IBEX Galen™ 3.0

Ibex Announces Galen™ 3.0 AI Powered Cancer Diagnostics Platform.

Latest version of Galen™ transforms diagnosis of prostate, breast and gastric cancer and includes expanded detection, user-interface, and workflow integration capabilities.

TEL AVIV, Israel, Sept. 4, 2022 – Ibex Medical Analytics, the leader in AI powered cancer diagnostics, today announced the launch and roll-out of Galen™ 3.0, a transformative solution offering new detection capabilities and a broad set of features to support pathologists in the diagnosis of multiple tissue types across various digital pathology workflows. Galen 3.0 is CE-Marked, approved in additional countries and now generally available to Ibex customers.

Creating a new modality for cancer diagnosis, Galen is the first and most widely deployed AI technology in pathology and used in routine clinical practice at laboratories, hospitals, and health systems worldwide. Galen supports pathologists across numerous diagnostic tasks during the review of breast, prostate, and gastric biopsies and helps improve the quality of cancer diagnosis, reduce turnaround time, boost productivity and improve user experience for pathologists. Galen demonstrated outstanding outcomes across clinical studies performed in multiple pathology labs and diagnostic workflows.

Galen 3.0 incorporates the very latest evolution of Ibex’s AI algorithms for detecting cancer and other clinically relevant features in prostate, breast, and gastric biopsies. To ensure very high accuracy and generalizability, Ibex trained the Deep Learning networks on huge, enriched data sets from laboratories worldwide that were digitized by multiple scanning systems, including rare prostatic malignancies such as intraductal carcinoma, neuroendocrine tumor, colorectal adenocarcinoma, lymphoma, and urothelial carcinoma. Galen also calculates a Gleason score, tumor size and percentage for each cancer slide, potentially enabling pathologists to save review time and reduce subjectivity.

IBEX Galen™ 3.0 AI Powered Diagnostics
IBEX Galen™ 3.0 AI Powered Diagnostics

“With an estimated 1.9 million new cancer cases diagnosed in the United States alone this year, we are excited to bring Galen 3.0 to pathology labs worldwide, providing clinically validated, automated decision-support tools that help pathologists diagnose cancer more rapidly and more accurately to support the high demand,” said Issar Yazbin, Vice President of Product Management at Ibex. “Keeping our customers’ needs central to our research and development, we are proud to deploy Galen 3.0, bringing enhanced detection capabilities, improved user experience, increased interoperability tools and ease of implementation into existing clinical workflows.”

Galen 3.0 features an open API (Application Programming Interface) accelerating interoperability and seamless integration with image management solutions, lab information systems and digital pathology workflow solutions. The Ibex API is already used in multiple collaborations between Ibex and leading digital pathology partners where Ibex’s AI findings are seamlessly integrated to the partners’ solutions. Version 3.0 also includes new customizable reporting modules, enabling every customer site to tailor the slide and case reports according to their own needs.

Ibex Medical Analytics presents at the European Congress of Pathology which takes place in Basel, Switzerland, between September 3-7 (booth no. 1).

Ibex is transforming cancer diagnostics with world-leading, clinical grade AI-powered solutions, empowering physicians to provide accurate, timely and personalized cancer diagnosis for every patient.

Galen™ suite of solutions is the first and most widely deployed AI-technology in pathology and used as part of everyday routine, supporting pathologists and providers worldwide in improving the quality and accuracy of diagnosis, implementing comprehensive quality control, reducing turnaround times and boosting productivity with more efficient workflows. Ibex’s Artificial Intelligence technology is built on Deep Learning algorithms trained by a team of pathologists, data scientists and software engineers.