AI & Data Providers

Enhancing image quality and algorithm performance by excluding artifact regions with automated QC

In digital pathology, high-quality images are essential not only for accurate diagnostics but also to ensure the reliability of research outcomes, (pre)clinical trials, and the performance of deep learning algorithms, all of which can be compromised by poor data. Artifacts in pathology slides can greatly hinder visual analysis and distort AI algorithms’ learning processes, resulting in less reliable outcomes. To address this challenge, data and AI providers must carefully perform a slide quality control (QC) to identify and exclude artifact regions, preserving the integrity of the data used for histology analysis or model training.

AiosynQC, our automated QC solution, substantially reduces the labor-intensive manual review time and helps your team ensure that only high-quality, artifact-free data drives pathology assessments, research, and AI development.

AI artifact detection capabilities

AiosynQC identifies and segments common artifacts encountered in H&E and IHC slides. These artifact types include out-of-focus regions, incomplete scans, white balance problems, air bubbles, tissue folds, dust, pen markers, and ink. Watch our latest webinar with Techcyte or schedule a demo to learn more.

Note: In the EU and the UK, AiosynQC is not considered a medical device under European IVDR and UK MDR 2002 legislation, respectively. AiosynQC is not intended to be used as an accessory to, nor is it necessary to be used in combination with any AI or other medical devices to specifically enable them to meet their intended purpose or directly assist in their functionality. AiosynQC is for Research Use Only (RUO) in the United States or any other jurisdiction.