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.

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.