人工智能在生殖内分泌领域中的应用

doi:10.3969/j.issn.1006-7795.2024.04.002

摘要/Abstract

摘要： 随着人工智能（artificial intelligence，AI）在医学领域的广泛应用与迅猛发展，生殖内分泌亚专科作为妇产科重要的一部分，也越来越多地应用AI技术解决生殖内分泌疾病的诊断、治疗、健康管理、监督及预防等问题。本文将从AI发展简史及AI在生殖内分泌领域中的应用进行阐述，总结AI在生殖内分泌领域应用中的现状、可行性及局限性，为AI在生殖内分泌领域的研究提供更广阔的思路。

关键词: 人工智能, 生殖内分泌, 机器学习, 深度学习, ChatGPT

Abstract: As artificial intelligence (AI) is widely used and rapidly developed in the medical field, more and more AI technology are applied to solve the problems of diagnosis, treatment, health management, supervision and prevention of reproductive endocrinology diseases.This article review briefly the history of AI development and the application of AI in the field of reproductive endocrinology, and summarized the current situation, feasibility, and limitations of the application of AI in the field of reproductive endocrinology so as to provide broader ideas for the research of AI in reproductive endocrinology.

Key words: artificial intelligence, reproductive endocrinology, machine learning, deep learning, ChatGPT

中图分类号:

R711
TP18

裴友娟, 阮祥燕, 鞠蕊, 王月姣, 蒋玲玲, 马郡. 人工智能在生殖内分泌领域中的应用[J]. 首都医科大学学报, 2024, 45(4): 566-572.

Pei Youjuan, Ruan Xiangyan, Ju Rui, Wang Yuejiao, Jiang Lingling, Ma Jun. The application of artificial intelligence in the reproductive endocrinology[J]. Journal of Capital Medical University, 2024, 45(4): 566-572.

