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Exploring the potential of machine learning in gynecological care: a review

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Abstract

Gynecological health remains a critical aspect of women’s overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecological health: preterm birth, breast cancer and cervical cancer and infertility treatment. Machine learning (ML) has emerged as a transformative technology with the potential to revolutionize gynecology and women’s healthcare. The subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. This paper investigates how machine learning (ML) algorithms are employed in the field of gynecology to tackle crucial issues pertaining to women’s health. This paper also investigates the integration of ultrasound technology with artificial intelligence (AI) during the initial, intermediate, and final stages of pregnancy. Additionally, it delves into the diverse applications of AI throughout each trimester.

This review paper provides an overview of machine learning (ML) models, introduces natural language processing (NLP) concepts, including ChatGPT, and discusses the clinical applications of artificial intelligence (AI) in gynecology. Additionally, the paper outlines the challenges in utilizing machine learning within the field of gynecology.

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References

  1. Iftikhar P et al (2020) Artificial intelligence: a new paradigm in obstetrics and gynecology research and clinical practice. Cureus 12(2):e7124

    PubMed  PubMed Central  Google Scholar 

  2. Sone K et al (2021) Application of artificial intelligence in gynecologic malignancies: a review. J Obstetr Gynaecol Res 47(8):2577–2585

    Article  Google Scholar 

  3. Brattain LJ et al (2018) Machine learning for medical ultrasound: status, methods, and future opportunities. Abdom Radiol 43:786–799

    Article  Google Scholar 

  4. Sim JA et al (2020) The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors: applications of machine learning. Sci Rep 10(1):10693

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. MacEachern SJ, Forkert ND (2021) Machine learning for precision medicine. Genome 64(4):416–425

    Article  PubMed  Google Scholar 

  6. Beckmann CRB et al (2013) Obstetrics and gynecology. Lippincott Williams & Wilkins, Philadelphia, PA

    Google Scholar 

  7. Ashton Acton Q (2012) Issues in gynecology, obstetrics, fertility, and pregnancy research: 2011 edition. ScholarlyEditions, Atlanta, GA

    Google Scholar 

  8. Leyland N et al (2010) Endometriosis: diagnosis and management. J Endometriosis 2(3):107–134

    Article  Google Scholar 

  9. Norman RJ et al (2007) Polycystic ovary syndrome. Lancet 370(9588):685–697

    Article  CAS  PubMed  Google Scholar 

  10. Avril N, Gourtsoyianni S, Reznek R (2011) Gynecological cancers. Methods Mol Biol 727:171–189

    Article  PubMed  Google Scholar 

  11. Grünebaum A et al (2023) The exciting potential for ChatGPT in obstetrics and gynecology. Am J Obstetr Gynecol 228(6):696–705

    Article  Google Scholar 

  12. Bertini A et al (2022) Using machine learning to predict complications in pregnancy: a systematic review. Front Bioeng Biotechnol 9:780389

    Article  PubMed  PubMed Central  Google Scholar 

  13. Alam MT et al (2022) Comparative analysis of different efficient machine learning methods for fetal health classification. Appl Bionics Biomech 2022:1–12

    Article  Google Scholar 

  14. Włodarczyk T et al (2021) Machine learning methods for preterm birth prediction: a review. Electronics 10(5):586

    Article  Google Scholar 

  15. Mennickent D et al (2022) Machine learning-based models for gestational diabetes mellitus prediction before 24–28 weeks of pregnancy: a review. Artif Intell Med 132:102378

    Article  PubMed  Google Scholar 

  16. Rabiei R et al (2022) Prediction of breast cancer using machine learning approaches. J Biomed Phys Eng 12(3):297

    Article  PubMed  PubMed Central  Google Scholar 

  17. Mehmood M et al (2021) Machine learning assisted cervical cancer detection. Front Publ Health 9:788376

    Article  Google Scholar 

  18. Bharati S, Podder P, Mondal MRH (2020) Diagnosis of polycystic ovary syndrome using machine learning algorithms. In: 2020 IEEE region 10 symposium (TENSYMP). IEEE, Piscataway, NJ

  19. Bhandari M, Zeffiro T, Reddiboina M (2020) Artificial intelligence and robotic surgery: current perspective and future directions. Curr Opin Urol 30(1):48–54

    Article  PubMed  Google Scholar 

  20. Abinader R, Warsof SL (2019) Benefits and pitfalls of ultrasound in obstetrics and gynecology. Obstetr Gynecol Clin 46(2):367–378

    Article  Google Scholar 

  21. Ondeck CL et al (2018) Ultrasonographic prenatal imaging of fetal ocular and orbital abnormalities. Surv Ophthalmol 63(6):745–753

    Article  PubMed  Google Scholar 

  22. Smeets NAC (2012) Fetal volume measurements in the first trimester of pregnancy with three-dimensional ultrasound. BMC Pregnancy Childbirth 12:38

    Article  PubMed  PubMed Central  Google Scholar 

  23. Moratalla J et al (2010) Semi-automated system for measurement of nuchal translucency thickness. Ultrasound Obstet Gynecol 36(4):412–416

