Nijmegen, the Netherlands – 1 September 2026. Aiosyn has launched its new website that reflects its current focus: quantitative AI-based tissue analysis for preclinical studies and automated quality control for clinical laboratories. The new structure highlights Aiosyn’s sharpened product offering for biopharma and clinical laboratories following its 2025 funding round and strategic partnership with TNO.

Aiosyn’s work does not start with AI. It starts with tissue and the questions that tissue can answer. Is there a treatment effect on atherosclerotic lesions across this cohort? Is this kidney tissue deteriorating? Is this digital slide ready for pathologist review, or should it return to the laboratory? Aiosyn’s new website is organized around these questions, and how they can bring value to biopharma and clinical laboratories.

Preclinical studies

Histological assessment is a common endpoint in many drug-development studies. Aiosyn’s image analysis measures structures and changes across whole-slide images, returning quantitative read-outs per animal that can be traced back to the underlying tissue.

Aiosyn’s biopharma work includes new models for atherosclerotic lesions and kidney, brain and liver tissue, developed together with TNO. Combined, they form the foundation of a multi-organ platform that Aiosyn is building to cover the major organs assessed in preclinical studies.

An aortic-root cross-section analyzed by Aiosyn’s atherosclerosis model, with the vessel wall delineated in green, the atherosclerotic lesions in orange, and each lesion marked with a letter for its severity class.

Figure 1. Aiosyn’s atherosclerosis model on an aortic-root cross-section. The vessel wall is delineated in green and the atherosclerotic lesions in orange, with each lesion marked by its severity class.

Clinical laboratory workflows

For clinical laboratories, the site introduces new features for AiosynQC within the context of the full digital pathology workflow.

AiosynQC checks digitized H&E and IHC slides for common image-quality problems, including out-of-focus regions, tissue folds, incomplete scans and white-balance errors. It supports laboratory teams in deciding whether a slide is ready for pathologist review or requires rescanning or new preparation, based on the laboratory’s own acceptance criteria. Its dashboard brings QC results together, helping laboratories identify recurring issues and make data-driven improvements to their digital pathology workflow. AiosynQC integrates with the scanners, image-management systems and viewing platforms laboratories already use.

Macro image of a glass slide holding two tissue sections and a control fragment.Macro image of the glass slide
The whole-slide image of the same slide, containing only a small strip of tissue.Whole-slide image
The AiosynQC read-out on the macro image, marking missing tissue in red and the captured region in green.AiosynQC read-out

Missing from the whole-slide imageCaptured

Tissue missing
88%
Tissue captured
12%
Figure 2. AiosynQC compares the macro image of the glass slide against the whole-slide image and flags if any missing tissue was detected.

About Aiosyn

Aiosyn develops computational pathology software for preclinical studies and clinical laboratory workflows. Its solutions turn whole-slide images into quantitative tissue read-outs and automate selected steps in digital pathology workflows.