Renê Gerhard, Cioly Rivero Colmenarez, Corinne Selle, Gaël Paul Hammer
{"title":"数字细胞学结合人工智能与传统显微镜肛门细胞学的比较：初步研究。","authors":"Renê Gerhard, Cioly Rivero Colmenarez, Corinne Selle, Gaël Paul Hammer","doi":"10.1111/cyt.13482","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Introduction</h3>\n \n <p>Recent studies have shown that digital cytology (DC) coupled with artificial intelligence (AI) algorithms is a valid approach to the diagnosis of cervico-vaginal lesions using liquid-based cytology (LBC). We evaluated the use of these methods for anal LBC specimens.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>A series of 124 anal LBC slides previously diagnosed by conventional microscopy (CC) were reviewed with a DC/AI system that generated a gallery of images. Diagnoses based on the selected images, according to the 2014 Bethesda System for Reporting Cervical Cytology, were compared to CC.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Overall, CC and DC/AI approaches detected a similar number of abnormal (ASC-US+) cases (63 and 62 cases, respectively). We observed an exact concordance between CC and DC in 70 (57.9%) cases, corresponding to a moderate agreement between the two approaches (κ = 0.41, <i>p</i> < 0.001). A moderate agreement (κ = 0.48, <i>p</i> < 0.001) was also found when positive cases were stratified into ‘low-grade’ (ASC-US, LSIL) and ‘high-grade’ lesions (ASC-H, HSIL). The DC/AI system detected more cases of higher severity (ASC-H, HSIL: 9 and 2 cases, respectively) than CC (3 cases classified as HSIL).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>The number of ASC-US+ cases detected by both systems was similar. The DC/AI system detected more cases of higher severity compared to the CC.</p>\n </section>\n </div>","PeriodicalId":55187,"journal":{"name":"Cytopathology","volume":"36 3","pages":"250-258"},"PeriodicalIF":1.1000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Digital Cytology Combined With Artificial Intelligence Compared to Conventional Microscopy for Anal Cytology: A Preliminary Study\",\"authors\":\"Renê Gerhard, Cioly Rivero Colmenarez, Corinne Selle, Gaël Paul Hammer\",\"doi\":\"10.1111/cyt.13482\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Introduction</h3>\\n \\n <p>Recent studies have shown that digital cytology (DC) coupled with artificial intelligence (AI) algorithms is a valid approach to the diagnosis of cervico-vaginal lesions using liquid-based cytology (LBC). We evaluated the use of these methods for anal LBC specimens.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>A series of 124 anal LBC slides previously diagnosed by conventional microscopy (CC) were reviewed with a DC/AI system that generated a gallery of images. Diagnoses based on the selected images, according to the 2014 Bethesda System for Reporting Cervical Cytology, were compared to CC.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Overall, CC and DC/AI approaches detected a similar number of abnormal (ASC-US+) cases (63 and 62 cases, respectively). We observed an exact concordance between CC and DC in 70 (57.9%) cases, corresponding to a moderate agreement between the two approaches (κ = 0.41, <i>p</i> < 0.001). A moderate agreement (κ = 0.48, <i>p</i> < 0.001) was also found when positive cases were stratified into ‘low-grade’ (ASC-US, LSIL) and ‘high-grade’ lesions (ASC-H, HSIL). The DC/AI system detected more cases of higher severity (ASC-H, HSIL: 9 and 2 cases, respectively) than CC (3 cases classified as HSIL).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>The number of ASC-US+ cases detected by both systems was similar. The DC/AI system detected more cases of higher severity compared to the CC.</p>\\n </section>\\n </div>\",\"PeriodicalId\":55187,\"journal\":{\"name\":\"Cytopathology\",\"volume\":\"36 3\",\"pages\":\"250-258\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2025-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cytopathology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/cyt.13482\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cytopathology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/cyt.13482","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Digital Cytology Combined With Artificial Intelligence Compared to Conventional Microscopy for Anal Cytology: A Preliminary Study
Introduction
Recent studies have shown that digital cytology (DC) coupled with artificial intelligence (AI) algorithms is a valid approach to the diagnosis of cervico-vaginal lesions using liquid-based cytology (LBC). We evaluated the use of these methods for anal LBC specimens.
Methods
A series of 124 anal LBC slides previously diagnosed by conventional microscopy (CC) were reviewed with a DC/AI system that generated a gallery of images. Diagnoses based on the selected images, according to the 2014 Bethesda System for Reporting Cervical Cytology, were compared to CC.
Results
Overall, CC and DC/AI approaches detected a similar number of abnormal (ASC-US+) cases (63 and 62 cases, respectively). We observed an exact concordance between CC and DC in 70 (57.9%) cases, corresponding to a moderate agreement between the two approaches (κ = 0.41, p < 0.001). A moderate agreement (κ = 0.48, p < 0.001) was also found when positive cases were stratified into ‘low-grade’ (ASC-US, LSIL) and ‘high-grade’ lesions (ASC-H, HSIL). The DC/AI system detected more cases of higher severity (ASC-H, HSIL: 9 and 2 cases, respectively) than CC (3 cases classified as HSIL).
Conclusions
The number of ASC-US+ cases detected by both systems was similar. The DC/AI system detected more cases of higher severity compared to the CC.
期刊介绍：
The aim of Cytopathology is to publish articles relating to those aspects of cytology which will increase our knowledge and understanding of the aetiology, diagnosis and management of human disease. It contains original articles and critical reviews on all aspects of clinical cytology in its broadest sense, including: gynaecological and non-gynaecological cytology; fine needle aspiration and screening strategy.
Cytopathology welcomes papers and articles on: ultrastructural, histochemical and immunocytochemical studies of the cell; quantitative cytology and DNA hybridization as applied to cytological material.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
