M. Rousian
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3 records found
1
Towards effective digital lifestyle interventions for pregnant women with obesity
A qualitative study exploring women's and healthcare providers’ perspectives
Background: Maternal obesity increases risks of adverse pregnancy outcomes and long-term diseases for mothers and child. Digital lifestyle interventions show promise, but their effectiveness depends on meeting the specific needs of pregnant women with obesity and healthcare providers (HCPs). Objectives: To explore perspectives and practices on healthy lifestyle and care for pregnant women with obesity, and to identify needs and preferences for digital lifestyle intervention development and implementation. Methods: A qualitative study using focus groups and interviews was conducted with 13 HCPs and 13 pregnant women with obesity. Sessions were audio-recorded, transcribed and analysed thematically. Women viewed a healthy lifestyle as multidimensional, encompassing nutrition, physical activity, mental well-being, and rest, but faced barriers such as pregnancy discomfort, limited knowledge, and stigma. Both women and HCPs emphasized child health as a motivator and valued goal setting and practical advice. Existing care was seen as inconsistent and generic, with HCPs constrained by time and unclear roles. Participants preferred a personalized, user-friendly mobile app with modular, evidence-based content tailored to individual goals, pregnancy stage, and medical status. Features such as self-monitoring, goal setting, and a supportive, non-judgmental tone were important. Integration into routine obstetric care was considered key for engagement and effectiveness. If designed accordingly, such tools could provide accessible, tailored support between appointments, reinforce positive behaviour change, improve patient-provider communication, and reduce HCP time pressures. Conclusions: Co-designing digital lifestyle tools with women and HCPs is vital. Personalized, feasible interventions integrated in obstetric care can support behaviour change and improve outcomes for mothers and children. Trial registration number: not applicable.
Artificial intelligence for automated Carnegie staging of the human embryo in three-dimensional ultrasound
The Rotterdam periconception cohort
The Carnegie staging system facilitates the assessment of normal and abnormal development in terms of morphology during the embryonic period. Using virtual reality (VR) it is possible to visually assess the Carnegie stage in-utero, which takes 1-2 minutes per ultrasound image. Adoption in clinical practice is hampered by the need for a VR set-up and required time for visual assessment. To overcome this, our aim is to automate in-utero Carnegie staging using Artificial Intelligence (AI).
Methods
1357 first trimester three-dimensional (3D) ultrasound images of 797 ongoing pregnancies resulting in life birth from The Rotterdam Periconception Cohort were used. We used DenseNet, a state-of-the-art deep learning algorithm for image classification. The algorithm was trained to estimate the Carnegie stage < 16, 16-23, and 23> solely based on the ultrasound images. We used 1100 images of 642 pregnancies for training. For evaluation, we used a test set of 257 images of 155 pregnancies, not used during training.
Results
The AI algorithm achieved an overall accuracy of 61%, which is close to the results of an independent rater, who achieved an accuracy of 63% on 46 images selected for manual VR assessment training. The accuracy was for stage < 16: 55% (n = 9), for stages 16-19: 59% (n = 79), for stages 20-23:62% (n = 151), and for stage >23: 61% (n = 18). The performance differences can partly be explained by the limited size of the embryo early in the first trimester.
Conclusions
Since automatic Carnegie staging using AI is real-time and does not require a VR set-up adoption in clinical practice becomes feasible. In future work, we aim to enhance interpretability by analysing the specific morphological aspects in ultrasound scans utilised by the algorithm to assign the Carnegie stage. Understanding the morphological aspects linked to the Carnegie stage by the algorithm might lead to more in-depth insight into the patterns of normal and abnormal morphological development across pregnancies. ...
The Carnegie staging system facilitates the assessment of normal and abnormal development in terms of morphology during the embryonic period. Using virtual reality (VR) it is possible to visually assess the Carnegie stage in-utero, which takes 1-2 minutes per ultrasound image. Adoption in clinical practice is hampered by the need for a VR set-up and required time for visual assessment. To overcome this, our aim is to automate in-utero Carnegie staging using Artificial Intelligence (AI).
Methods
1357 first trimester three-dimensional (3D) ultrasound images of 797 ongoing pregnancies resulting in life birth from The Rotterdam Periconception Cohort were used. We used DenseNet, a state-of-the-art deep learning algorithm for image classification. The algorithm was trained to estimate the Carnegie stage < 16, 16-23, and 23> solely based on the ultrasound images. We used 1100 images of 642 pregnancies for training. For evaluation, we used a test set of 257 images of 155 pregnancies, not used during training.
Results
The AI algorithm achieved an overall accuracy of 61%, which is close to the results of an independent rater, who achieved an accuracy of 63% on 46 images selected for manual VR assessment training. The accuracy was for stage < 16: 55% (n = 9), for stages 16-19: 59% (n = 79), for stages 20-23:62% (n = 151), and for stage >23: 61% (n = 18). The performance differences can partly be explained by the limited size of the embryo early in the first trimester.
Conclusions
Since automatic Carnegie staging using AI is real-time and does not require a VR set-up adoption in clinical practice becomes feasible. In future work, we aim to enhance interpretability by analysing the specific morphological aspects in ultrasound scans utilised by the algorithm to assign the Carnegie stage. Understanding the morphological aspects linked to the Carnegie stage by the algorithm might lead to more in-depth insight into the patterns of normal and abnormal morphological development across pregnancies.
Trustworthy Embodied Conversational Agents for Healthcare
A Design Exploration of Embodied Conversational Agents for the periconception period at Erasmus MC
This paper explores the potential implications of embodied conversational agents (ECAs) in healthcare, focusing on the impact of appearance and conversation style on trustworthiness. We conducted a Research through Design investigation of ECAs for supporting women during the periconception period and in pregnancy. The paper presents the results of a Wizard of Oz study in which two alternative prototypes, a chatbot, and an ECA, were tested in a tertiary hospital by 25 participants. Reflecting on the results we suggest that limited patients' trust in ECAs maybe be beneficial for achieving trustworthy use of these agents in the healthcare context.