Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States.
Research Institute for Future Medicine, Samsung Medical Center, Republic of Korea.
Eur J Radiol. 2023 Aug;165:110887. doi: 10.1016/j.ejrad.2023.110887. Epub 2023 May 23.
Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities. While efforts have been made to standardize image acquisition and interpretation via the development of systems, such as PI-RADS and PI-QUAL, the scoring systems still depend on the subjective experience and acumen of humans. Artificial intelligence (AI) has been increasingly used in many applications, including medical imaging, due to its ability to automate tasks and lower human error rates. These advantages have the potential to standardize the tasks of image interpretation and quality control of prostate MRI. Despite its potential, thorough validation is required before the implementation of AI in clinical practice. In this article, we explore the opportunities and challenges of AI, with a focus on the interpretation and quality of prostate MRI.
前列腺 MRI 在前列腺成像和周围组织成像方面发挥着重要作用,特别是在前列腺癌的诊断和管理方面。近年来,随着多参数磁共振成像的广泛应用,人们越来越关注成像质量的可变性。一些因素导致图像质量的不一致,如采集参数、扫描仪差异和观察者间的差异。虽然已经通过开发 PI-RADS 和 PI-QUAL 等系统来努力标准化图像采集和解释,但评分系统仍然依赖于人类的主观经验和敏锐度。人工智能 (AI) 因其能够自动化任务和降低人为错误率,已在许多应用中得到越来越多的应用,包括医学成像。这些优势有可能使前列腺 MRI 的图像解释和质量控制任务标准化。尽管有这些优势,但在 AI 应用于临床实践之前,需要进行彻底的验证。在本文中,我们探讨了 AI 的机遇和挑战,重点关注前列腺 MRI 的解释和质量。