Artificial Intelligence has been widely applied to a majority of research areas, including health and medicine. Certain complications or disorders that can appear during pregnancy can endanger the life of both mother and fetus. There is enough scientific literature to support the idea that emotional aspects can be a relevant risk factor in pregnancy (such as anxiety, stress or depression, for instance). This paper presents a scoping review of the scientific literature from the past 12 years (2008-2020) to identify which methodologies, techniques, algorithms and frameworks are used in Artificial Intelligence and Affective Computing for pregnancy health and well-being. The methodology proposed by Arksey and O'Malley, in conjunction with PRISMA-ScR framework has been used to create this review. Despite the relevance that emotional status can have as a risk factor during pregnancy, one of the main findings of this study is that there is still not a significant amount of literature on automatic analysis of emotion. Health enhancement and well-being for pregnant women can be achieved with artificial intelligence or affective computing based devices, hence future work on this topic is strongly suggested.

The use of artificial intelligence in healthcare in general and in obstetrics and gynecology in particular has great potential. Specifically, machine learning methods could help improve the health and well-being of pregnant women, closely monitoring their health parameters during pregnancy, or reducing maternal and perinatal morbidity and mortality with early detection of pathologies. In this work, we propose a machine learning model to predict risk events in pregnancy, in particular the prediction of pre-eclampsia and intrauterine growth restriction, using Doppler measures of the uterine artery, sFlt-1, and PlGF values. For this purpose, we used a public dataset from a study carried out by the University Medical Center of Ljubljana, in which data were collected from 95 pregnant women with pre-eclampsia and intrauterine growth restriction. We adopted a multi-label approach to accomplish the prediction task. Different classifiers were evaluated and compared. The performance of each model was tested in terms of accuracy, precision, recall, F1 score, Hamming loss, and AUC-ROC. On the basis of these parameters, a variation of the decision tree classifier was found to be the best performing model. Our model had a robust recall metric (0.89) and an AUC ROC metric (0.87), taking into account the size of the data and the unbalance of the class.

Background: Dentistry interventions cause common anxiety and fear problems in children (6-11 years), and according to scientific evidence, this causes a decrease in their quality of life. Therapies mediated by IT-based tools have been shown to positively influence children’s mood based on distraction as well as relaxing activities, but there is no evidence of their use to reduce dental anxiety in children. Objective: The aim of this study was to answer the following research question: Does our new children-centered codesign methodology contribute to achieving a usable mobile-based product with a highly scored user experience? Methods: A mobile health app was developed to reduce dental anxiety in children using rapid application development following the usage-centered design methodology. Structured interviews were conducted to test the usability and user experience of the app prototype among 40 children (n=20, 50%, boys and n=20, 50%, girls; age 6-11 years) using a children-adapted questionnaire and the 7-point Single Ease Question rating scale. The Smiley Faces Program—Revised questionnaire was used to assess the level of dental anxiety in participants. Results: There were no significant differences between girls and boys. The task completion rate was 95% (n=19) for children aged 6-8 years (group 1) and 100% (n=20) for children aged 9-11 years (group 2). Group 1 found watching the relaxing video (task C) to be the easiest, followed by playing a video minigame (task B) and watching the narrative (task A). Group 2 found task C to be the easiest, followed by task A and then task B. The average time spent on the different types of tasks was similar in both age groups. Most of the children in both age groups were happy with the app and found it funny. All children thought that having the app in the waiting room during a dental visit would be useful. Conclusions: The findings confirmed that the app is usable and provides an excellent user experience. Our children-adapted methodology contributes to achieving usable mobile-based products for children with a highly scored user experience.

Artificial intelligence has the potential to support and improve the quality of life of people with disabilities. Mobility is a potentially dangerous activity for people with impaired ability. This article presents an assistive technology solution to assist visually impaired pedestrians in safely crossing the street. We use a signal trilateration technique and deep learning (DL) for image processing to segment visually impaired pedestrians from the rest of pedestrians. The system receives information about the presence of a potential user through WiFi signals from a mobile application installed on the user’s phone. The software runs on an intelligent semaphore originally designed and installed to improve urban mobility in a smart city context. This solution can communicate with users, interpret the traffic situation, and make the necessary adjustments (with the semaphore’s capabilities) to ensure a safe street crossing. The proposed system has been implemented in Maringá, Brazil, for a one-year period. Trial tests carried out with visually impaired pedestrians confirm its feasibility and practicality in a real-life environment.

Background: The COVID-19 pandemic caused many changes in primary health care systems in Europe. The fast adoption of telemedicine, the shift of health care resources to COVID-19–related tasks, and the tendency of patients to cancel their nonurgent appointments are some examples of these changes. Patient satisfaction is an important outcome of health care services, and the changes caused by COVID-19 in the system may have affected it. Google Maps reviews provide an important channel for patients to communicate about their experiences regarding the primary health care system. Objective: Drawing from research on social media data analytics and text mining, this study set out to investigate the changes in public sentiment regarding primary health care in Finland and Andalusia (Spain) before, during, and after the COVID-19 pandemic. Methods: We collected 55,043 Google Maps reviews from primary health care locations in Finland and Andalusia from January 1, 2013, to May 15, 2024. There are 604 primary health care locations in Finland and 1016 in Andalusia. The total number of Google Maps reviews collected was 12,247 for Finland and 42,796 for Andalusia. First, lexicon-based sentiment analysis using the open-source software AFINN was conducted for the Finnish- and Spanish-language datasets. Thereafter, transformer-based deep learning models for sentiment analysis were applied for both languages. The numeric user ratings and the results of the sentiment analysis were then analyzed. In addition, we conducted a word frequency analysis of the reviews. Results: There were important changes in the ratings and sentiment in the data for Andalusia. The ratings shifted from median 4 (IQR 3) before the COVID-19 pandemic to median 1 (IQR 2) during and median 1 (IQR 3) after the COVID-19 pandemic, on a scale from 1 to 5. The median of the sentiment values of the review texts shifted from neutral before the COVID-19 pandemic to –2 (IQR 0.055) or –1 (IQR 1) during and after the COVID-19 pandemic, depending on which sentiment analysis method was used. Interestingly, changes in numeric ratings and sentiment of the review texts in Finland were only minor, and the median values were the same during all 3 periods. Lexical analysis revealed changes in word frequencies across the periods, reflecting shifts in primary health care experiences during the pandemic, especially among the Spanish-language reviews. Conclusions: The change toward a more negative public discussion on primary health care in Andalusia during the COVID-19 pandemic was considerable. This can be observed both in the numeric user ratings and in the sentiment analysis of the review texts. However, the data for Finland show that the public discourse stayed mostly neutral or slightly positive. The findings have implications on the quality management procedures in primary health care and on the use of user-generated content as an additional information source.