研究动态
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用于 RCM-OCT 图像中基底细胞癌检测和肿瘤深度评估的人工智能算法和 3D 体积渲染:一项试点研究。

Artificial intelligence algorithms and 3D volumetric rendering for basal cell carcinoma detection and tumor depth assessment in RCM-OCT images: a pilot study.

发表日期:2024 May 23
作者: Alexander Pan, Nathalie de Carvalho, Luisa Silva, Ucalene Harris, Stephen Dusza, Aditi Sahu, Kivanc Kose, Jilliana Monnier, Chih-Shan Chen, Manu Jain
来源: CLINICAL AND EXPERIMENTAL DERMATOLOGY

摘要:

反射共聚焦显微镜 - 光学相干断层扫描 (RCM-OCT) 设备在检测和评估体内基底细胞癌 (BCC) 深度方面显示出实用性,但对于新手来说解释起来具有挑战性。应用于 RCM-OCT 的人工智能 (AI) 可以为读者提供帮助。我们使用活检确认的 BCC 的 OCT 光栅来训练人工智能 (AI) 模型,以检测和创建 3D BCC 渲染并自动测量肿瘤深度。经过训练的 AI 模型被应用于包含基底细胞癌、良性病变和正常皮肤栅格的单独测试集。进行盲法读者分析以及肿瘤深度与组织病理学的相关性。 BCC 检测从仅查看 OCT 栅格(灵敏度 73.3%,特异性 45.5%)改进为使用 AI 生成的 BCC 渲染查看栅格(灵敏度 86.7%,特异性 48.5%)。 AI 和组织学测量深度之间的肿瘤深度测量达到 Pearson 相关性 r2 = 0.59 (p=0.02)。因此,在 RCM-OCT 设备中添加人工智能可能会广泛扩展其效用。© 作者 2024。由牛津大学出版社代表英国皮肤科医师协会出版。版权所有。如需商业重复使用,请联系 reprints@oup.com 获取转载和转载的翻译权。所有其他权限都可以通过我们网站文章页面上的权限链接通过我们的 RightsLink 服务获得 - 如需更多信息,请联系journals.permissions@oup.com。
The Reflectance Confocal Microscopy - Optical Coherence Tomography (RCM-OCT) device has shown utility in detecting and assessing depth of basal cell carcinoma (BCC) in vivo but is challenging for novices to interpret. Artificial intelligence (AI) applied to RCM-OCT could aid readers. We trained artificial intelligence (AI) models, using OCT rasters of biopsy-confirmed BCC, to detect and create 3D BCC rendering and automatically measure tumor depth. Trained AI models were applied to a separate test set containing rasters of BCC, benign lesions, and normal skin. Blinded reader analysis and tumor depth correlation with histopathology were conducted. BCC detection improved from viewing OCT rasters only (sensitivity 73.3%, specificity 45.5%) to viewing rasters with AI-generated BCC rendering (sensitivity 86.7%, specificity 48.5%). A Pearson Correlation r2 = 0.59 (p=0.02) was achieved for the tumor depth measurement between AI and histologic measured depths. Thus, addition of AI to the RCM-OCT device may expand its utility widely.© The Author(s) 2024. Published by Oxford University Press on behalf of British Association of Dermatologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.