计算机视觉和病理学的进步:发掘人工智能在精准诊断及其他方面的潜力。

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-06-26 DOI:10.1016/bs.acr.2024.05.006
Justin Chang, Bryce Hatfield
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引用次数: 0

摘要

通过幻灯片数字化将计算机视觉技术整合到病理学中,是该领域发展过程中的一次变革性飞跃。传统的病理学方法虽然可靠,但往往费时费力,而且容易受到观察者内部和观察者之间差异的影响。相比之下,计算机视觉在人工智能(AI)和机器学习(ML)的赋能下,有望带来革命性的变化,以不断提高的速度和可扩展性提供一致、可重复和客观的结果。CNN 和 U-Nets 等先进算法和深度学习架构的应用增强了病理学家的诊断能力,开辟了自动图像分析的新领域。随着这些技术的成熟和与数字病理工作流程的整合,它们将为深入了解疾病过程、量化和标准化生物标志物、提高患者预后、自动化常规任务以及减少病理学家的工作量提供有力支持。然而,这种变革力量需要病理学家、计算机科学家和行业创新者之间的跨学科合作,以推动研究与开发。本章在肯定人工智能潜力的同时,也探讨了人工智能在病理学中的局限性,包括开发和实施过程中的技术、实践和伦理方面的考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond.

The integration of computer vision into pathology through slide digitalization represents a transformative leap in the field's evolution. Traditional pathology methods, while reliable, are often time-consuming and susceptible to intra- and interobserver variability. In contrast, computer vision, empowered by artificial intelligence (AI) and machine learning (ML), promises revolutionary changes, offering consistent, reproducible, and objective results with ever-increasing speed and scalability. The applications of advanced algorithms and deep learning architectures like CNNs and U-Nets augment pathologists' diagnostic capabilities, opening new frontiers in automated image analysis. As these technologies mature and integrate into digital pathology workflows, they are poised to provide deeper insights into disease processes, quantify and standardize biomarkers, enhance patient outcomes, and automate routine tasks, reducing pathologists' workload. However, this transformative force calls for cross-disciplinary collaboration between pathologists, computer scientists, and industry innovators to drive research and development. While acknowledging its potential, this chapter addresses the limitations of AI in pathology, encompassing technical, practical, and ethical considerations during development and implementation.

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