参考文献

［1］ Zhao J, Wang R F, Song L Y, et al. Causal association between lipid-lowering drugs and female reproductive endocrine diseases: a drug-targeted Mendelian randomization study［J］. Front Endocrinol (Lausanne), 2023, 14: 1295412.
［2］ Wouk N, Helton M. Abnormal uterine bleeding in premenopausal women［J］. Am Fam Physician, 2019, 99(7): 435-443.
［3］ Azziz R, Carmina E, Chen Z J, et al. Polycystic ovary syndrome［J］. Nat Rev Dis Primers, 2016, 2: 16057.
［4］ Du L, Xu B, Huang C, et al. Menopausal symptoms and perimenopausal healthcare-seeking behavior in women aged 40-60 years: a community-based cross-sectional survey in Shanghai, China［J］. Int J Environ Res Public Health, 2020, 17(8): 2640.
［5］ Chon S J, Umair Z, Yoon M S. Premature ovarian insufficiency: past, present, and future［J］. Front Cell Dev Biol, 2021, 9: 672890.
［6］ Cowan S, Lim S, Alycia C, et al. Lifestyle management in polycystic ovary syndrome-beyond diet and physical activity［J］. BMC Endocr Disord, 2023, 23(1): 14.
［7］鄢海蓝, 史精华, 冷金花. 人工智能在子宫内膜异位症诊断中的研究进展［J］. 中华妇产科杂志, 2023, 58(6): 469-473.
［8］陈敏欣, 郜意, 康玉, 等. 人工智能在妇科恶性肿瘤领域应用的研究进展［J］. 中华妇产科杂志, 2022, 57(1): 72-78.
［9］ Tiwari S, Kane L, Koundal D, et al. SPOSDS: a smart polycystic ovary syndrome diagnostic system using machine learning［J］. Expert Syst Appl, 2022, 203: 117592.
［10］ Farrington G C.ENIAC: the birth of the information age［J］. Popular Science: the What's New Magazine, 1996, 248(3): 76.
［11］明斯基, 郑海燕. 人能思考, 计算机为什么不能?［J］. 国外社会科学, 1984(12): 18-20, 11.
［12］李新晖. 人工智能中的推理方法［J］. 计算机与现代化, 2001(2): 55-62.
［13］ Das S, Dey A, Pal A, et al.Applications of artificial intelligence in machine learning: review and prospect［J］. Int J Comput Appl, 2015, 115(9): 31-41.
［14］ Bishop C M. Pattern recognition and machine learning［M］. New York: Springer, 2006.
［15］ Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning［J］. Electron Mark, 2021, 31(3): 685-695.
［16］ Kamilaris A, Prenafeta-Boldú F X. Deep learning in agriculture: a survey［J］. Comput Electron Agric, 2018, 147: 70-90.
［17］ Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks［J］. Commun ACM, 2017, 60(6): 84-90.
［18］ Avanzo M, Wei L S, Stancanello J, et al. Machine and deep learning methods for radiomics［J］. Med Phys, 2020, 47(5): e185-e202.
［19］ Tran K A, Kondrashova O, Bradley A, et al. Deep learning in cancer diagnosis, prognosis and treatment selection［J］. Genome Med, 2021, 13(1): 152.
［20］ Guo Y M, Liu Y, Oerlemans A, et al. Deep learning for visual understanding: a review［J］. Neurocomputing, 2016, 187: 27-48.
［21］ LeCun Y, Bengio Y, Hinton G. Deep learning［J］. Nature, 2015, 521(7553): 436-444.
［22］ Radford A, Narasimhan K, Salimans T, et al. Improving language understanding by generative pre-training［DB/OL］. ［2024-02-20］. https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
［23］ Gordijn B, Have H T. ChatGPT: evolution or revolution?［J］. Med Health Care Philos, 2023, 26(1): 1-2.
［24］ Marr B. Revolutionizing healthcare the top 14 uses of ChatGPT in medicine and wellness［EB/OL］. (2023-03-15)［2024-03-18］. https://bernardmarr.com/revolutionizing-healthcare-the-top-14-uses-of-chatgpt-in-medicine-and-wellness/.
［25］ Mokmin N A M, Ibrahim N A. The evaluation of chatbot as a tool for health literacy education among undergraduate students［J］. Educ Inf Technol (Dordr), 2021, 26(5): 6033-6049.
［26］ Fraser I S, Critchley H O, Broder M, et al. The FIGO recommendations on terminologies and definitions for normal and abnormal uterine bleeding［J］. Semin Reprod Med, 2011, 29(5): 383-390.
［27］ Munro M G, Critchley H O, Fraser I S, et al. The FIGO classification of causes of abnormal uterine bleeding in the reproductive years［J］. Fertil Steril, 2011, 95(7): 2204-2208, 2208. e1-e3.
［28］ Makris G M, Pouliakis A, Siristatidis C, et al. Image analysis and multi-layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions［J］. Diagn Cytopathol, 2017, 45(3): 202-211.
［29］ Farzaneh F, Jafari Ashtiani A, Hashemi M, et al. Endometrial cancer in women with abnormal uterine bleeding: data mining classification methods［J］. Caspian J Intern Med, 2023, 14(3): 526-533.
［30］ Zhang J W, Yang C, Gong C M, et al. Magnetic resonance imaging parameter-based machine learning for prognosis prediction of high-intensity focused ultrasound ablation of uterine fibroids［J］. Int J Hyperthermia, 2022, 39(1): 835-846.
［31］ ESHRE Guideline Group on RPL, Bender Atik R, Christiansen O B, et al. ESHRE guideline: recurrent pregnancy loss: an update in 2022［J］. Hum Reprod Open, 2023, 2023(1): hoad002.
［32］ Li R, Zhang Q F, Yang D Z, et al. Prevalence of polycystic ovary syndrome in women in China: a large community-based study［J］. Hum Reprod, 2013, 28(9): 2562-2569.
［33］ Chitra P, Sumathi M, Srilatha K, et al.Review of artificial intelligent based algorithm for prediction of polycystic ovary syndrome (PCOS) from blood samples［C］//2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA). Coimbatore, India: IEEE, 2022: 1172-1176.