    Article  CAS  PubMed  Google Scholar 

  24. Woolery LK, Grzymala-Busse J (1994) Machine learning for an expert system to predict preterm birth risk. J Am Med Inform Assoc 1(6):439–446

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Grzymala-Busse JW, Woolery LK (1994) Improving prediction of preterm birth using a new classification scheme and rule induction. In: Proceedings of the annual symposium on computer application in medical care. American Medical Informatics Association, Bethesda, MD

  26. Grzymala-Busse JW (1992) LERS—a system for learning from examples based on rough sets. In: Intelligent decision support: handbook of applications and advances of the rough sets theory, pp 3–18

  27. Goodwin L, Maher S (2000) Data mining for preterm birth prediction. In: Proceedings of the 2000 ACM symposium on applied computing, vol 1. pp 46–51

  28. Vega FA et al (2009) Classification and regression trees (CARTs) for modelling the sorption and retention of heavy metals by soil. J Hazard Mater 167(1–3):615–624

    Article  CAS  PubMed  Google Scholar 

  29. Frize M, Nicole Yu, Weyand S (2011) Effectiveness of a hybrid pattern classifier for medical applications. Int J Hybrid Intell Syst 8(2):71–79

    Google Scholar 

  30. Lee KS, Ahn KH (2019) Artificial neural network analysis of spontaneous preterm labor and birth and its major determinants. J Korean Med Sci 34(16):e128

    Article  PubMed  PubMed Central  Google Scholar 

  31. Rawashdeh H et al (2020) Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage. Comput Biol Chem 85:107233

    Article  CAS  PubMed  Google Scholar 

  32. Prema NS, Pushpalatha MP (2019) Machine learning approach for preterm birth prediction based on maternal chronic conditions. In: Emerging Research in Electronics, Computer Science and Technology: Proceedings of International Conference, ICERECT 2018. Springer, Singapore

  33. Koivu A, Sairanen M (2020) Predicting risk of stillbirth and preterm pregnancies with machine learning. Health Inform Sci Syst 8:1–12

    Google Scholar 

  34. Mercer BM et al (1996) The preterm prediction study: a clinical risk assessment system. Am J Obstet Gynecol 174(6):1885–1895

    Article  CAS  PubMed  Google Scholar 

  35. Maner WL, Garfield RE (2007) Identification of human term and preterm labor using artificial neural networks on uterine electromyography data. Ann Biomed Eng 35:465–473

    Article  PubMed  Google Scholar 

  36. Most O et al (2008) Can myometrial electrical activity identify patients in preterm labor? Am J Obstetr Gynecol 199(4):378-e1

    Article  Google Scholar 

  37. Fergus P et al (2013) Prediction of preterm deliveries from EHG signals using machine learning. PLoS ONE 8(10):e77154

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Vovsha I et al (2014) Predicting preterm birth is not elusive: machine learning paves the way to individual wellness. In: 2014 AAAI Spring Symposium Series

  39. Tran T et al (2016) Preterm birth prediction: stable selection of interpretable rules from high dimensional data. In: Machine Learning for Healthcare Conference. PMLR

  40. Weber A et al (2018) Application of machine-learning to predict early spontaneous preterm birth among nulliparous non-Hispanic black and white women. Ann Epidemiol 28(11):783–789

    Article  PubMed  Google Scholar 

  41. Despotovic D et al (2018) A machine learning approach for an early prediction of preterm delivery. In: 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY). IEEE, Piscataway, NJ

  42. Esty A et al (2018) Applying data preprocessing methods to predict premature birth. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Piscataway, NJ

  43. Gao C et al (2019) Deep learning predicts extreme preterm birth from electronic health records. J Biomed Inform 100:103334

    Article  PubMed  PubMed Central  Google Scholar 

  44. Degbedzui DK, Yüksel ME (2020) Accurate diagnosis of term–preterm births by spectral analysis of electrohysterography signals. Comput Biol Med 119:103677

    Article  PubMed  Google Scholar 

  45. Hamidinekoo A et al (2018) Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal 47:45–67

    Article  PubMed  Google Scholar 

  46. Mohammed MA et al (2018) Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images. Comput Electr Eng 70:871–882

    Article  Google Scholar 

  47. Asri H et al (2016) Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput Sci 83:1064–1069

    Article  Google Scholar 

  48. Huang MW et al (2017) SVM and SVM ensembles in breast cancer prediction. PLoS ONE 12(1):e0161501

    Article  PubMed  PubMed Central  Google Scholar 

  49. Khourdifi Y, Bahaj M (2018) Applying best machine learning algorithms for breast cancer prediction and classification. In: 2018 International conference on electronics, control, optimization and computer science (ICECOCS). IEEE, Piscataway, NJ

  50. Al-Azzam N, Shatnawi I (2021) Comparing supervised and semi-supervised machine learning models on diagnosing breast cancer. Ann Med Surg 62:53–64

