Intelligent systems in obstetrics and midwifery: Applications of machine learning

Abstract

INTRODUCTION Machine learning is increasingly utilized over recent years in order to develop models that represent and solve problems in a variety of domains, including those of obstetrics and midwifery. The aim of this systematic review was to analyze research studies on machine learning and intelligent systems applications in midwifery and obstetrics. METHODS A thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only articles that discussed machine learning and intelligent systems applications in midwifery and obstetrics, were considered in this review. Selected articles were critically evaluated as for their relevance and a contextual synthesis was conducted. RESULTS Thirty-two articles were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that machine learning and intelligent systems have produced successful models and systems in a broad list of midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc. CONCLUSIONS This systematic review suggests that machine learning represents a very promising area of artificial intelligence for the development of practical and highly effective applications that can support human experts, as well the investigation of a wide range of exciting opportunities for further research.

The Impact of Artificial Intelligence (AI) on Midwifery Education: A Scoping Review

“…Further studies indicate that AI-related technologies enable midwifery-specific advancements such as improved diagnosis, pregnancy risk assessment and foetal monitoring. The primary goal should be to assist human experts in their work [ 34 ]. This is supported by the literature analysed here, as midwives expressed a desire for AI to support them in their daily work [ 13 ].…”

“…This is supported by the literature analysed here, as midwives expressed a desire for AI to support them in their daily work [ 13 ]. Overall, further research is needed in several areas for AI to be successfully implemented in midwifery [ 33 , 34 ]. The analysed literature supports the integration of midwives into AI-related research [ 10 ].…”

“…However, the transition from theory to practice poses several potential risks that could undermine the trust of healthcare professionals and patients in AI-related systems [ 34 ]. The examined studies reveal a lack of trust in these systems and scepticism among midwives [ 13 ], as well as the discussion of the ethical and legal implications of AI for midwifery [ 10 , 34 ]. The use of large and high-quality training datasets is crucial for accurate results in practice [ 34 ].…”

“…The examined studies reveal a lack of trust in these systems and scepticism among midwives [ 13 ], as well as the discussion of the ethical and legal implications of AI for midwifery [ 10 , 34 ]. The use of large and high-quality training datasets is crucial for accurate results in practice [ 34 ]. This is also highlighted in the analysed articles, which recommend that midwives themselves should collect high-quality patient and health data to enhance care through AI technologies [ 10 ].…”

Identification of DTL as Related Biomarker and Immune Infiltration Characteristics of Nasopharyngeal Carcinoma via Comprehensive Strategies

“…The input file can be text, images, or anything that is digitally stored. 13 AI/ML techniques have been applied to various fields of biomedicine including novel target identification, understanding of target-disease associations, drug candidate selection, protein structure predictions, molecular compound design and optimization, understanding of disease mechanisms, development of new prognostic and predictive biomarkers, biometrics data analysis from wearable devices, imaging, precision medicine, and more recently clinical trial design, conduct, and analysis. 14,15 To this end, we used microarray datasets of gene expression to assess the differentially expressed genes (DEGs) between NPC and normal nasopharyngeal tissue, then ML algorithm was used to screen biomarkers in DEGs for early identification of NPC.…”