 stored spatiotemporal image correlation volume data sets of second‐trimester fetuses were evaluated by 3 physicians with different levels of expertise in fetal echocardiography by using the FINE approach. Data were analyzed regarding the examination time and proper reconstruction of the diagnostic cardiac planes. The completions and numbers of correct depictions of all diagnostic planes were evaluated by a blinded expert (time t0). To determine interobserver and intraobserver variability, the volumes were reassessed after a 4‐week training interval (time t1). Results All operators were able to perform the investigation on all 30 volumes. At t0, the interobserver variability between the beginner and both the advanced (P = .0013) and expert (P < .0001) examiners was high. Focusing on intraobserver variability at t1, the beginner showed a marked improvement (P = .0087), whereas in advanced and expert hands, no further improvement regarding proper achievement of all diagnostic planes could be noticed (P > .999; P = .8383). The beginner also showed improvement in the mean investigation time (t0, 82.8 seconds; t1, 73.4 seconds; P = .0895); nevertheless, the advanced and expert examiners were faster in completing the examination (t1, advanced, 20.9 seconds; expert, 28.3 seconds; each P < .0001). Conclusions Based on our results, the FINE technique is a reliable and easily learned method. The use of this semiautomatic work flow–based approach supports evaluation of the fetal heart in a standardized and time‐saving manner. A semiautomatic evaluation of the fetal heart might be useful in facilitating the detection of fetal cardiac anomalies.

The long-awaited progress in digitalisation is generating huge amounts of medical data every day, and manual analysis and targeted, patient-oriented evaluation of this data is becoming increasingly difficult or even infeasible. This state of affairs and the associated, increasingly complex requirements for individualised precision medicine underline the need for modern software solutions and algorithms across the entire healthcare system. The utilisation of state-of-the-art equipment and techniques in almost all areas of medicine over the past few years has now indeed enabled automation processes to enter – at least in part – into routine clinical practice. Such systems utilise a wide variety of artificial intelligence (AI) techniques, the majority of which have been developed to optimise medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection and classification and, as an emerging field of research, radiogenomics. Tasks handled by AI are completed significantly faster and more precisely, clearly demonstrated by now in the annual findings of the ImageNet Large-Scale Visual Recognition Challenge (ILSVCR), first conducted in 2015, with error rates well below those of humans. This review article will discuss the potential capabilities and currently available applications of AI in gynaecological-obstetric diagnostics. The article will focus, in particular, on automated techniques in prenatal sonographic diagnostics.

(1) Objective: To scrutinize the reliability and the clinical value of routinely used fetal intelligent navigation echocardiography (FINE) static mode (5DHeartStatic™) for accelerated semiautomatic volumetric assessment of the normal fetal heart. (2) Methods: In this study, a total of 296 second and third trimester fetuses were examined by targeted ultrasound. Spatiotemporal image correlation (STIC) volumes of the fetal heart were acquired for further volumetric assessment. In addition, all fetal hearts were scanned by a fast acquisition time volume (1 s). The volumes were analyzed using the FINE software. The data were investigated regarding the number of properly reconstructed planes and cardiac axis. (3) Results: A total of 257 volumes were included for final analysis. The mean gestational age (GA) was 23.9 weeks (14.3 to 37.7 weeks). In 96.9 (standard acquisition time, FINE standard mode) and 94.2% (fast acquisition time, FINE static mode) at least seven planes were reconstructed properly (p = 0.0961, not significant). Regarding the overall depiction rate, the standard mode was able to reconstruct 96.9% of the planes properly, whereas the static mode showed 95.2% of the planes (p = 0.0098). Moreover, there was no significant difference between the automatic measurement of the cardiac axis (37.95 + 9.14 vs. 38.00 + 8.92 degrees, p = 0.8827, not significant). (4) Conclusions: Based on our results, the FINE static mode technique is a reliable method. It provides similar information of the cardiac anatomy compared to conventional STIC volumes assessed by the FINE method. The FINE static mode has the potential to minimize the influence of motion artifacts during volume acquisition and might therefore be helpful concerning volumetric cardiac assessment in daily routine.

Zusammenfassung Hintergrund Der ärztliche Nachwuchs hat disruptive Effekte und macht auch vor dem Fach Frauenheilkunde und Geburtshilfe nicht halt. Noch fokussiert sich der Diskurs auf die Generation Y (1980–1994). Um dem Nachwuchs ein konstruktives Arbeitsumfeld zu bieten, drängt die Zeit. Es gilt, sich dessen Anforderungen an ein solches zu vergegenwärtigen. Zielsetzung Erfassen des Stimmungsbilds des ärztlichen Nachwuchses in der Frauenheilkunde und Geburtshilfe mit anschließender Ableitung praxisrelevanter Aspekte unter Berücksichtigung der künftig dominierenden Generation Z (1995–2009). Methoden Von Januar bis Oktober 2021 wurde eine deskriptive Querschnittserhebung des ärztlichen Nachwuchses ausbildender Kliniken im Fach Frauenheilkunde und Geburtshilfe durchgeführt. Es wurden 81 Fragen zu 6 Themen online abgefragt. Ergebnis Ausgewertet wurden 122 Fragebögen (n = 122): 28 % (n = 33) schätzen die Arbeitsbelastung als sehr hoch, 56 % (n = 67) als hoch ein. Zwei Drittel (n = 81) arbeiten wöchentlich 40–59 h. Den Anteil delegierbarer Tätigkeiten beziffern 67 % (n = 80) auf > 25 %. 88 % (n = 105) verbringen 25–75 % der täglichen Arbeitszeit mit Dokumentieren. 92 % (n = 109) wünschen sich regelmäßige Ober- bzw. Chefarztvisiten, 81 % (n = 95) beurteilen die Weiterbildung schlechter als gut. Für 32 % (n = 38) besteht ein ausgeglichenes Verhältnis zwischen Gesundheit und Beruf, 25 % (n = 29) beurteilen die Arbeitsbedingungen als familienfreundlich, und 88 % (n = 102) wären bereit, bei anhaltender Unzufriedenheit den Arbeitgeber zu wechseln. Schlussfolgerung Den Nachwuchs dominieren Forderungen nach Weiterbildung, Teilzeit, Sinnhaftigkeit, Vereinbarkeit von Familie und Beruf, Wertschätzung und Selbstfürsorge. Lösungskonzepte, um diesen gerecht zu werden, stünden zur Verfügung.