参考文献/References:
[1]Hoffman BL,主编.段华,王建六,主译.威廉姆斯妇科学(中文版).第3版.北京:北京大学医学出版社,2021.188-212.
[2]Stamenov GS, Vitale SG, Della Corte L, et al. Hysteroscopy and female infertility: A fresh look to a busy corner. Hum Fertil,2020,25(3):430-446.
[3]Lebduska E, Beshear D, Spataro BM. Abnormal uterine bleeding. Med Clin North Am,2023,107(2):235-246.
[4]Khoiwal K, Zaman R, Bahurupi Y, et al. Comparison of vaginoscopic hysteroscopy and traditional hysteroscopy: A systematic review and metaanalysis. Int J Gynecol Obstet,2023,164(1): 47-55.
[5]LeCun Y, Bengio Y, Hinton G. Deep learning. Nature,2015,521(7553):436-444.
[6]van der Velden BHM, Kuijf HJ, Gilhuijs KGA, et al. Explainable artificial intelligence (XAI) in deep learningbased medical image analysis. Med Image Anal,2022,79:102470.
[7]Russell SJ, Norvig P. Artificial Intelligence: A Modern Approach. 4th ed. Global ed. Boston: Pearson,1996.4-26.
[8]Moorthy J, Gandhi UD. A survey on medical image segmentation based on deep learning techniques. Big Data Cogn Comput,2022,6(4):117.
[9]Sogancioglu E, all E, Ginneken B van, et al. Deep learning for chest Xray analysis: A survey. Med. Image Anal,2021,72:102125.
[10]FridAdar M, Klang E, Amitai M, et al. Synthetic data augmentation using GAN for improved liver lesion classification. IEEE Int Symp Biomed Imaging,2018,2018:289-293.
[11]Neofytou MS, Tanos V, Pattichis MS, et al. A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer. Biomed Eng Online,2007,6:44.
[12]Neofytou MS, Tanos V, Pattichis MS, et al. Color based textureclassification of hysteroscopy images of the endometrium. Annu Int Conf IEEE Eng Med Biol Soc,2007,2007:864-867.
[13]Neofytou MS, Tanos V, Constantinou I, et al. Computeraided diagnosis in hysteroscopic imaging. IEEE J Biomed Health Inform,2015,19(3):1129-1136.
[14]Vlachokosta AA, Asvestas PA, Gkrozou F, et al. Classification of hysteroscopical images using texture and vessel descriptors. Med Biol Eng Comput,2013,51(8):859-867.
[15]Song D, Li TC, Zhang Y, et al. Correlation between hysteroscopy findings and chronic endometritis. Fertil Steril,2019,111(4):772-779.
[16]Bouet PE, El Hachem H, Monceau E, et al. Chronic endometritis in women with recurrent pregnancy loss and recurrent implantation failure: Prevalence and role of office hysteroscopy and immunohistochemistry in diagnosis. Fertil Steril,2016,105(1):106-110.
[17]Cicinelli E, Resta L, Nicoletti R, et al. Detection of chronic endometritis at fluid hysteroscopy. J Minim Invasive Gynecol,2005,12(6):514-518.
[18]Kitaya K, Yasuo T, Yamaguchi T. Bridging the diagnostic gap between histopathologic and hysteroscopic chronic endometritis with deep learning models. Medicina (Kaunas),2024,60(6):972.
[19]Zhao A, Du X, Yuan S, et al. Automated detection of endometrial polyps from hysteroscopic videos using deep learning. Diagnostics,2023,13(8):1409.
[20]Geubbelmans M, Rousseau AJ, Burzykowski T, et al. Artificial neural networks and deep learning. Am J Orthod Dentofacial Orthop,2024,165(2):248-251.
[21]Takahashi Y, Sone K, Noda K, et al. Automated system for diagnosing endometrial cancer by adopting deeplearning technology in hysteroscopy. PLoS One,2021,16(3):e0248526.
[22]Zhang Y, Wang Z, Zhang J, et al. Deep learning model for classifying endometrial lesions. J Transl Med,2021,19(1):10.
[23]Raimondo D, Raffone A, Salucci P, et al. Detection and classification of hysteroscopic images using deep learning. Cancers,2024,16(7):1315.
[24]Trk P, Harangi B. Digital image analysis with fully connected convolutional neural network to facilitate hysteroscopic fibroid resection. Gynecol Obstet Invest,2018,83(6):615-619.
[25]Chen M, Kong W, Li B, et al. Revolutionizing hysteroscopy outcomes: AIpowered uterine myoma diagnosis algorithm shortens operation time and reduces blood loss. Front Oncol,2023,13: 1325179.
[26]Sroussi J, Bourret A, Pourcelot AG, et al. Does hyaluronic acid gel reduce intrauterine adhesions after dilation and curettage in women with miscarriage? A multicentric randomized controlled trial (HYFACO study). Am J Obstet Gynecol,2022,227(4):597.e1-597.e8.
[27]Zhao X, Gao B, Yang X, et al. The density of endometrial glandular openings: A novel variable to predict the live birth rate in patients with intrauterine adhesions following hysteroscopic adhesiolysis. Hum Reprod,2021,36(4):965-975.
[28]Li Y, Duan H, Wang S. An XGBoost predictive model of ongoing pregnancy in patients following hysteroscopic adhesiolysis. Reprod Biomed Online,2023,46(6):965-972.
[29]Cao M, Pan Y, Zhang Q, et al. Predictive value of live birth rate based on different intrauterine adhesion evaluation systems following TCRA. Reprod Biol Endocrinol,2021,19(1):13.
[30]Zhao X, Sun D, Zhang A, et al. Uterine cavity parameters evaluated by hysteroscopy can predict the live birth rate for intrauterine adhesion patients. Front Med,2022,9:926754.
[31]Li B, Chen H, Duan H. Artificial intelligencedriven prognostic system for conception prediction and management in intrauterine adhesions following hysteroscopic adhesiolysis: A diagnostic study using hysteroscopic images. Front Bioeng Biotechnol,2024,12:1327207.
[32]Li B, Chen H, Lin X, et al. Multimodal learning system integrating electronic medical records and hysteroscopic images for reproductive outcome prediction and risk stratification of endometrial injury: A multicenter diagnostic study. Int J Surg,2024,110(6):3237-3248.