[1]	陈艳红, 陈敦金. 人工智能——助力产科服务提升[J]. 中国实用妇科与产科杂志, 2024, 40(10):964-968. DOI:10.19538/j.fk2024100102. Cited in this article [1]

[2]	张瑜亮, 杜丽丽, 陈敦金. 人工智能在产科领域的研究进展[J]. 中华围产医学杂志, 2025, 28(3):258-260. DOI:10.3760/cma.j.cn113903-20240607-00413. Cited in this article [1]

[3]	李雷, 高宇, 郎景和. 人工智能时代的妇产科学发展[J]. 中华妇产科杂志, 2024, 59(9):732-736. DOI:10.3760/cma.j.cn112141-20240315-00155. Cited in this article [1]

[4]	Stringer JSA, Pokaprakarn T, Prieto JC, et al. Diagnostic accuracy of an integrated AI tool to estimate gestational age from blind ultrasound sweeps[J]. JAMA, 2024, 332(8):649657. DOI:10.1001/jama.2024.10770. Cited in this article [1]

[5]	Danish HM, Suhail Z, Farooq F. Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound images[J]. Front Pediatr, 2024,12:1453302. DOI:10.3389/fped.2024.1453302. Cited in this article [1]

[6]	Płotka S, Klasa A, Lisowska A, et al. Deep learning fetal ultrasound video model match human observers in biometric measurements[J]. Phys Med Biol, 2022, 67(4):045010. DOI:10.1088/1361-6560/ac4d85. Cited in this article [1]

[7]	Cho HC, Sun S, Hyun CM, et al. Automated ultrasound assessment of amniotic fluid index using deep learning[J]. Med Image Anal, 2021,69:101951. DOI:10.1016/j.media.2020.101951. Cited in this article [1]

[8]

Wang Y, Zhang Q, Chen C, et al. Automated prediction of early spontaneous miscarriage based on analyzing ultrasonographic gestational sac imaging by convolutional neural network: a case-control and cohort study[J]. BMC Pregnancy Childbirth, 2022, 22(1):621. DOI:10.1186/s12884-022-04936-0. Cited in this article [1]

[9]	Rueangket P, Rittiluechai K, Prayote A. Predictive analytical model for ectopic pregnancy diagnosis: statistics vs. machine learning[J]. Front Med (Lausanne), 2022,9:976829. DOI:10.3389/fmed.2022.976829. Cited in this article [1]

[10]	Gil MM, Cuenca-Gómez D, Rolle V, et al. Validation of machine-learning model for first-trimester prediction of pre-eclampsia using cohort from PREVAL study[J]. Ultrasound Obstet Gynecol, 2024, 63(1):68-74. DOI: 10.1002/uog.27478. Cited in this article [1]

[11]	Wu Y, Shen L, Zhao L, et al. Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features[J]. NPJ Digit Med, 2025, 8(1):188. DOI: 10.1038/s41746-025-01582-6. Cited in this article [1]

[12]	唐灵儿, 王妍. 机器学习在产前电子胎心监护领域的应用[J]. 中国实用妇科与产科杂志, 2024, 40(4):470-472. DOI:10.19538/j.fk2024040119. Cited in this article [1]

[13]	Hayajneh A, Serpedin E, Stotland MA. Facial anomaly appraisal using discrepancy optimization-driven automatic inpainting[J]. IEEE J Biomed Health Inform, 2025, 29(11):8423-8435. DOI:10.1109/JBHI.2025.3569290. Cited in this article [1]

[14]	Arnaout R, Curran L, Zhao Y, et al. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease[J]. Nat Med, 2021, 27(5):882-891. DOI:10.1038/s41591-021-01342-5. Cited in this article [1]

[15]	Fiorentino MC, Villani FP, Di Cosmo M, et al. A review on deep-learning algorithms for fetal ultrasound-image analysis[J]. Med Image Anal, 2023,83:102629. DOI:10.1016/j.media.2022.102629. Cited in this article [1]

[16]	Athalye C, van Nisselrooij A, Rizvi S, et al. Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection[J]. Ultrasound Obstet Gynecol, 2024, 63(1):44-52. DOI:10.1002/uog.27503. Cited in this article [1]

[17]	Young D, Khan N, Hobson SR, et al. Diagnosis of placenta accreta spectrum using ultrasound texture feature fusion and machine learning[J]. Comput Biol Med, 2024,178:108757. DOI:10.1016/j.compbiomed.2024.108757. Cited in this article [1]

[18]	Kliewer MA, Bagley AR, Sadowski EA, et al. Placenta accreta spectrum: the pattern and character of intraplacental blood flow by color and spectral Doppler[J]. Abdom Radiol (NY), 2023, 48(1):377-386. DOI:10.1007/s00261-022-03708-w. Cited in this article [1]

