Deployment and assessment of a deep learning model for real-time detection of anal precancer with high frame rate high-resolution microendoscopy.
Deployment and assessment of a deep learning model for real-time detection of anal precancer with high frame rate high-resolution microendoscopy.
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利用高帧率高分辨率显微内窥镜实时检测肛门癌前病变的深度学习模型的部署和评估。
DOI：
10.1038/s41598-023-49197-9
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发表时间：
2023-12-14
影响因子：
4.6
通讯作者：
Chiao, Elizabeth
中科院分区：
文献类型：
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作者：
Brenes, David;Kortum, Alex;Coole, Jackson;Carns, Jennifer;Schwarz, Richard;Vohra, Imran;Richards-Kortum, Rebecca;Liu, Yuxin;Cai, Zhenjian;Sigel, Keith;Anandasabapathy, Sharmila;Gaisa, Michael;Chiao, Elizabeth
艾滋病毒感染者的肛门癌发病率明显较高，因为艾滋病毒增加了人乳头瘤病毒的致癌潜力。美国肛门癌的发病率最近有所增加，诊断和治疗受到高随访失踪率的阻碍。自动实时诊断AIN 2+的新方法可以实现“观察和治疗”策略，降低失访率。先前的一项回顾性研究表明，高分辨率显微内窥镜（HRME）结合深度学习模型诊断AIN 2+的准确性与专家临床印象相当（敏感性0.92 [P = 0.68]，特异性0.60 [P = 0.48]）。然而，运动伪影和噪声导致许多图像质量控制失败（17%）。在这里，我们提出了一种具有改进图像质量的高帧率HRME (HF-HRME)，与深度学习模型一起应用于临床，并对实时检测AIN 2+进行了前瞻性评估。HF-HRME通过采用高帧率相机来增强对比度和限制运动伪影，将图像质量控制失败的比例降低到4.6%。在检测组织病理学证实的AIN 2+时，HF-HRME的敏感性为0.91，特异性为0.87，优于以往HRME （P < 0.001）和临床印象（P < 0.0001）。
Anal cancer incidence is significantly higher in people living with HIV as HIV increases the oncogenic potential of human papillomavirus. The incidence of anal cancer in the United States has recently increased, with diagnosis and treatment hampered by high loss-to-follow-up rates. Novel methods for the automated, real-time diagnosis of AIN 2+ could enable "see and treat" strategies, reducing loss-to-follow-up rates. A previous retrospective study demonstrated that the accuracy of a high-resolution microendoscope (HRME) coupled with a deep learning model was comparable to expert clinical impression for diagnosis of AIN 2+ (sensitivity 0.92 [P = 0.68] and specificity 0.60 [P = 0.48]). However, motion artifacts and noise led to many images failing quality control (17%). Here, we present a high frame rate HRME (HF-HRME) with improved image quality, deployed in the clinic alongside a deep learning model and evaluated prospectively for detection of AIN 2+ in real-time. The HF-HRME reduced the fraction of images failing quality control to 4.6% by employing a high frame rate camera that enhances contrast and limits motion artifacts. The HF-HRME outperformed the previous HRME (P < 0.001) and clinical impression (P < 0.0001) in the detection of histopathologically confirmed AIN 2+ with a sensitivity of 0.91 and specificity of 0.87.
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影响因子：
3.4
作者：
Coole, Jackson B.;Brenes, David;Richards-Kortum, Rebecca
通讯作者：
Richards-Kortum, Rebecca
影响因子：
6.4
作者：
de Martel C;Plummer M;Vignat J;Franceschi S
通讯作者：
Franceschi S
影响因子：
168.9
作者：
MELBYE, M;SPROGEL, P
通讯作者：
SPROGEL, P
DOI：
10.1016/j.compmedimag.2022.102052
发表时间：
2022-04
期刊：
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
影响因子：
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作者：
通讯作者：
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DOI：
10.1056/nejmoa2201048
发表时间：
2022-06-16
期刊：
The New England journal of medicine
影响因子：
--
作者：
通讯作者：
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