AI-Powered Prostate Cancer Diagnosis Tool Accurately Identifies and Classifies Tumors

Now, a powerful tool to detect prostatic adenocarcinoma leverages advanced machine learning algorithms to accurately identify and classify malignant and benign areas in prostate biopsies, thereby improving diagnostic accuracy and patient outcomes.

Qritive (Singapore) has launched a new AI (Artificial Intelligence) module, QAi Prostate, for prostate cancer diagnosis that uses advanced machine learning algorithms to analyze whole slide images of prostate core needle biopsies, identify prostatic adenocarcinoma region, and segment and classify them into benign and malignant areas. QAi Prostate uses whole slide images as well as regions of interest (ROI) for screening adenocarcinoma. QAi Prostate provides a detailed summary of the tumor size and percentage for each slide or ROI, allowing pathologists to quickly make more informed and accurate diagnoses, thus reducing the time to treatment for patients.

QAi Prostate has been built using a comprehensive dataset that has passed through extensive testing and validation procedures to ensure it meets the required clinical metrics and reliability standards. It is compatible with all standard digital pathology systems and can be easily integrated into existing workflows using Open API interface through a customizable middleware. It allows for secured communication using standard SSL encryption and the middleware as a single point of entry with role-based access to ensure advanced security and compliance. QAi Prostate is currently not approved by the FDA or CE marked and can be deployed for research use or as laboratory developed test only.

"We are excited to introduce this new AI module for prostate cancer diagnosis," said Dr. Kaveh Taghipour, CTO of Qritive. "QAi Prostate has been developed using millions of data points and thousands of hours of expert knowledge. This technology has the potential to improve the accuracy and speed of prostate cancer diagnosis significantly, leading to better outcomes for patients."

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