Métodos de inteligência artificial na predição e diagnóstico precoces de complicações na gravidez
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Resumo
Objetivo: Analisar e sintetizar as evidências científicas disponíveis na literatura sobre os métodos de inteligência artificial (IA) na predição e diagnóstico precoces de complicações na gravidez. Métodos: Trata-se de uma revisão integrativa, realizada nas bases de dados EMBASE, PubMed, Scopus e Web of Science, por meio dos seguintes descritores: “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Pregnancy Complications”, “perinatal complications” e “Postpartum”. Foram incluídos estudos publicados entre 2019 a 2023 que utilizam métodos de IA para prever complicações na gravidez. Resultados: A amostra final consistiu em 13 artigos, com uma concentração mais elevada de publicações nos anos de 2019 e 2020. A maioria dos estudos foi conduzida na China, e os designs de pesquisa predominantes foram estudos de coorte. Os modelos de IA utilizados mostraram eficácia na predição de complicações como pré-eclâmpsia, diabetes mellitus gestacional, nascimento prematuro e invasão placentária, utilizando dados de prontuários eletrônicos e outros biomarcadores. Considerações finais: Esta revisão evidencia o impacto significativo dos modelos de IA na melhoria da predição e diagnóstico de complicações na gravidez. Os resultados sublinham a necessidade de integração contínua dessas tecnologias na prática clínica obstétrica, bem como de pesquisas futuras para validar e expandir sua aplicabilidade em diferentes contextos clínicos.
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Referências
2. ARTZI NS, et al. Prediction of Gestational Diabetes Based on Nationwide Electronic Health Records. Nat. Med. 2020; 26(1): 71-76.
3. BRASIL. Ministério da Saúde (MS). Lei no 9.610, de 19 de fevereiro de 1998, que altera, atualiza e consolida a legislação sobre direitos autorais e dá outras providências. Diário Oficial da União. Brasília: Ministério da Saúde, 1998.
4. CARVALHO DV, et al. Machine Learning Interpretability: A Survey on Methods and Metrics. Electronics. 2019; 8: 832.
5. CECULA P. Artificial Intelligence: The Current State of Affairs for AI in Pregnancy and Labour. J. Gynecol. Obstet. Hum. Reprod. 2021; 50: 102048.
6. CERSONSKY TEK, et al. Identifying risk of stillbirth using machine learning. Am J Obstet Gynecol. 2023; 229(3): 327.e1-327.e16.
7. DHOMBRES F, et al. Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review. J Med Internet Res. 2022; 24(4): e35465.
8. EDWARDS P, et al. Obesity in Pregnancy. Obstet. Gynaecol. Reprod. Med. 2020; 30: 315-320.
9. GAO C, et al. Learning to Identify Severe Maternal Morbidity from Electronic Health Records. Stud. Health Technol. Inform. 2019; 264: 143-147.
10. GUO Z, et al. Whole‐Genome Promoter Profiling of Plasma DNA Exhibits Diagnostic Value for Placenta‐Origin Pregnancy Complications. Adv. Sci. 2020; 7(7): 1901819.
11. HO CWL, et al. Governance of Automated Image Analysis and Artificial Intelligence Analytics in Healthcare. Clin. Radiol. 2019; 74: 329-337.
12. IFTIKHAR PM, et al. Artificial Intelligence: A New Paradigm in Obstetrics and Gynecology Research and Clinical Practice. Cureus, 2020; 12: e7124.
13. JHEE JH, et al. Prediction Model Development of Late-Onset Preeclampsia Using Machine Learning-Based Methods. PLoS ONE. 2019; 14(8): e0221202.
14. KHATIBI T, et al. Analysis of Big Data for Prediction of Provider-Initiated Preterm Birth and Spontaneous Premature Deliveries and Ranking the Predictive Features. Arch. Gynecol. Obstet. 2019; 300(6): 1565-1582.
