Cells undergoing division, also named mitotic figures or mitoses, are identified and highlighted with Aiosyn’s algorithm for automated mitosis detection. Credit: AIOSYN.

Aiosyn has initiated its first artificial intelligence (AI)-based mitosis detection solution to aid cancer research activities.

Called Aiosyn Mitosis Research, the deep-learning algorithm is part of the company’s mitosis product portfolio.

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It can automatically scan for cells undergoing division (mitoses) to provide mitotic figure detection to research pathology laboratories with whole slide images from cancer biopsies and resections.

Modular and flexible software is available as a service solution that can be combined with the current digital pathology software.

It can be installed via cloud or on-premise, thereby enabling the adaption to various laboratory setups.

Aiosyn Mitosis Research enables laboratories to execute a consistent and enhanced protocol for drug development studies and biomarker discovery. This improves the quality and standardisation of results.

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Aiosyn’s algorithm can highlight mitotic figures in hematoxylin and eosin slides before reviewing cases to improve the capability of mitosis evaluation, which is a crucial biomarker for examining the growth of a tumour.

The company’s latest launch comes after the introduction of an automated quality control (QC) solution that streamlines the digital pathology workflow, AiosynQC. 

Aiosyn is currently conducting clinical validation for the second algorithm of its Mitosis portfolio, Aiosyn Mitosis Breast, for diagnostic use.

Both Aiosyn Mitosis Research and Aiosyn Mitosis Breast will be part of a deep learning algorithms portfolio that is presently under development for various pathologies that require improved diagnostic precision and quality.

Aiosyn CEO Patrick de Boer said: “We are thrilled to introduce Aiosyn Mitosis Research, an AI-powered mitosis detection solution that will support cancer researchers with an innovative AI-based computational biomarker approach that improves the efficiency and consistency of results.”