Editorial for “Deep‐Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi‐Parametric MRI”
Abstract
Editorial for “Deep-Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi-Parametric MRI” Prostate magnetic resonance imaging (MRI) is commonly used in clinical management of localized prostate cancer (PCa), mainly to assist biopsy decisions and guidance in screening positive patients. Despite its documented success in major clinical trials, prostate MRI has a large performance variation, which is associated with a multistep quality chain including image acquisition, image interpretation, image processing, biopsy procedure, and histopathology interpretation. Success in each of these steps of this fragile workflow is heavily dependent on human performance and expertise. Specifically for image interpretation, although well-established guidelines exist, the learning curve is quite steep and despite all efforts, inter and intra-reader variation is still a challenge. Artificial intelligence (AI) impacts almost every aspect of our lives including medicine. Particularly for radiology, AI has been an important research area since radiology imaging studies can be a big data source for computer vision-related AI model development efforts. A cross sectional imaging study of a patient often include hundreds and even sometimes thousands of images with exquisite spatial resolution and detail for which clinical radiologists are responsible for evaluation and diagnosis. Due to their heavy workload, it is very likely for radiologists to…
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