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人工智能辅助的受激拉曼组织学解读为未经处理的前列腺活检提供了近乎实时的病理反馈。

Stimulated Raman Histology Interpretation by Artificial Intelligence Provides Near-Real-Time Pathologic Feedback for Unprocessed Prostate Biopsies.

机构信息

Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada.

Vancouver Prostate Centre, Vancouver, British Columbia, Canada.

出版信息

J Urol. 2024 Mar;211(3):384-391. doi: 10.1097/JU.0000000000003811. Epub 2023 Dec 15.

Abstract

PURPOSE

Stimulated Raman histology is an innovative technology that generates real-time, high-resolution microscopic images of unprocessed tissue, significantly reducing prostate biopsy interpretation time. This study aims to evaluate the ability for an artificial intelligence convolutional neural network to interpretate prostate biopsy histologic images created with stimulated Raman histology.

MATERIALS AND METHODS

Unprocessed, unlabeled prostate biopsies were prospectively imaged using a stimulated Raman histology microscope. Following stimulated Raman histology creation, the cores underwent standard pathological processing and interpretation by at least 2 genitourinary pathologists to establish a ground truth assessment. A network, trained on 303 prostate biopsies from 100 participants, was used to measure the accuracy, sensitivity, and specificity of detecting prostate cancer on stimulated Raman histology relative to conventional pathology. The performance of the artificial intelligence was evaluated on an independent 113-biopsy test set.

RESULTS

Prostate biopsy images obtained through stimulated Raman histology can be generated within a time frame of 2 to 2.75 minutes. The artificial intelligence system achieved a rapid classification of prostate biopsies with cancer, with a potential identification time of approximately 1 minute. The artificial intelligence demonstrated an impressive accuracy of 96.5% in detecting prostate cancer. Moreover, the artificial intelligence exhibited a sensitivity of 96.3% and a specificity of 96.6%.

CONCLUSIONS

Stimulated Raman histology generates microscopic images capable of accurately identifying prostate cancer in real time, without the need for sectioning or tissue processing. These images can be interpreted by artificial intelligence, providing physicians with near-real-time pathological feedback during the diagnosis or treatment of prostate cancer.

摘要

目的

受激拉曼组织学是一种创新技术,可实时生成未经处理组织的高分辨率微观图像,显著缩短前列腺活检的解读时间。本研究旨在评估卷积神经网络人工智能解读受激拉曼组织学生成的前列腺活检组织学图像的能力。

材料与方法

使用受激拉曼组织学显微镜对未经处理、未经标记的前列腺活检进行前瞻性成像。受激拉曼组织学创建后,将核心进行标准病理处理和至少 2 位泌尿生殖病理学家的解读,以建立真实评估。一个经过 100 名参与者的 303 个前列腺活检训练的网络,用于测量人工智能在受激拉曼组织学上检测前列腺癌相对于常规病理学的准确性、敏感性和特异性。在一个独立的 113 活检测试集中评估人工智能的性能。

结果

通过受激拉曼组织学获得的前列腺活检图像可以在 2 到 2.75 分钟的时间内生成。人工智能系统能够快速分类有癌症的前列腺活检,潜在的识别时间约为 1 分钟。人工智能在检测前列腺癌方面表现出令人印象深刻的准确性,达到 96.5%。此外,人工智能还表现出 96.3%的敏感性和 96.6%的特异性。

结论

受激拉曼组织学生成的微观图像能够实时准确地识别前列腺癌,无需切片或组织处理。这些图像可以由人工智能进行解读,为医生在诊断或治疗前列腺癌期间提供近乎实时的病理反馈。

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