Institute of Physical Chemistry and Abbe Center of Photonics (IPC), Friedrich-Schiller-University, Jena, Germany.
Leibniz Institute of Photonic Technology (IPHT), Jena, Germany.
Sci Rep. 2021 Jun 2;11(1):11629. doi: 10.1038/s41598-021-91081-x.
Bladder cancer is one of the top 10 frequently occurring cancers and leads to most cancer deaths worldwide. Recently, blue light (BL) cystoscopy-based photodynamic diagnosis was introduced as a unique technology to enhance the detection of bladder cancer, particularly for the detection of flat and small lesions. Here, we aim to demonstrate a BL image-based artificial intelligence (AI) diagnostic platform using 216 BL images, that were acquired in four different urological departments and pathologically identified with respect to cancer malignancy, invasiveness, and grading. Thereafter, four pre-trained convolution neural networks were utilized to predict image malignancy, invasiveness, and grading. The results indicated that the classification sensitivity and specificity of malignant lesions are 95.77% and 87.84%, while the mean sensitivity and mean specificity of tumor invasiveness are 88% and 96.56%, respectively. This small multicenter clinical study clearly shows the potential of AI based classification of BL images allowing for better treatment decisions and potentially higher detection rates.
膀胱癌是最常见的癌症之一，也是全球导致癌症死亡的主要原因。最近，基于蓝光（BL）膀胱镜的光动力诊断作为一种独特的技术被引入，以提高膀胱癌的检测率，尤其是对平坦和小病变的检测率。在这里，我们旨在展示一个基于 BL 图像的人工智能（AI）诊断平台，该平台使用了 216 张 BL 图像，这些图像分别来自四个不同的泌尿科部门，并根据癌症的恶性程度、侵袭性和分级进行了病理鉴定。然后，使用四个预先训练的卷积神经网络来预测图像的恶性程度、侵袭性和分级。结果表明，恶性病变的分类灵敏度和特异性分别为 95.77%和 87.84%，而肿瘤侵袭性的平均灵敏度和平均特异性分别为 88%和 96.56%。这项小型多中心临床研究清楚地表明，基于 AI 的 BL 图像分类具有潜在的优势，可以更好地做出治疗决策，并有可能提高检测率。