    Article  Google Scholar 

  51. Abdar M, Makarenkov V (2019) CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer. Measurement 146:557–570

    Article  Google Scholar 

  52. Assiri AS, Nazir S, Velastin SA (2020) Breast tumor classification using an ensemble machine learning method. J Imaging 6(6):39

    Article  PubMed  PubMed Central  Google Scholar 

  53. Chougrad H, Zouaki H, Alheyane O (2018) Deep convolutional neural networks for breast cancer screening. Comput Methods Programs Biomed 157:19–30

    Article  PubMed  Google Scholar 

  54. Karthik SR, Perumal S, Chandra Mouli PVSSR. Breast cancer classification using deep neural networks. In: Knowledge computing and its applications: knowledge manipulation and processing techniques, vol 1. pp 227–241

  55. Cai H et al (2019) Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms. Comput Math Methods Med 2019:2717454

    Article  PubMed  PubMed Central  Google Scholar 

  56. Conant EF et al (2019) Improving accuracy and efficiency with concurrent use of artificial intelligence for digital breast tomosynthesis. Radiol Artif Intell 1(4):e180096

    Article  PubMed  PubMed Central  Google Scholar 

  57. Ionescu GV et al (2019) Prediction of reader estimates of mammographic density using convolutional neural networks. J Med Imaging 6(3):031405

    Article  Google Scholar 

  58. Wu W, Zhou H (2017) Data-driven diagnosis of cervical cancer with support vector machine-based approaches. IEEE Access 5:25189–25195

    Article  Google Scholar 

  59. Kurniawati YE, Permanasari AE, Fauziati S (2016) Comparative study on data mining classification methods for cervical cancer prediction using pap smear results. In: 2016 1st International Conference on Biomedical Engineering (IBIOMED). IEEE, Piscataway, NJ

  60. Malli PK, Nandyal S (2017) Machine learning technique for detection of cervical cancer using k-NN and artificial neural network. Int J Emerg Trend Technol Comput Sci (IJETTCS) 6(4):145–149

    Google Scholar 

  61. Vidya R, Nasira GM (2016) Prediction of cervical cancer using hybrid induction technique: a solution for human hereditary disease patterns. Indian J Sci Technol 9(30):1–10

    Article  Google Scholar 

  62. Kashyap D et al (2016) Cervical cancer detection and classification using independent level sets and multi SVMs. In: 2016 39th International conference on telecommunications and signal processing (TSP). IEEE, Piscataway, NJ

  63. Njoroge E et al (2006) Classification of cervical cancer cells using FTIR data. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, Piscataway, NJ

  64. Hyeon J et al (2017) Automating papanicolaou test using deep convolutional activation feature. In: 2017 18th IEEE International Conference on Mobile Data Management (MDM). IEEE, Piscataway, NJ

  65. Teeyapan K, Theera-Umpon N, Auephanwiriyakul S (2015) Application of support vector-based methods for cervical cancer cell classification. In: 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, Piscataway, NJ

  66. Harrison JE et al (2021) ICD-11: an international classification of diseases for the twenty-first century. BMC Med Inform Decision Making 21(6):1–10

    Google Scholar 

  67. Carson SA, Kallen AN (2021) Diagnosis and management of infertility: a review. JAMA 326(1):65–76

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Wang CW et al (2022) Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization. PLoS ONE 17(6):e0267554

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Penzias A et al (2021) Fertility evaluation of infertile women: a committee opinion. Fertil Steril 116(5):1255–1265

    Article  Google Scholar 

  70. Liu R et al (2021) Multifactor prediction of embryo transfer outcomes based on a machine learning algorithm. Front Endocrinol 12:745039

    Article  Google Scholar 

  71. Lee DH, Yoon SN (2021) Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. Int J Environ Res Publ Health 18(1):271

    Article  Google Scholar 

  72. Bori L et al (2020) Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential. Fertil Steril 114(6):1232–1241

    Article  PubMed  Google Scholar 

  73. Fainberg J, Kashanian JA (2019) Recent advances in understanding and managing male infertility. F1000Res 8:F1000 Faculty Rev-670

  74. Agarwal A et al (2015) Male infertility. Lancet 397(10271):319–333

    Article  Google Scholar 

  75. Sadeghi MR (2015) Unexplained infertility, the controversial matter in management of infertile couples. J Reprod Infertil 16(1):1

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Piché MP et al (2018) Lifestyle-related factors associated with reproductive health in couples seeking fertility treatments: results of a pilot study. Int J Fertil Steril 12(1):19

    PubMed  PubMed Central  Google Scholar 

  77. Lee T et al (2024) A brief history of artificial intelligence embryo selection: from black-box to glass-box. Hum Reprod 39(2):285–292

    Article  PubMed  Google Scholar 

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  1. Harcourt Butler Technical University, Kanpur, India

    Imran Khan & Brajesh Kumar Khare

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  1. Imran Khan
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Khan, I., Khare, B.K. Exploring the potential of machine learning in gynecological care: a review. Arch Gynecol Obstet (2024). https://doi.org/10.1007/s00404-024-07479-1

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