Aiforia

Step 1: Digitize gastric tissue samples.

Step 2: Upload digital slides to the Aiforia platform.

Step 3: Apply the Aiforia Gastric Cancer AI application.

Step 4: Review AI-detected regions of interest on the digital slide.

Step 5: Confirm or modify the AI findings based on pathological expertise.

Step 6: Generate a report with AI-supported diagnostic information.

Step 1: Stain breast tissue samples for relevant biomarkers (e.g., ER, PR, HER2).

Step 2: Scan stained slides into digital images.

Step 3: Upload images to the Aiforia platform and select the Breast Cancer Suite.

Step 4: Run AI algorithms to quantify biomarker expression and identify tumor regions.

Step 5: Review the AI-generated data and visualizations.

Step 6: Integrate AI results into the pathology report for improved diagnostic accuracy.

Step 1: Process and stain lymph node tissue samples.

Step 2: Scan the slides to create digital images.

Step 3: Upload the digital slides to Aiforia.

Step 4: Utilize the Lymph Node Metastasis AI model.

Step 5: Review the AI-identified suspicious regions for metastasis.

Step 6: Finalize the diagnosis based on AI assistance and pathological assessment.

Step 1: Prepare and stain mouse lung tissue samples.

Step 2: Digitize the slides using a scanner.

Step 3: Upload the digital slides to the Aiforia Create platform.

Step 4: Develop a custom AI model for lung tumor grading.

Step 5: Train the AI model on annotated images of lung tumors.

Step 6: Apply the trained AI model to grade new lung tumor samples.

Step 1: Obtain pre-treatment breast cancer tissue samples.

Step 2: Scan the tissue samples into digital images.

Step 3: Use Aiforia Create to develop an AI model that predicts chemotherapy response.

Step 4: Train the AI model on historical patient data with known treatment outcomes.

Step 5: Apply the AI model to predict the outcome for new patients undergoing neoadjuvant chemotherapy.

Step 6: Use the AI predictions to inform treatment decisions.