APPLICATION OF MACHINE LEARNING IN THE DIAGNOSIS OF ECTOPIC PREGNANCY: A LITERATURE REVIEW
DOI:
https://doi.org/10.47820/recima21.v6i7.6533Keywords:
Ectopic pregnancy, Machine learning, Artificial intelligence, Clinical diagnosis, UltrasonographyAbstract
This paper presents literature review on the use of machine learning (ML) in the diagnosis of ectopic pregnancy (EP). Studies published between 2020 and 2024 were analyzed using rigorous inclusion and exclusion criteria. Results show that advanced techniques, such as convolutional neural networks, Bayesian algorithms, and support vector machines, achieve high diagnostic accuracy, especially when integrating multiple data sources, including clinical, laboratory, and ultrasound information. These models demonstrated potential to reduce clinical errors and improve diagnostic efficiency, overcoming limitations of traditional methods. However, challenges such as the lack of robust clinical validation, scarcity of representative data, and the complexity of models, often seen as "black boxes," still hinder their practical implementation. This review contributes to a clearer understanding of the state of the art, highlighting the most effective approaches, main challenges, and opportunities for future research. The responsible adoption of these technologies has the potential to transform EP diagnosis, improving patient safety and clinical outcomes.
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BOWMAN, C. E. There is nothing medically magical about machine learning. Journal of the Royal Society of Medicine, v. 115, n. 9, p. 332, 2022. DOI: https://doi.org/10.1177/01410768221123239
COUCKUYT, A.; SEURINCK, R.; EMMANEEL, A. et al. Challenges in translational machine learning. Human Genetics, v. 141, p. 1451-1466, 2022. DOI: https://doi.org/10.1007/s00439-022-02439-8
DALZOCHIO, J. MILPDM: an architecture for predictive maintenance of assets in the military domain. 2024. Tese (Doutorado em Ciência da Computação) -- Universidade do Vale do Rio dos Sinos (UNISINOS), 2024.
ELLIS, R. J.; SANDER, R. M.; LIMON, A. Twelve key challenges in medical machine learning and solutions. Intelligence-Based Medicine, v. 6, p. 100068, 2022. DOI: https://doi.org/10.1016/j.ibmed.2022.100068
FISTOURIS, J.; BERGH, C.; STRANDELL, A. Pregnancy of unknown location: external validation of the hcg-based m6np and m4 prediction models in an emergency gynaecology unit. BMJ Open, v. 12, n. 11, 2022. DOI: https://doi.org/10.1136/bmjopen-2021-058454
GARZON, N. A.; BARBOSA, L. S. O. Aprendizado de Máquina na Medicina: Como Algoritmos de Aprendizado de Máquina Podem Ser Aplicados em Diagnósticos Médicos, Prognósticos e Descoberta de Novos Tratamentos. RECIMA21 - Revista Científica Multidisciplinar, v. 4, n. 12, 2023. DOI: https://doi.org/10.47820/recima21.v4i12.4708
HEINRICHS, B.; EICKHOFF, S. B. Your evidence? Machine learning algorithms for medical diagnosis and prediction. Human Brain Mapping, v. 41, n. 6, p. 1435-1444, 2020. DOI: https://doi.org/10.1002/hbm.24886
KITCHENHAM, B. Procedures for Performing Systematic Reviews. Keele University, UK: Department of Computer Science, 2004. Technical Report TR/SE-0401.
MA, D.; YANG, R.; CHEN, Y. et al. Identification of noninvasive diagnostic biomarkers for ectopic pregnancy using data-independent acquisition (DIA) proteomics: a pilot study. Scientific Reports, v. 12, p. 19992, 2022. DOI: https://doi.org/10.1038/s41598-022-23374-8
MENNICKENT, D.; RODRÍGUEZ, A.; OPAZO, M.; RIEDEL, C.; CASTRO, E.; ERIZ-SALINAS, A.; APPEL-RUBIO, J.; AGUAYO, C.; DAMIANO, A.; GUZMÁN-GUTIÉRREZ, E.; ARAYA, J. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Frontiers in Endocrinology, v. 14, p. 1130139, 2023. DOI: https://doi.org/10.3389/fendo.2023.1130139
MULLANY, K.; MINNECI, M.; MONJAZEB, R.; COIADO, O. C. Overview of ectopic pregnancy diagnosis, management, and innovation. Women's Health, v. 19, p. 17455057231160349, 2023. DOI: https://doi.org/10.1177/17455057231160349
POON, A. I. F.; SUNG, J. J. Y. Opening the black box of AI-Medicine. Journal of Gastroenterology and Hepatology, v. 36, n. 3, p. 581-584, 2021. DOI: https://doi.org/10.1111/jgh.15384
RUEANGKET, P.; RITTILUECHAI, K.; PRAYOTE, A. Predictive analytical model for ectopic pregnancy diagnosis: statistics vs. machine learning. Frontiers in Medicine, v. 9, 2022. DOI: https://doi.org/10.3389/fmed.2022.976829
SAVARIS, R.; MAISSIAT, J.; MOL, B.; BARNHART, K.; LINK, C. Diagnosing ectopic pregnancy using the Bayes theorem, a classic that is good as new: a retrospective cohort study. Human Reproduction, v. 37, Supplement 1, p. deac107.410, 2022. DOI: https://doi.org/10.1093/humrep/deac107.410
SHABAN, M.; MOLLAZADEH, S.; ESLAMI, S. et al. Prediction of chromosomal abnormalities in the screening of the first trimester of pregnancy using machine learning methods: a study protocol. Reproductive Health, v. 21, p. 101, 2024. DOI: https://doi.org/10.1186/s12978-024-01839-5
SHAZLY, S. A.; TRABUCO, E. C.; NGUFOR, C. G.; FAMUYIDE, A. O. Introduction to Machine Learning in Obstetrics and Gynecology. Obstetrics & Gynecology, v. 139, n. 4, p. 669-679, 2022. DOI: https://doi.org/10.1097/AOG.0000000000004706
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