Neil B. Marya MD, Patrick D. Powers, Melanie C. Bois MD, Christopher Hartley MD, Sarah E. Kerr MD, Judith Jebastin Thangaiah MBBS, MD, Daniel Norton BA, Barham K. Abu Dayyeh MD, MPH, Richard Cantley MD, Vinay Chandrasekhara MD, Gregory Gores MD, Ferga C. Gleeson MB, BCh, Ryan J. Law DO, Zahra Maleki MD, John A. Martin MD, Liron Pantanowitz MB, BCh, Bret Petersen MD, Andrew C. Storm MD, Michael J. Levy MD, Rondell P. Graham MBBS
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The aim of this trial was to assess the efficiency and accuracy of cytologists using a novel application with this AI tool.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Consecutive bile duct brushing WSIs from indeterminate strictures were obtained. A multidisciplinary panel reviewed all relevant information and provided a central interpretation for each WSI as being “positive,” “negative,” or “indeterminate.” The WSIs were then uploaded to the AI application. The AI scored each WSI as positive or negative for malignancy (i.e., computer-aided diagnosis [CADx]). For each WSI, the AI prioritized cytologic tiles by the likelihood that malignant material was present in the tile. Via the AI, blinded cytologists reviewed all WSIs and provided interpretations (i.e., computer-aided detection [CADe]). The diagnostic accuracies of the WSI evaluation via CADx, CADe, and the original clinical cytologic interpretation (official cytologic interpretation [OCI]) were compared.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Of the 84 WSIs, 15 were positive, 42 were negative, and 27 were indeterminate after central review. The WSIs generated on average 141,950 tiles each. Cytologists using the AI evaluated 10.5 tiles per WSI before making an interpretation. Additionally, cytologists required an average of 84.1 s of total WSI evaluation. WSI interpretation accuracies for CADx (0.754; 95% CI, 0.622–0.859), CADe (0.807; 95% CI, 0.750–0.856), and OCI (0.807; 95% CI, 0.671–0.900) were similar.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>This trial demonstrates that an AI application allows cytologists to perform a triaged review of WSIs while maintaining accuracy.</p>\n </section>\n </div>","PeriodicalId":9410,"journal":{"name":"Cancer Cytopathology","volume":"132 12","pages":"779-787"},"PeriodicalIF":2.6000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Utilization of an artificial intelligence–enhanced, web-based application to review bile duct brushing cytologic specimens: A pilot study\",\"authors\":\"Neil B. Marya MD, Patrick D. Powers, Melanie C. Bois MD, Christopher Hartley MD, Sarah E. Kerr MD, Judith Jebastin Thangaiah MBBS, MD, Daniel Norton BA, Barham K. 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引用次数: 0
摘要
背景：作者之前开发了一种人工智能（AI），用于协助细胞学专家评估从胆管刷洗标本中生成的数字全切片图像（WSI）。本试验旨在评估细胞学专家使用该人工智能工具的新型应用的效率和准确性：方法：连续获取来自不确定狭窄的胆管刷洗 WSI。一个多学科小组审查了所有相关信息，并对每份 WSI 提供了 "阳性"、"阴性 "或 "不确定 "的集中解释。然后将 WSI 上传到人工智能应用程序。人工智能将每个 WSI 打分为恶性肿瘤阳性或阴性（即计算机辅助诊断 [CADx]）。对于每个 WSI，人工智能会根据片段中出现恶性物质的可能性对细胞学片段进行优先排序。通过人工智能，盲法细胞学专家对所有 WSI 进行审查并提供解释（即计算机辅助检测 [CADe]）。通过 CADx、CADe 和原始临床细胞学解释（官方细胞学解释 [OCI]）对 WSI 评估的诊断准确率进行了比较：在 84 例 WSI 中，15 例为阳性，42 例为阴性，27 例经中央审查后为不确定。每项 WSI 平均产生 141,950 张纸片。使用人工智能的细胞学专家在做出解释前对每个 WSI 评估了 10.5 张纸片。此外，细胞学专家对 WSI 的总评估时间平均为 84.1 秒。CADx（0.754；95% CI，0.622-0.859）、CADe（0.807；95% CI，0.750-0.856）和OCI（0.807；95% CI，0.671-0.900）的WSI判读准确率相似：这项试验表明，人工智能应用允许细胞学专家对 WSI 进行分流审查，同时保持准确性。
Utilization of an artificial intelligence–enhanced, web-based application to review bile duct brushing cytologic specimens: A pilot study
Background
The authors previously developed an artificial intelligence (AI) to assist cytologists in the evaluation of digital whole-slide images (WSIs) generated from bile duct brushing specimens. The aim of this trial was to assess the efficiency and accuracy of cytologists using a novel application with this AI tool.
Methods
Consecutive bile duct brushing WSIs from indeterminate strictures were obtained. A multidisciplinary panel reviewed all relevant information and provided a central interpretation for each WSI as being “positive,” “negative,” or “indeterminate.” The WSIs were then uploaded to the AI application. The AI scored each WSI as positive or negative for malignancy (i.e., computer-aided diagnosis [CADx]). For each WSI, the AI prioritized cytologic tiles by the likelihood that malignant material was present in the tile. Via the AI, blinded cytologists reviewed all WSIs and provided interpretations (i.e., computer-aided detection [CADe]). The diagnostic accuracies of the WSI evaluation via CADx, CADe, and the original clinical cytologic interpretation (official cytologic interpretation [OCI]) were compared.
Results
Of the 84 WSIs, 15 were positive, 42 were negative, and 27 were indeterminate after central review. The WSIs generated on average 141,950 tiles each. Cytologists using the AI evaluated 10.5 tiles per WSI before making an interpretation. Additionally, cytologists required an average of 84.1 s of total WSI evaluation. WSI interpretation accuracies for CADx (0.754; 95% CI, 0.622–0.859), CADe (0.807; 95% CI, 0.750–0.856), and OCI (0.807; 95% CI, 0.671–0.900) were similar.
Conclusions
This trial demonstrates that an AI application allows cytologists to perform a triaged review of WSIs while maintaining accuracy.
期刊介绍：
Cancer Cytopathology provides a unique forum for interaction and dissemination of original research and educational information relevant to the practice of cytopathology and its related oncologic disciplines. The journal strives to have a positive effect on cancer prevention, early detection, diagnosis, and cure by the publication of high-quality content. The mission of Cancer Cytopathology is to present and inform readers of new applications, technological advances, cutting-edge research, novel applications of molecular techniques, and relevant review articles related to cytopathology.
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