[19]	Barnova K, Martinek R, Vilimkova Kahankova R, et al. Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring[J]. Archives of Computational Methods in Engineering, 2024,31: 2557‑2588. DOI:10.1007/s11831-023-10055-6. Cited in this article [1]

[20]	刘瀛, 王俊芳, 张磊磊. 人工智能在胎心监护判读中的应用. 中华围产医学杂志, 2024, 27(12):1092-1096. DOI:10.3760/cma.j.cn113903-20230826-00156. Cited in this article [1]

[21]	Liang H, Lu Y. A CNN-RNN unified framework for intrapartum cardiotocograph classification[J]. Comput Methods Programs Biomed, 2023,229:107300. DOI:10.1016/j.cmpb.2022.107300. Cited in this article [1]

[22]	McCoy JA, Levine LD, Wan G, et al. Intrapartum electronic fetal heart rate monitoring to predict acidemia at birth with the use of deep learning[J]. Am J Obstet Gynecol, 2025, 232(1):116.e1-116.e9. DOI:10.1016/j.ajog.2024.04.022. Cited in this article [1]

[23]	温慧莹, 李笑天. 人工智能及机器学习深度算法在产科母胎疾病中的应用[J]. 中国实用妇科与产科杂志, 2024, 40(8):804-809. DOI:10.19538/j.fk2024080109. Cited in this article [1]

[24]	Huang ZY, Yu JS, Shan Y. A multimodal deep learning‑based algorithm for specific fetal heart rate events detection[J]. Biomed Tech(Berl), 2024, 70(2):183-194. DOI:10.1515/bmt-2024-0334. Cited in this article [1]

[25]	Bai J, Kang X, Wang W, et al. A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system[J]. Digit Health, 2024,10:20552076241304934. DOI:10.1177/20552076241304934. Cited in this article [1]

[26]	Owusu FO, Addai-Manu H, Agbedinu ES, et al. Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana[J]. BMC Pregnancy Childbirth, 2025, 25(1):690. DOI:10.1186/s12884-025-07716-8. Cited in this article [1]

[27]	De Ramón Fernández A, Ruiz Fernández D, Prieto Sánchez MT. Prediction of the mode of delivery using artificial intelligence algorithms[J]. Comput Methods Programs Biomed, 2022,219:106740. DOI:10.1016/j.cmpb.2022.106740. Cited in this article [1]

[28]	Varghese C, Harrison EM, O'Grady G, et al. Artificial intelligence in surgery[J]. Nat Med, 2024, 30(5):1257-1268. DOI:10.1038/s41591-024-02970-3. Cited in this article [1]

[29]	贺芳, 陈敦金. 人工智能赋能产科发展:挑战与方向[J]. 中华围产医学杂志, 2025, 28(10):823-828. DOI:10.3760/cma.j.cn113903-20250705-00364. Cited in this article [1]

[30]	Kong AYH, Liu N, Tan HS, et al. Artificial intelligence in obstetric anaesthesiology - the future of patient care?[J]. Int J Obstet Anesth, 2025,61:104288. DOI:10.1016/j.ijoa.2024.104288. Cited in this article [1]

[31]	Xu L, Liu Z, Ma N, et al. Development and validation of an artificial neural network prediction model for postpartum hemorrhage with placenta previa[J]. Minerva Anestesiol, 2023, 89(11):977-985.DOI:10.23736/S0375-9393.23.17366-4. Cited in this article [1]

[32]	Wang M, Gao Y, Zhang Y, et al. Quantitative prediction of postpartum hemorrhage in cesarean section on machine learning[J]. BMC Med Inform Decis Mak, 2024, 24(1):166. DOI:10.1186/s12911-024-02571-7. Cited in this article [1]

[33]	Ende HB, Domenico HJ, Polic A, et al. Development and validation of an automated,real-time predictive model for postpartum hemorrhage[J]. Obstet Gynecol, 2024, 144(1):109-117. DOI: 10.1097/AOG.0000000000005600. Cited in this article [1]

[34]	Xu Z, Gao Y, Wang F, et al. Depression detection methods based on multimodal fusion of voice and text[J]. Sci Rep, 2025, 15((1):21907. DOI:10.1038/s41598-025-03524-4. Cited in this article [1]

[35]	García‑Méndez S, de Arriba‑Pérez F. Detecting and explaining postpartum depression in real‑time with generative artificial intelligence[J]. Applied Artificial Intelligence, 2025, 39(1):2515063. DOI:10.1080/08839514.2025.2515063. Cited in this article [1]

[36]	Makhloughi F. Artificial intelligence-based non-invasive bilirubin prediction for neonatal jaundice using 1D convolutional neural network[J]. Sci Rep, 2025, 15(1):11571. DOI:10.1038/s41598-025-96100-9. Cited in this article [1]

