Find the artificial intelligence based software for pathology that you are looking for. All products listed are available for the European market (CE-IVD marked).
The data presented in the pathology domain is under a publication embargo until May 2026 and should not be used for publication purposes until the embargo period has ended.
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Subspeciality: Breast pathology
Modality: Histopathology
Source: Public
No key features available
Virasoft ER Nuclear Analysis automates the identification of ER-positive cells, providing scorring for cancer screening in breast tissue samples.
Subspeciality: Dermatopathology
Modality: Histopathology
Source: Public
No key features available
CS-MITOMELANOMA identifies and quantifies cells in mitosis in malignant cutaneous melanocytic tumors (melanoma), ensuring an accurate cancer assessment in skin tissue samples.
Subspeciality: Gastrointestinal pathology
Modality: Histopathology
Source: Public
No key features available
CS-PHH3 algorithm identifies and quantifies cells in the mitotic phase in malignant tumors using Phosphohistone 3 (PHH3) and hematoxylin-eosin (H&E) staining.
Subspeciality: Uropathology
Modality: Histopathology
Source: Public
No key features available
CS-Deferens algorithm identifies epithelial cells in tissue samples from the vas deferens obtained from vasectomies.
Subspeciality: Uropathology
Modality: Histopathology
Source: Public
No key features available
The CS-CAPROST algorithm automatically locates areas of malignant tumor in prostate tissue samples, providing a preliminary diagnosis. It is intended for cancer screening in prostate tissue samples.
Subspeciality: Pulmonary pathology
Modality: Histopathology
Source: Public
No key features available
The CS-PDL1 algorithm is intended for cancer screening in lung tissue samples: - Detection of cells positive for the PDL1 antibody. - Assists in diagnosing pathological conditions related to the presence of the PDL1 antibody. - Provides specific information about a physiological or pathological state. - A quantitative automated algorithm that integrates Artificial Intelligence. - Uses lung tissue samples from patients of any gender and age. Therefore, the purpose of the algorithm is to provide the pathologist or healthcare professional using it with an automatic identification of findings, facilitating the interpretation of areas with cancer on an image of a processed lung tissue sample. This is achieved by highlighting potential areas of interest so that the pathologist can make a faster and safer diagnosis, less influenced by inter-observer variability since it is standardized. The diagnosis provided by the algorithm is not definitive: it must always be validated by the pathologist, who confirms the accuracy of the generated information.
Subspeciality: Gastrointestinal pathology
Modality: Histopathology
Source: Public
No key features available
Identifies and classifies polyps in colon tissue samples, distinguishing between benign tumor types and lesions with dysplasia or adenocarcinoma.
Subspeciality: Gastrointestinal pathology
Modality: Histopathology
Source: Public
No key features available
CS-META identifies intestinal metaplasia in esophageal tissue samples, with and without dysplasia, to support cancer diagnosis.
Subspeciality: Breast pathology
Modality: Histopathology
Source: Public
No key features available
Automatically detects and quantifies HER2-positive cells, delivering precise and consistent results for cancer screening in breast tissue samples
Subspeciality: Breast pathology
Modality: Histopathology
Source: Public
No key features available
CS-BC-PR automatically detects and quantifies ER-positive cells, increasing screening accuracy in breast tissue samples.
Subspeciality: Breast pathology
Modality: Histopathology
Source: Public
No key features available
CS-BC-ER detects and automatically quantifies ER-positive cells, increasing accuracy in breast cancer screening in breast tissue samples.
Subspeciality: Breast pathology
Modality: Histopathology
Source: Public
No key features available
CS-BC-Ki67 automates the identification of Ki67-positive cells, providing accurate and efficient counting for cancer screening in breast tissue samples.
