Vendor source

Reviewed 25 Sept 2026

Completeness Strong (75%)

AI-Rad Companion Prostate MR (VA61)

Siemens Healthineers

AI-Rad Companion Prostate MR is an AI-powered post-processing software that supports prostate MRI interpretation and reporting. It enables automatic segmentation of the prostate gland and suspected lesions, quantitative analysis, and lesion classification from multiparametric MR images.

The software provides structured, visual results, including lesion contours and a global PI-RADS score. Outputs can be reviewed, edited, and exported to PACS (e.g., GSPS/DICOM). Results can also be exported as RTSS objects for use in TRUS-guided biopsy workflows.

Supported by 10 papers

AI-Rad Companion Prostate MR (VA61) thumbnail 1
AI-Rad Companion Prostate MR (VA61) thumbnail 2
AI-Rad Companion Prostate MR (VA61) thumbnail 3
AI-Rad Companion Prostate MR (VA61) thumbnail 4

Key Features

  • Editable AI results (size, location, score)
  • Global PI-RADS scoring
  • Lesion detection, quantification, and classification
  • Prostate gland segmentation and volumetric assessment
  • RTSS objects for TRUS-guided biopsy

General Information

Product name

AI-Rad Companion Prostate MR (VA61)

Modality

MR

Main task

Not specified

Subspeciality

Abdomen

Disease targeted

Prostate cancer

Regulatory Information

CE Certification

Pathway:

MDR

Class:

Class IIb

Verified by Health AI Register

FDA Certification

Pathway:

510(k) cleared

Class:

Class II

Verified by Health AI Register

Other certifications

Not specified

Evidence & Research

Peer-Reviewed Papers

Diagnostic performance of AI-powered prostate MRI against biopsy ground truth: quasi-continuous risk scoring versus fixed PI-RADS thresholds

Cancer Imaging · Sep 2026

Assessing inter‐observer variability in prostate and GTV segmentation on mpMRI: A comparison between radiation oncologists and AI‐based method

Journal of Applied Clinical Medical Physics · Apr 2026

MRI-based Deep Learning Algorithm for Assisting Clinically Significant Prostate Cancer Detection: A Bicenter Prospective Study

Radiology · Mar 2025

Improved prostate cancer diagnosis: upgraded prostate imaging reporting and data system (PI-RADS) scores by zoomed diffusion-weighted imaging enhance deep-learning-based computer-aided diagnosis accuracy

Quantitative Imaging in Medicine and Surgery · Mar 2025

Multi-Center Benchmarking of a Commercially Available Artificial Intelligence Algorithm for Prostate Imaging Reporting and Data System (PI-RADS) Score Assignment and Lesion Detection in Prostate MRI

Cancers · Feb 2025

Prediction of upgrade to clinically significant prostate cancer in patients under active surveillance: Performance of a fully automated AI‐algorithm for lesion detection and classification

The Prostate · Mar 2023

Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI

European Radiology · Nov 2022

A concurrent, deep learning–based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists

European Radiology · Jul 2022

Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience

European Journal of Radiology · Aug 2021

A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate

Investigative Radiology · Mar 2021

Technical Specifications

Patient population age

Not specified

Input

2D TSE axial or 3D TSE / SPACE MR image data and DWI image

Input format

DICOM

Output

Prostate gland and lesion contours, structured report with volumetrics, PSA density and classification, RTSS

Output format

DICOM, DICOM RT-STRUCT

Integration

Integration in standard reading environment (PACS), Integration via AI marketplace or distribution platform, Stand-alone webbased

Deployment

Hybrid solution

Trigger for analysis

Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload

Processing time

1 - 10 minutes

Population

Men with suspected cancer in treatment-naïve prostate glands

Market Presence

On market since

05-2020

Resellers

Not specified

Countries present

Not specified

Paying clinical customers

Not specified

Research/test users

Not specified

Pricing Information

Pricing model

Subscription

Based on

Number of analyses

Source: Vendor | First published: Jul 19, 2023 | Last updated: Sep 25, 2026