Quality Control

Quality Control Check with Artificial Intelligence

Check the quality of your biospecimens before you buy, sell, or consume them

Image QC

Check the quality of your image using PathQA Sierra scanner-agnostic AI validation using high-fidelity ICC profiles.

The measure of a digital pathology scanner’s colour reproducibility.

The Sierra slide carries 55 biopolymer patches, which mimic the spectral response of human tissue when stained for histopathology.

The spectral absorption of commonly used pathology stains is then measured using a traceable calibration spectrophotometer.

Tissue QC

Check the content of your biospecimens using artificial intelligence.

Trained and tested by Board Certified Pathologists, Quality Control using computer-assisted pathology AI adds a layer of precision based on detailed cellular structures and tumor nuclei.

Accuracy approaching 99%, results on:

Tumor Area (%)
Necrosis (%)
Tissue Area mm^2
Tumor Nuclei (#)
Tumor Nuclei (%)

Visually identified heatmaps show where the:

Highlights Tumor Concentration

Outlines Tumor vs. Necrosis vs. Normal

1. Xie R, Chung J-Y, Ylaya K, et al. Factors Influencing the Degradation of Archival Formalin-F

Fixation QC

Check the fixation quality of your tissue block.

Diagen is working in conjunction with some of the brightest minds on a digital biomarker panel to assess the quality of fixation in FFPE tissue blocks.

Coming soon...

Identifying Morphologic, Histopathologic, and Pathologic Fea

The associated patent (US20220108442A1 filing) was invented and assigned to TriMetis Life Sciences.

Abstract

A system and method for use in a standardized laboratory for a specimen including a staining specific for a marker in the specimen. The method includes scanning an image, having an image magnification, of the specimen; and detecting morphologic, histopathologic and pathologic (MHP) features in the image, where the app includes a neural network (NN) trained by (a) importing into the NN, control images and associated annotations, where each of the associated annotations identifies one of the MHP features, (b) analyzing a test image with the NN to generate testing annotations for portions of the test image, (c) assessing whether the testing annotations are satisfactory, (d) enhancing the NN when the testing annotations made by the NN are unsatisfactory by repeating the importing, the analyzing and the assessing, and (e) creating the app including the NN when the testing annotations made by the NN are satisfactory.

US PTO: 20220108442

It's like having dedicated pathologists on demand.

Instantly available and supremely scalable,

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