15. LI YX, et al. Novelelectronic health records applied for prediction of pre-eclampsia: Machine-learning algorithms. Pregnancy Hypertens. 2021; 26:102-109.
16. LIU M, et al. Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in China. Front Physiol. 2022; 13: 896969.
17. MALACOVA E, et al. Stillbirth Risk Prediction Using Machine Learning for a Large Cohort of Births from Western Australia, 1980-2015. Sci. Rep. 2020; 10(1): 5354.
18. MARIONA FG. Perspectives in Obesity and Pregnancy. Womens Health (Lond). 2016; 12: 523-532.
19. MCHUGH ML. Interrater reliability: the kappa statistic. Biochemia Medica, 2012; 22(3): 276-282.
20. MENDES KDS, et al. Use of the bibliographic reference manager in the selection of primary studies in integrative reviews. Texto Contexto Enferm. 2019; 28: e20170204.
21. PAPADA SM. Machine learning in medicine: It has arrived, let's embrace it. J Card Surg. 2021; 36(11): 4121-4124.
22. NGUYEN P, et al. $\mathtt {Deepr}$: A Convolutional Net for Medical Records. IEEE J. Biomed. Health Inform. 2017; 21, 22–30.
23. PEREIRA AS, et al. Metodologia da pesquisa científica. (1ª ed.): UFSM, NTE, 2018.
24. POON LC, et al. Early prediction of preeclampsia. Obstet Gynecol Int. 2014; 2014: 297397.
25. RAHMAN A, et al. Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin. Clin Pathol. 2024; 17:2632010X241226887.
26. RAJKOMAR A, et al. Scalable and Accurate Deep Learning with Electronic Health Records. Npj Digital Med. 2018; 1, 18.
27. RANJBAR A, et al. Machine learning models for predicting preeclampsia: a systematic review. BMC Pregnancy Childbirth. 2024; 24: 6.
28. RITTENHOUSE KJ, et al. Improving Preterm Newborn Identification in Low-Resource Settings with Machine Learning. PLoS One. 2019; 14(2): e0198919.
29. SHARMA DD, et al. The Management of Preeclampsia: A Comprehensive Review of Current Practices and Future Directions. Cureus. 2024; 16(1): e51512.
30. SIRCAR M, et al. Pathogenesis of preeclampsia. Curr Opin Nephrol Hypertens. 2015; 24(2): 131-8.
31. STILLWELL S, et al. Evidence– based practice: step by step. Am J Nurs, 2010; 110(5): 41-47.
32. SUFRIYANA H, et al. Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis. JMIR Med Inform. 2020; 8(11): e16503.
33. SUN H, et al. Identification of Suspicious Invasive Placentation Based on Clinical MRI Data Using Textural Features and Automated Machine Learning. Eur. Radiol. 2019; 29(11): 6152-6162.
34. SWEETING AN, et al. A Novel Early Pregnancy Risk Prediction Model for Gestational Diabetes Mellitus. Fetal Diagn Ther. 2019; 45(2): 76-84.
35. WATANABE M, et al. Prediction of gestational diabetes mellitus using machine learning from birth cohort data of the Japan Environment and Children's Study. Sci Rep. 2023; 13(1): 17419.
36. WHITTEMORE R, et al. The integrative review: updated methodology. J Adv Nurs. 2005; 52(5):546-53.
37. WORLD HEALTH ORGANIZATION (WHO). WHO recommendations on intrapartum care for a positive childbirth experience. World Health Organization; 2018.
38. WORLD HEALTH ORGANIZATION (WHO). Trends in Maternal Mortality: 2000 to 2017: Estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division. 2019. Geneva: World Health Organization. WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division.
39. ZHANG H, et al. Integration of clinical demographics and routine laboratory analysis parameters for early prediction of gestational diabetes mellitus in the Chinese population. Front Endocrinol (Lausanne). 2023; 14:1216832.