PROView

PROView is an MR image analysis software designed to assist clinicians by providing a guided workflow for reviewing multi-parametric prostate MR images, utilizing a deep learning algorithm for automatic prostate segmentation and PI-RADS v2.1 compliant lesion assessment. The system aims to enhance the analysis of prostate volume and lesions, generating standardized reports to support diagnostic and treatment planning processes for patients with suspected prostate conditions.

PROView is a medical diagnostic software intended to assist clinicians, radiologists, and physicians in the review and analysis of multi-parametric prostate magnetic resonance (MR) images. Its primary purpose is to support the assessment of prostate gland volume and the analysis of findings in patients with known or suspected prostate lesions, adhering to PI-RADS guidelines. The software facilitates the communication of clinically relevant information, including measurements and associated scoring, to referring physicians through a comprehensive report, thereby aiding in diagnosis and treatment planning. The PROView system offers a guided workflow for processing, analyzing, and reviewing 3D reconstructed images from MR scanning devices, focusing on multi-parametric prostate exams from a single date. Key features include automatic prostate volume calculation using a deep learning algorithm for organ segmentation, with an option for manual adjustment if the automatic segmentation is unsatisfactory. It also provides tools for PSA Density calculation, mapping and measurement of lesions, and scoring of T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) acquisitions. All analyses and measurements are compiled into an automatically generated report compliant with PI-RADS™ v2.1 guidelines.

AI Regulatory Concerns

Powered by

Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. How does the performance of PROView's deep learning algorithm for automatic prostate segmentation compare to manual segmentation by experienced radiologists in terms of accuracy, inter-observer variability, and time efficiency across diverse patient cohorts?; What is the range of accuracy for the automatic prostate volume calculation, and how does this variability impact the calculated PSA Density and subsequent clinical interpretations based on PI-RADS v2.1?; Are there specific MR image characteristics (e.g., field strength, sequence parameters, presence of artifacts) that significantly affect the reliability of the automatic segmentation and lesion scoring features of PROView?; How does PROView address challenging cases, such as post-treatment changes, large or irregularly shaped glands, or equivocal lesions, within its PI-RADS v2.1 framework?; What validation studies have been conducted to demonstrate the clinical utility of PROView in improving diagnostic accuracy or inter-reader agreement for prostate lesion assessment compared to conventional reading without the software?