Stay Informed: Transforming Radiology with Structured Reporting and Data-Driven Approaches

Dive into our activities, projects, and product updates. Catch on the latest industry news and learn who we are as a company and as a team.

Screenshot of a prostate lesion in mint Lesion

Streamline Prostate Cancer Screenings with Prostate.Carcinoma.ai in mint Lesion

The Prostate.Carcinoma.ai plug-in, developed by our partner FUSE-AI, is a powerful addition to mint Lesion, designed specifically to enhance prostate cancer screenings. This advanced solution leverages artificial intelligence to automate and accelerate prostate MR evaluations based on the PI-RADS 2.1 guidelines, transforming the workflow and precision for radiologists.

At the click of a button, Prostate.Carcinoma.ai performs full segmentation of the prostate and its anatomical zones, and detects and segments potentially malignant lesions. mint Lesion maps each finding onto the standardized prostate lesion scheme, eliminating the need for time-consuming manual lesion annotation and drawing.

Radiologists maintain full control over the AI-generated results, with the ability to review, reject, modify, or add new findings to ensure accuracy. Based on these inputs, mint Lesion automatically calculates the PI-RADS score, delivering a comprehensive and structured report.

mint Lesion also enables longitudinal tracking of prostate lesions for MRI-based active surveillance. Radiologists can easily monitor changes in lesions over time, supporting informed decision-making for patient management and enhancing the continuity of care.

Learn more on our partner page.

Related Resources

Related Resources

Doctors looking at MRI scans to evaluate a glioblastoma.

Optimizing Glioblastoma Imaging: Enhancing MRI Efficiency and Quality with Deep Learning

This study investigates the use of deep learning (DL) to optimize MRI protocols for glioblastoma patients, aiming to reduce scan time and improve…

Three important sequences (FLAIR, T2, T1 with contrast agent) in the assessment of glioblastoma

University Hospital Tübingen: Advancing MRI Efficiency in Glioblastoma Care with Deep Learning

This study explores the use of deep learning (DL) to optimize MRI protocols for glioblastoma patients. Glioblastomas, known for being the most…

Radiologist using for medical image analysis

Advancing Real-World Federated Learning in Radiology

Federated Learning (FL) enables collaborative model training without data centralization – a crucial aspect for radiological image analysis where…