[37]	Zheng M, Zhang Y, Laws RA, et al. Development of machine learning-based risk prediction models to predict rapid weight gain in infants: analysis of seven cohorts[J]. JMIR Public Health Surveill, 2025,11:e69220. doi:10.2196/69220. Cited in this article [1]

[38]	Chen SB, Huang CH, Weng SC, et al. Detection of pediatric developmental delay with machine learning technologies[J]. PLoS One, 2025, 20(5):e0324204. DOI:10.1371/journal.pone.0324204. Cited in this article [1]

[39]	El-Sayegh B, Dumoulin C, Leduc-Primeau F, et al. Improving pelvic floor muscle training with AI: a novel quality assessment system for pelvic floor dysfunction[J]. Sensors (Basel), 2024, 24(21):6937. DOI:10.3390/s24216937. Cited in this article [1]

[40]	De Souza J, Viswanath VK, Echterhoff JM, et al. Augmenting telepostpartum care with vision-based detection of breastfeeding-related conditions: algorithm development and validation[J]. JMIR AI, 2024,3:e54798. DOI: 10.2196/54798. Cited in this article [1]

[41]	Montenegro J, da Costa CA, da Rosa Righi R, et al. Development and validation of conversational agent to pregnancy safe-education[J]. J Med Syst, 2023, 47(1):7. DOI:10.1007/s10916-022-01903-2. Cited in this article [1]

[42]	Kim J, Oh S, Avila R, et al. A compact,wireless system for continuous monitoring of breast milk expressed during breastfeeding[J]. Nat Biomed Eng, 2025, 9(10):1645-1655. DOI:10.1038/s41551-025-01393-w. Cited in this article [1]

[43]	Leitner K, Cutri‑French C, Mandel A, et al. A conversational agent using natural language processing for postpartum care for new mothers: development and engagement analysis[J]. JMIR AI, 2025,4:e58454. DOI:10.2196/58454. Cited in this article [1]

[44]	Abbas Q, Jeong W, Lee SW. Explainable AI in clinical decision support systems: a meta-analysis of methods,applications,and usability challenges[J]. Healthcare (Basel), 2025, 13(17):2154. DOI:10.3390/healthcare13172154. Cited in this article [1]

[45]

Terry A, Arnold C, White Z, et al. Closing-the-loop: a novel care coordination tool to reduce maternal healthcare utilization postpartum and collaboratively build interventions to address community needs[J]. BMC Pregnancy Childbirth, 2025, 25(1):580. DOI:10.1186/s12884-025-07702-0. Cited in this article [1]

[46]	Moor M, Banerjee O, Abad ZSH, et al. Foundation models for generalist medical artificial intelligence[J]. Nature, 2023, 616(7956):259-265. DOI:10.1038/s41586-023-05881-4. Cited in this article [1]

[47]	Bucciarelli V, Moscucci F, Dei Cas A, et al. Maternal-fetal dyad beyond the phenomenology of pregnancy: from primordial cardiovascular prevention on out,do not miss this boat![J]. Curr Probl Cardiol, 2024, 49(9):102695. DOI:10.1016/j.cpcardiol.2024.102695. Cited in this article [1]

[48]	狄文, 金明珠. 人工智能技术在妇产科应用中的思考与挑战[J]. 中国实用妇科与产科杂志, 2025, 41(1):11-14. DOI:10.19538/j.fk2025010104. Cited in this article [1]

[49]	董蜜兰, 罗征秀. 机器学习在儿童重症肺炎领域中的研究进展及应用前景[J]. 中国实用儿科杂志, 2025, 40(8):673-676. DOI:10.19538/j.ek2025080609. Cited in this article [1]

[50]	唐雷, 钟世镇. 人工智能及其在医学领域中的应用[J]. 中国实用妇科与产科杂志, 2024, 40(9):876-878. DOI:10.19538/j.fk2024090104. Cited in this article [1]

[51]	陈春林, 李朋飞, 陈晓林. 微无创与人工智能的融合:妇产科疾病诊治的未来[J]. 中国实用妇科与产科杂志, 2024, 40(9):867-871. DOI:10.19538/j.fk2024090102. Cited in this article [1]

[52]	杨孜. 妊娠并发症及重症发生发展之预测预防预警三位一体贯穿始终[J]. 中国实用妇科与产科杂志, 2024, 40(8):769-774. DOI:10.19538/j.fk2024080101. Cited in this article [1]

[53]	方莹, 熊实秋. 重视儿童Peutz-Jeghers综合征的全流程规范化管理[J]. 中国实用儿科杂志, 2025, 40(11):881-885. DOI:10.19538/j.ek2025110601. Cited in this article [1]