［34］ Ramamoorthy S, Senthil Kumar T, Mansoorroom S, et al. Enhancing intricate details of ultrasound PCOD scan images using Tailored Anisotropic Diffusion Filter (TADF)［C］//Intelligence in Big Data Technologies-Beyond the Hype: Proceedings of ICBDCC. Singapore: Springer, 2021: 43-52.
［35］ Wagh P, Panjwani M, Amrutha S. Early detection of PCOD using machine learning techniques［M］//Dev A, Sharma A, Agrawal S S. Artificial Intelligence and Speech Technology. Florida: CRC Press, 2021: 9-20.
［36］ Kiruthika V, Sathiya S, Ramya M M. Machine learning based ovarian detection in ultrasound images［J］. Int J Adv Mechatronic Syst, 2020, 8(2/3): 75-85.
［37］ Kodipalli A, Devi S. Prediction of PCOS and mental health using fuzzy inference and SVM［J］. Front Public Health, 2021, 9: 789569.
［38］ Mehrotra P, Chatterjee J, Chakraborty C, et al.Automated screening of polycystic ovary syndrome using machine learning techniques［C］//2011 Annual IEEE India Conference. Hyderabad, India: IEEE, 2011: 1-5.
［39］ Kaur R, Kumar R, Gupta M. Food image-based diet recommendation framework to overcome PCOS problem in women using deep convolutional neural network［J］. Comput Electr Eng, 2022, 103: 108298.
［40］ Shahmoradi L, Azadbakht L, Farzi J, et al. Nutritional management recommendation systems in polycystic ovary syndrome: a systematic review［J］. BMC Womens Health, 2024, 24(1): 234.
［41］杨冬梓, 赵晓苗. 多囊卵巢综合征的多学科干预和长期管理［J］. 中华妇产科杂志, 2015, 50(11): 810-813.
［42］ Wang M, Kartsonaki C, Guo Y, et al. Factors related to age at natural menopause in China: results from the China Kadoorie Biobank［J］. Menopause, 2021, 28(10): 1130-1142.
［43］ Thurston R C, Hernandez J, Del Rio J M, et al. Support vector machines to improve physiologic hot flash measures: application to the ambulatory setting［J］. Psychophysiology, 2011, 48(7): 1015-1021.
［44］ Malik M, Garg P, Malik C. Chapter 9—artificial intelligence-based prediction of health risks among women during menopause［M］//Gupta M, Hemanth D J. Artificial intelligence and machine learning for women’s health issues. New York: Academic Press, 2024: 137-150.
［45］ Pergialiotis V, Pouliakis A, Parthenis C, et al. The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women［J］. Public Health, 2018, 164: 1-6.
［46］ Bacak H O, Leblebicioglu K, Tanacan A, et al. Computerized hybrid decision-making system for hormone replacement therapy in menopausal women［J］. Technol Health Care, 2019, 27(1): 49-59.
［47］ Choueka D, Tabakin A L, Shalom D F. ChatGPT in urogynecology research: novel or not?［J］. Urogynecology (Phila), 2024: 10-1097.
［48］ European Society for Human Reproduction and Embryology (ESHRE) Guideline Group on POI, Webber L, Davies M, et al. ESHRE guideline: management of women with premature ovarian insufficiency［J］. Hum Reprod, 2016, 31(5): 926-937.
［49］ Giri R, Vincent AJ. Prevalence and risk factors of premature ovarian insufficiency/early menopause［J］. Semin Reprod Med, 2020, 38(4/5): 237-246.
［50］ De Vos M, Devroey P, Fauser B C. Primary ovarian insufficiency［J］. Lancet, 2010, 376(9744): 911-921.
［51］ Vujovic S. Aetiology of premature ovarian failure［J］. Menopause Int, 2009, 15(2): 72-75.
［52］中国人体健康科技促进会生育力保护与保存专业委员会, 国际妇科内分泌学会中国妇科内分泌学分会, 北京妇产学会内分泌分会, 等. 卵巢组织冻存移植防治医源性早发性卵巢功能不全临床应用指南［J］. 首都医科大学学报, 2023, 44(5): 695-703.
［53］ Sütcüolu B M, Güler M. Appropriateness of premature ovarian insufficiency recommendations provided by ChatGPT［J］. Menopause, 2023, 30(10): 1033-1037.
［54］ Yu Z Y, Li M J, Peng W L. Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning［J］. Sci Rep, 2023, 13(1): 11498.
［55］ Zhang Y Y, Hou J, Wang Q Y, et al. Application of transfer learning and feature fusion algorithms to improve the identification and prediction efficiency of premature ovarian failure［J］. J Healthc Eng, 2022, 2022: 3269692.
［56］ Yu L L, Qing X F. Diagnosis of idiopathic premature ovarian failure by color doppler ultrasound under the intelligent segmentation algorithm［J］. Comput Math Methods Med, 2022, 2022: 2645607.
［57］ Gianfrancesco M A, Tamang S, Yazdany J, et al. Potential biases in machine learning algorithms using electronic health record data［J］. JAMA Intern Med, 2018, 178(11): 1544-1547.
［58］ Chen J H, Asch S M. Machine learning and prediction in medicine-beyond the peak of inflated expectations［J］. N Engl J Med, 2017, 376(26): 2507-2509.
［59］ Hopcroft K. Artificial intelligence may not recognise the nuances of general practice［J］. BMJ, 2018, 363: k5205.
［60］ WHO. Ethics and governance of artificial intelligence for health［M］. Geneva, Switzerland: World Health Organization, 2021.
［61］国家药监局. 国家药监局发布《人工智能医用软件产品分类界定指导原则》［J］. 医学信息学杂志, 2021, 42(7): 94.

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