Aiforia

Aiforia equips pathologists and scientists in preclinical, academic, and clinical labs with the most powerful deep learning artificial intelligence and cloud-based technology to increase the speed, accuracy, and consistency of analyzing large and complex medical images across a variety of fields from oncology to neuroscience.

Aiforia is composed of an ideal mix of scientists experienced in a variety of medical areas from pathology to cancer diagnostics, software developers who collectively hold over 100 years' experience in artificial intelligence, and a business team with backgrounds in the pharmaceutical and biotechnology industries.

Applications by this vendor

Clinical AI Model for Lung Cancer: PD-L1

CE | PD-L1

Intended for use with digital images of PD-L1-stained cancerous lung tissue with NSCLC of human specimens to support the detection of tumor epithelium and scoring of PD-L1 positive and negative cells of epithelial origin from WSI and displays results of image analysis.

Clinical AI Model for Prostate Cancer: Gleason Grade Groups

CE | H&E

Intended for use with digital images of H&E-stained cancerous prostate tissue with malignant neoplasm of human prostate specimens to support the detection of tumor epithelium and assessing Gleason patterns from WSI and displays results of image analysis.

Clinical AI Model for Breast Cancer: PR

CE | PR

Intended for use with digitized images of PR stained normal and cancerous human breast tissue to support the detection of tumor epithelium and scoring of PR positive and negative cells of epithelial origin from WSI or the selected image areas.

Clinical AI Model for Breast Cancer: ER

CE | ER

Intended for use with digitized images of ER stained normal and cancerous human breast tissue to support the detection of tumor epithelium and scoring of ER positive and negative cells of epithelial origin from WSI or the selected image areas.

Clinical AI Model for Breast Cancer: Ki67

CE | KI67

Intended for use with digitized images of Ki67 stained normal and cancerous human breast tissue to support the detection of tumor epithelium and scoring of Ki67 positive and negative cells of epithelial origin from WSI or the selected image areas.