AI-powered urinary cytology analysis that identifies and ranks suspicious and atypical cells while providing quantitative cell-level insights for efficient review.

Identifies vessels and calculates microvessel density, cross-sectional area fraction, and volumetric intensity measure in colorectal cancer tissues using an in situ hybridization assay with a miR-126 probe. The assay utilizes LNA:DNA chimeric probes visualized with NBT/BCIP and is counterstained with nuclear fast red for tissue orientation and tumor area identification.

Automatically detects invasive carcinoma and quantifies ER-positive and negative tumor cells from whole-slide images (WSI) or selected tissue areas, supporting consistent and objective scoring.

Automates breast cancer grading from H&E-stained whole-slide images (WSI), accurately identifying invasive carcinoma and ductal carcinoma in situ (DCIS). It objectively scores mitotic count, tubular formation, and nuclear pleomorphism, addressing key challenges of manual grading such as variability and time constraints, consistent with the Nottingham Grading System.

Accurately identifies invasive carcinoma and scores HER2 expression in alignment with CAP guidelines, improving diagnostic reliability and workflow efficiency.

Automatically detects invasive carcinoma and quantifies Ki67-positive and negative tumor cells from whole-slide images (WSI) or targeted image regions, improving diagnostic accuracy and workflow.

Automatically detects invasive carcinoma and quantifies PR-positive and negative cells from whole-slide images (WSI) or selected tissue regions, enhancing diagnostic clarity and workflow.

Aiforia® Gastric Cancer is a CE-IVD marked AI solution assisting pathologists in identifying carcinoma areas in digitized H&E-stained gastric tissue samples. It supports diagnostic decision-making while ensuring pathologists maintain full control to review or modify AI findings before final report approval.

The H. pylori AI model supports identification of Helicobacter pylori in gastric tissue samples, where bacteria may be sparse, unevenly distributed, or difficult to detect by manual review alone. By highlighting areas of interest directly on the whole slide image, the model helps guide the pathologist's attention and supports efficient verification.

Overcome the challenges of subjective and time-consuming manual PD-L1 scoring in lung cancer – Aiforia® Lung Cancer Suite can reduce errors and help standardize scoring by providing AI-driven PD-L1 assessments that are objective and reproducible.

Digital biomarker automatically predicts the outcome of a colorectal patient’s cancer. Combined with tumor and lymph node status, the test report provides information about patients’ risk profile and 5-year cancer-specific survival to inform the decision to use adjuvant chemotherapy.

Clinical-grade solution that assists pathologists in improving the detection and grading of breast cancer.