{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T11:33:32Z","timestamp":1791804812887,"version":"4.3.4"},"reference-count":39,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Colposcopy is indispensable for the diagnosis of cervical lesions. However, its diagnosis accuracy for high\u2010grade squamous intraepithelial lesion (HSIL) is at about 50%, and the accuracy is largely dependent on the skill and experience of colposcopists. The advancement in computational power made it possible for the application of artificial intelligence (AI) to clinical problems.\u00a0Here, we explored the feasibility and accuracy of the application of AI on precancerous and cancerous cervical colposcopic image recognition and classification.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The images were collected from 6002 colposcopy examinations of normal control, low\u2010grade squamous intraepithelial lesion (LSIL), and HSIL. For each patient, the original, Schiller test, and acetic\u2010acid images were all collected. We built a new neural network classification model based on the hybrid algorithm. EfficientNet\u2010b0 was used as the backbone network for the image feature extraction, and GRU(Gate Recurrent Unit)was applied for feature fusion of the three modes examinations (original, acetic acid, and Schiller test).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The connected network classifier achieved an accuracy of 90.61% in distinguishing HSIL from normal and LSIL. Furthermore, the model was applied to \u201cTrichotomy\u201d, which reached an accuracy of 91.18% in distinguishing the HSIL, LSIL and normal control at the same time.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our results revealed that as shown by the high accuracy of AI in the classification of colposcopic images, AI exhibited great potential to be an effective tool for the accurate diagnosis of cervical disease and for early therapeutic intervention in cervical precancer.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cam4.5581","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T11:41:32Z","timestamp":1673437292000},"page":"8690-8699","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Application of EfficientNet\u2010B0 and GRU\u2010based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5156-2148","authenticated-orcid":false,"given":"Xiaoyue","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Gynecology Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowen","family":"Pu","sequence":"additional","affiliation":[{"name":"Department of Gynecology Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhirou","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Gynecology Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lanzhen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Automation Shanghai Jiao Tong University  Shanghai China"},{"name":"Ningbo Artificial Intelligent Institute Shanghai Jiao Tong University  Ningbo China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kong\u2010Nan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Basic Medical Science Wenzhou Medical University  Wenzhou China"},{"name":"Australian Institute for Bioengineering and Nanotechnology The University of Queensland  St Lucia Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haichun","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Automation Shanghai Jiao Tong University  Shanghai China"},{"name":"Ningbo Artificial Intelligent Institute Shanghai Jiao Tong University  Ningbo China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1030-4601","authenticated-orcid":false,"given":"Haiyan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Gynecology Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(19)30072-5"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/AOG.0b013e31827001d5"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/1471-0528.15554"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/LGT.0000000000000342"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.5468\/ogs.2018.61.1.1"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/1471-0528.14783"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.24074"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00404-021-06012-y"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.7314\/APJCP.2014.15.1.423"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers12030603"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2009.06.006"},{"issue":"6","key":"e_1_2_8_14_1","first-page":"583","article-title":"Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images","volume":"11","author":"Miyagi Y","year":"2019","journal-title":"Mol Clin Oncol"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-70490-4"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djy225"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.21037\/atm-21-885"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.4137\/CIN.S17948"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-68252-3"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2020.104352"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-020-01613-x"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13040661"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13102419"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1097\/JTO.0b013e3181ec173d"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-11833-1"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.3390\/genes13101839"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21196346"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5580914"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.3390\/mi12060702"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2020.08.025"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2018-024920"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejogrb.2019.07.003"},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2352311"},{"key":"e_1_2_8_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2994778"},{"key":"e_1_2_8_35_1","doi-asserted-by":"publisher","DOI":"10.1093\/ajcp\/aqaa069"},{"key":"e_1_2_8_36_1","first-page":"4370851","article-title":"The performance of artificial intelligence in cervical colposcopy: a retrospective data analysis","volume":"2022","author":"Zhao Y","year":"2022","journal-title":"J Oncol"},{"key":"e_1_2_8_37_1","doi-asserted-by":"publisher","DOI":"10.3892\/mco.2021.2460"},{"key":"e_1_2_8_38_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.851367"},{"key":"e_1_2_8_39_1","doi-asserted-by":"publisher","DOI":"10.2147\/MDER.S366303"},{"key":"e_1_2_8_40_1","doi-asserted-by":"publisher","DOI":"10.3802\/jgo.2022.33.e57"}],"container-title":["Cancer Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cam4.5581","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/cam4.5581","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cam4.5581","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T01:19:21Z","timestamp":1692321561000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cam4.5581"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,11]]},"references-count":39,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["10.1002\/cam4.5581"],"URL":"https:\/\/doi.org\/10.1002\/cam4.5581","archive":["Portico"],"relation":{},"ISSN":["2045-7634","2045-7634"],"issn-type":[{"value":"2045-7634","type":"print"},{"value":"2045-7634","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,11]]},"assertion":[{"value":"2022-07-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-12-17","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-01-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}