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

Abstract

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.

A Clinical Approach to Semiautomated Three-Dimensional Fetal Brain Biometry—Comparing the Strengths and Weaknesses of Two Diagnostic Tools: 5DCNS+TM and SonoCNSTM

“…Volumetric approaches might have the potential to be used on a large scale because 3D ultrasound is reported to be a cheap, simple and accessible technique [ 46 ]. The stored volumes can be used for teaching, training and remote solutions [ 13 , 14 , 47 ]. In fact, assisting tools like SonoCNS™ and 5DCNS+™ might effectively aid in detailed neurosonographic examination, which is of great importance, since a broad variability in quality and evaluability of ultrasound scans has been reported [ 48 ].…”

“…The integration of automatized programs as well as the use of AI and ML algorithms in clinical routine diagnostic might help to overcome these limitations. Since 3D ultrasound programs are preinstalled on many of the commonly used ultrasound systems, they can be used for a standardized and reproducible evaluation, especially in terms of image recognition and plane reconstruction [ 13 , 14 ]. Additionally, several manufacturers provide onboard software tools (e.g., SonoCNS™, Smart Planes CNS™, 5DCNS+™), which allow a standardized semiautomatic plane reconstruction and biometric measurement of the fetal CNS.…”

How automated techniques ease functional assessment of the fetal heart: applicability of two-dimensional speckle-tracking echocardiography for comprehensive analysis of global and segmental cardiac deformation using fetalHQ®

“…Ultrasound examination is highly examiner-dependent -especially in complex cardiac anatomy -and acquisition and quantification of fetal cardiac functional parameters with accurate prenatal diagnosis by fetal echocardiography depends on the skill and experience of the operator [25,46,47]. The applicability of speckletracking is limited when imaging conditions are suboptimal [40].…”

Feasibility and Acceptance of Self-Guided Mobile Ultrasound among Pregnant Women in Routine Prenatal Care

“…Technology continues to advance worldwide and is becoming more important in the healthcare sector. Mobile and remote medical devices are currently being introduced in prenatal medicine [ 3 , 4 ]. The COVID-19 pandemic might have accelerated this development as alternative designs for prenatal care in accordance with social distancing were needed [ 5 , 6 ].…”