Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial.

Accurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor, is crucial for treating with targeted therapies; however, molecular testing can delay patient care due to the time and tissue required. Successful development, validation, and deployment of an AI-based, biomarker-detection algorithm could reduce screening cost and accelerate patient recruitment. Here, we develop a deep-learning algorithm using >3000 H&E-stained whole slide images from patients with advanced urothelial cancers, optimized for high sensitivity to avoid ruling out trial-eligible patients.

Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysis.

A system for analysis of histopathology data within a pharmaceutical R&D environment has been developed with the intention of enabling interdisciplinary collaboration. State-of-the-art AI tools have been deployed as easy-to-use self-service modules within an open-source whole slide image viewing platform, so that non-data scientist users (e.g.

Prosthetic status and prosthetic need among the patients attending various dental institutes of ahmedabad and gandhinagar district, gujarat.

The oral health being an integral part for the healthy living, necessity of disability limitation and rehabilitation in oral health has taken a paramount role. To assess the prosthetic status and to evaluate the prosthetic needs of the patients attending various institutes of Ahmedabad and Gandhinagar district. A total of 510 (264 males and 246 females) subjects at various dental institutes were examined in the study.