The periconceptional period, encompassing the embryonic phase, is a critical window where a majority of reproductive failures, pregnancy complications, and adverse pregnancy outcomes arise. The Carnegie staging system comprises 23 stages which are based on embryonic morphological development. This allows for the assessment of normal and abnormal embryonic development during this critical period. In-utero Carnegie staging using three-dimensional (3D) ultrasound scans visualized with virtual reality offers valuable insights but is currently a time-consuming manual process. To address this, we propose a deep learning approach for Carnegie staging in 3D ultrasound scans.

We used a dataset comprising 1413 3D ultrasound scans from the Rotterdam Periconceptional Cohort, annotated with Carnegie stages spanning from stages 13 to 23, including fetal subjects. Various training strategies were explored. We compared a metric regression approach, which considers the ordered nature of the Carnegie stages by treating the Carnegie stages as a continuous variable, with a multi-class classification approach, treating stages as independent categories. Additionally, we evaluated the influence of using a loss function accommodating the categorical nature of the Carnegie stages in the metric regression approach and examined the impact of incorporating embryonic size in the model input. Ultimately, a regression approach using the Mean Squared Error (MSE) loss function emerged as the optimal choice.

This model achieved a classification accuracy of 0.59 and a Root Mean Squared Error (RMSE) of 0.62 on the test set. This performance is comparable to an intermediate human rater, which achieved an accuracy of 0.63 and a RMSE of 0.65. Our findings represent a significant step towards the development of an automated Carnegie staging method, offering the potential for a more comprehensive evaluation of the critical embryonic phase in the clinic.

Objective: To develop and evaluate a machine learning approach for predicting blastocyst viability using automated expansion measurements, clinical variables and image features.
Methods: A convolutional neural network was developed to automatically segment and measure blastocyst cross-sectional area from time-lapse images. We generated expansion curves and extracted features for 315 blastocysts. Various machine learning models were trained to predict biochemical and ongoing pregnancy using expansion, clinical and image-derived features. Model performance was evaluated using cross-validation and an unseen test set.
Results: The segmentation model achieved a Jaccard index of 97.6% on the validation set. Support vector machines using clinical and expansion features achieved the highest performance, with AUCs of 0.71 and 0.70 for predicting biochemical and ongoing pregnancy, respectively, on the test set. Blastocysts resulting in pregnancy expanded significantly faster and reached larger final cross-sectional areas compared to those that did not implant. Key predictive features included expansion rate and maternal age.
Conclusions: Automated quantification of blastocyst expansion dynamics combined with clinical variables enables prediction of implantation potential. Incorporating objective expansion metrics into embryo selection may enhance IVF success rates beyond traditional morphological grading systems.

In the Netherlands, the healthcare sector is facing increasing staff shortages and the demand for adequately trained healthcare personnel is expected to increase in the coming years. Shortages in obstetric care mean that not all women and their partners receive the care they need before, during and after pregnancy. To counter these shortages, there are opportunities in technology and continuity in maternity care.

Erasmus MC has set up its own (online) care path for preconception care, Smarter Pregnant. This programme consists of online platforms for preconception screening and the outpatient clinic healthy pregnant (OCHP). OCHP is a counselling session for couples or individuals to make pregnancy as healthy as possible or to obtain the healthiest possible lifestyle to get pregnant. Most of the pain points occur when filling in and processing data in the Slimmer Zwanger's system. The current system of Smarter Pregnant is often not understood by patients and nurses have to process the data manually. This processing of data is perceived by nurses as monotonous - which is why nurses report that the work feels robotic. Moreover, there is less focus on delivering care and more on interacting with the system. As a result, patients have to search for more information on the internet, getting lost in the validated and non-validated e-health for conception. Therefore, the need for an authoritative and trustworthy source of information for the OCHP is great.

The specific focus of this study is on creating a trustworthy VA for OCHP. The dimensions of trustworthiness are leading for the interaction and thus the design of the Voice Assistant. There are three layers that were considered when creating the Voice Assistant for the OCHP; the user, the context and the technology. In the current context, trustworthiness is generated by taking responsibility and exuding expertise. Responsibility is something the nurse has to take on the word of the patient and partner. The trustworthiness of the nurse can be measured by his or her expertise. Expertise is considered a dimension of trustworthiness in existing models.

To decide what the VA had to look and sound like, several experimental prototypes were conducted during the context analysis and collection of existing theories and methods. These prototypes served both as validation of research, and to spawn further research. The results of these studies led to a final prototype.

Each test ended with a qualitative interview with the patient based on the dimensions of trustworthiness. To structure patients' responses to the VA, three research questions were formulated regarding; expectations, comparing human-to-human versus human-to-robot interaction and characteristics affecting trustworthiness. Because the context analysis revealed that in the context of OCHP, trustworthiness depends on expertise, this research question was reformulated specifically regarding expertise.

These themes helped accomplish and structure the overarching goal of the project: Forming principles on how trustworthy VAs can be designed for healthcare in the future. These principles form a set of pointers on how trustworthiness can be implemented in a VA for healthcare.