Vendor source

Reviewed 15 Jul 2026

Completeness Strong (75%)

Prostate MR on syngo.via

Siemens Healthineers

Currently only available with Siemens Healthineers' syngo.via Siemens Healthineers Prostate MR on syngo.via supports prostate MRI reading and reporting. By deploying artificial intelligence, Prostate MR assists in the interpretation of multiparametric Prostate MRI. The deep neural networks of Prostate MR are designed to aid the detection and classification of prostate lesions and generate a pre-populated report. The results can be exported as a RTSS object which is then used in TRUS-guided biopsy by the urologist. The product is not commercially available in some countries, e.g. the US. Due to regulatory reasons the future availability cannot be guaranteed. Please contact your local Siemens Healthineers organization for further details.

Supported by 8 papers

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Key Features

  • Exporting results in rtss format
  • Lesion candidate localization and classification
  • Pre-populated pi-rads report
  • Prostate gland segmentation and volumetric assessment
  • Suspicion map highlighting the lesion candidates

General Information

Product name

Prostate MR on syngo.via

Modality

MR

Main task

Not specified

Subspeciality

Abdomen

Disease targeted

Prostate cancer

Regulatory Information

CE Certification

Pathway:

MDD

Class:

Class IIa

Verified by Health AI Register

Other certifications

Not specified

Evidence & Research

Peer-Reviewed Papers

AI-based prostate volume on MRI in screening of 50-year-old men and its impact on PSA density–based biopsy decisions

European Journal of Radiology Artificial Intelligence · 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

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

Transversal T2-weighted image, low and high b-value DWI

Input format

DICOM

Output

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

Output format

DICOM, DICOM RT-STRUCT

Integration

Integration in Advanced Visualization and Post-Processing Platform

Deployment

Locally on dedicated hardware, Locally virtualized (virtual machine, Docker)

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

< 10 seconds

Population

Men with suspected cancer in treatment-naïve prostate glands

Market Presence

On market since

08-2020

Resellers

Not specified

Countries present

Not specified

Paying clinical customers

>10

Research/test users

>10

Pricing Information

Pricing model

Subscription

Based on

Number of analyses

Source: Vendor | First published: Jul 19, 2023 | Last updated: Jul 15, 2026