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Philips and Ibex partner to support pathology diagnostics

by John R. Fischer, Senior Reporter | April 15, 2021
Artificial Intelligence Pathology Health IT
Philips and Ibex Medical Analytics are combing their technologies to enhance and integrate more personalized care into pathologists' workflows and productivity
Royal Philips is combining the capabilities of Ibex Medical Analytics’ Galen AI-powered cancer diagnostics platform with its IntelliSite Pathology Solution to support pathologist workflow and productivity worldwide.

The two aim to help pathologists produce objective, reproducible findings and better manage increasing demand for pathology-based diagnostics brought on by a global shortage of trained pathologists and a rising number of cancer patients. They plan to do this by combining the power of imaging, pathology, genomics and longitudinal data, guided by insights derived with artificial intelligence.

"This will enable faster turnaround time for the patient by improving the workflow for pathologists, enabling precision diagnosis. Precision diagnosis will, therefore, enhance the personalized care for every individual patient," Adrianus Ermers, general manager of radiation oncology at Philips, told HCB News.

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Ibex’s Galen platform utilizes AI to support cancer detection, prioritize cases and grade accurately. It also helps diagnose multiple clinical features, including tumor size, perineural invasion and high-grade prostatic intraepithelial neoplasia, and distinguishes discrepancies between a pathologist’s diagnosis and the AI algorithm’s findings. The solution has a sensitivity rate of 98.46%, specificity of 97.33% and an AUC of 0.991.

Philips’ IntelliSite platform speeds up and simplifies access to histopathology information across cancer care and supports full-scale digitization of histology in pathology labs and lab networks. The solution is made up of an ultra-fast pathology slide scanner, and an image management system and display that manages slide scanning, image storage, case review and the sharing of patient information. It also helps streamline pathology workflows and supports multi-disciplinary teams and specialties in making complex cancer diagnosis and treatment decisions.

"By assisting the pathologist the role can change from manual counting to reviewing and assessing the AI output," said Ermers.

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