The incidence of urothelial carcinoma continues to rise annually, particularly among the elderly. Prompt diagnosis and treatment can significantly enhance patient survival and quality of life. Urine cytology remains a widely-used early screening method for urothelial carcinoma, but it still has limitations including sensitivity, labor-intensive procedures, and elevated cost. In recent developments, microfluidic chip technology offers an effective and efficient approach for clinical urine specimen analysis. Digital holographic microscopy, a form of quantitative phase imaging technology, captures extensive data on the refractive index and thickness of cells. The combination of microfluidic chips and digital holographic microscopy facilitates high-throughput imaging of live cells without staining. In this study, digital holographic flow cytometry was employed to rapidly capture images of diverse cell types present in urine and to reconstruct high-precision quantitative phase images for each cell type. Then, various machine learning algorithms and deep learning models were applied to categorize these cell images, and remarkable accuracy in cancer cell identification was achieved. This research suggests that the integration of digital holographic flow cytometry with artificial intelligence algorithms offers a promising, precise, and convenient approach for early screening of urothelial carcinoma.

中文翻译：

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通过机器学习和深度学习方法，基于数字全息流式细胞术筛查尿液中的尿路上皮癌细胞

尿路上皮癌的发病率每年持续上升，特别是在老年人中。及时诊断和治疗可以显着提高患者的生存率和生活质量。尿细胞学仍然是尿路上皮癌广泛使用的早期筛查方法，但它仍然存在局限性，包括敏感性、劳动密集型操作和成本高昂。近年来的发展，微流控芯片技术为临床尿液样本分析提供了一种有效且高效的方法。数字全息显微镜是一种定量相位成像技术，可捕获有关细胞折射率和厚度的大量数据。微流控芯片和数字全息显微镜的结合有助于在不染色的情况下对活细胞进行高通量成像。在这项研究中，采用数字全息流式细胞术快速捕获尿液中存在的多种细胞类型的图像，并重建每种细胞类型的高精度定量相位图像。然后，应用各种机器学习算法和深度学习模型对这些细胞图像进行分类，在癌细胞识别方面取得了显着的准确性。这项研究表明，数字全息流式细胞术与人工智能算法的集成为尿路上皮癌的早期筛查提供了一种有前途、精确且方便的方法。