Semiautomatic Assessment of Fetal Fractional Limb Volume for Weight Prediction in Clinical Praxis: How Does It Perform in Routine Use?

Prediction of large-for-gestational age at 36 weeks' gestation: two-dimensional ultrasound vs three-dimensional ultrasound vs magnetic resonance imaging

OBJECTIVE

To compare the performance of two-dimensional ultrasound (2D-US), three-dimensional ultrasound (3D-US) and magnetic resonance imaging (MRI) at 36 weeks' gestation in predicting the delivery of a large-for-gestational-age (LGA) neonate, defined as birth weight ≥ 95th percentile, in patients at high and low risk for macrosomia.

METHODS

This was a secondary analysis of a prospective observational study conducted between January 2017 and February 2019. Women with a singleton pregnancy at 36 weeks' gestation underwent 2D-US, 3D-US and MRI within 15 min for estimation of fetal weight. Weight estimations and birth weight were plotted on a growth curve to obtain percentiles for comparison. Participants were considered high risk if they had at least one of the following risk factors: diabetes mellitus, estimated fetal weight ≥ 90th percentile at the routine third-trimester ultrasound examination, obesity (prepregnancy body mass index ≥ 30 kg/m2) or excessive weight gain during pregnancy. The outcome was the diagnostic performance of each modality in the prediction of birth weight ≥ 95th percentile, expressed as the area under the receiver-operating-characteristics curve (AUC), sensitivity, specificity and positive and negative predictive values.

RESULTS

A total of 965 women were included, of whom 533 (55.23%) were high risk and 432 (44.77%) were low risk. In the low-risk group, the AUCs for birth weight ≥ 95th percentile were 0.982 for MRI, 0.964 for 2D-US and 0.962 for 3D-US; pairwise comparisons were non-significant. In the high-risk group, the AUCs were 0.959 for MRI, 0.909 for 2D-US and 0.894 for 3D-US. A statistically significant difference was noted between MRI and both 2D-US (P = 0.002) and 3D-US (P = 0.002), but not between 2D-US and 3D-US (P = 0.503). In the high-risk group, MRI had the highest sensitivity (65.79%) compared with 2D-US (36.84%, P = 0.002) and 3D-US (21.05%, P < 0.001), whereas 3D-US had the highest specificity (98.99%) compared with 2D-US (96.77%, P = 0.005) and MRI (96.97%, P = 0.004).

CONCLUSIONS

At 36 weeks' gestation, MRI has better performance compared with 2D-US and 3D-US in predicting birth weight ≥ 95th percentile in patients at high risk for macrosomia, whereas the performance of 2D-US and 3D-US is comparable. For low-risk patients, the three modalities perform similarly. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

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

(1) Objective: We aimed to evaluate the accuracy and efficacy of AI-assisted biometric measurements of the fetal central nervous system (CNS) by comparing two semiautomatic postprocessing tools. We further aimed to discuss the additional value of semiautomatically generated sagittal and coronal planes of the CNS. (2) Methods: Three-dimensional (3D) volumes were analyzed with two semiautomatic software tools, 5DCNS+™ and SonoCNS™. The application of 5DCNS+™ results in nine planes (axial, coronal and sagittal) displayed in a single template; SonoCNS™ depicts three axial cutting sections. The tools were compared regarding automatic biometric measurement accuracy. (3) Results: A total of 129 fetuses were included for final analysis. Our data indicate that, in terms of the biometric quantification of head circumference (HC), biparietal diameter (BPD), transcerebellar diameter (TCD) and cisterna magna (CM), the accuracy of SonoCNS™ was higher with respect to the manual measurement of an experienced examiner compared to 5DCNS+™, whereas it was the other way around regarding the diameter of the posterior horn of the lateral ventricle (Vp). The inclusion of four orthogonal coronal views in 5DCNS+™ gives valuable information regarding spatial arrangements, particularly of midline structures. (4) Conclusions: Both tools were able to ease assessment of the intracranial anatomy, highlighting the additional value of automated algorithms in clinical use. SonoCNS™ showed a superior accuracy of plane reconstruction and biometry, but volume reconstruction using 5DCNS+™ provided more detailed information, which is needed for an entire neurosonogram as suggested by international guidelines.

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

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.