On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review

Abstract

A significant challenge for hospitals and medical practitioners in low- and middle-income nations is the lack of sufficient health care facilities for timely medical diagnosis of chronic and deadly diseases. Particularly, maternal and neonatal morbidity due to various non-communicable and nutrition related diseases is a serious public health issue that leads to several deaths every year. These diseases affecting either mother or child can be hospital-acquired, contracted during pregnancy or delivery, postpartum and even during child growth and development. Many of these conditions are challenging to detect at their early stages, which puts the patient at risk of developing severe conditions over time. Therefore, there is a need for early screening, detection and diagnosis, which could reduce maternal and neonatal mortality. With the advent of Artificial Intelligence (AI), digital technologies have emerged as practical assistive tools in different healthcare sectors but are still in their nascent stages when applied to maternal and neonatal health. This review article presents an in-depth examination of digital solutions proposed for maternal and neonatal healthcare in low resource settings and discusses the open problems as well as future research directions.

Artificial Intelligence for Global Health: Difficulties and Challenges: A Narrative Review

“…Maternal death is quite common in third-world countries, and it was reported that over 99 percent of maternal deaths occur in developing countries. During the last decade, it was shown that AI coupled with electronic health records can be used to help prevent maternal morbidity and mortality [33]. The algorithm considers factors such as a woman's age, previous pregnancies, and medical records to identify women at high risk.…”

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“…In welldeveloped countries, AI has been implemented that significantly reduces the mortality rates of this age group of children. 10 A study by Gao and colleagues developed ML algorithms using urine and blood samples among 81 pregnant females. This model was found to be capable of predicting preterm births among the cohorts of low and middle-income countries including Karachi,Pakistan.…”

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“…Also, pregnant women and children have unique health challenges that could be further aggravated or complicated by comorbidities [25]. To further compound the difficulties of managing comorbidities in children and pregnant women, many health facilities especially in developing countries are fraught with low manpower and lack of basic health facilities, both of which can prolong diagnostic time for chronic and lifethreatening medical conditions in pregnant women and children [26]. Some of these conditions may also be difficult to detect and diagnose early due to their timing, insidious nature, and societal irrelevance, often becoming more difficult and/or expensive to manage over time.…”

“…For example, postpartum depression, a condition characterized by major depressive symptoms and loss of interest in a woman postdelivery can often go unnoticed until it becomes severe [27]. To this end, Khan et al [26] suggest that given the high maternal mortality, and the unavailability or incompatibility of foreign-developed infant technologies in low-resource settings, there is an urgent need to develop health technologies that are effective, efficient, inexpensive, and easy to use and understand. They further highlight the role of AI in predicting miscarriage, gestational diabetes (GDM), anemia, hypertensive disorders in pregnancy, and postpartum depression.…”

“…Most studies have focused on predicting a limited range of chronic co-morbidities such as diabetes, hypertension, and depression in the general population [13]. Maternal and neonatal health concerns are often unaddressed and undocumented owing to a lack of skilled manpower, the inability of caregivers to identify red-flag symptoms in the early stage, and erroneous documentation [26]. There has been some gain however in recent times, such as the use of XAI to predict preterm deliveries [28], and the use of XAI in predicting autism spectrum disorder in children [29].…”