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. The algorithm is validated on a dataset of 350 patients, achieving an area under the curve of 0.75, specificity of 31.8% at 88.7% sensitivity, and projected 28.7% reduction in molecular testing. We successfully deploy the system in a non-interventional study comprising 89 global study clinical sites and demonstrate its potential to prioritize/deprioritize molecular testing resources and provide substantial cost savings in the drug development and clinical settings.

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., clinicians) can utilize and evaluate pre-trained algorithms and retrieve quantitative results. The outputs of analysis are automatically cataloged in the database to track data provenance and can be viewed interactively on the slide as annotations or heatmaps. Commonly used models for analysis of whole slide images including segmentation, extraction of hand-engineered features for segmented regions, and slide-level classification using multi-instance learning are included and new models can be added as needed. The source code that supports running inference with these models internally is backed up by a robust CI/CD pipeline to ensure model versioning, robust testing, and seamless deployment of the latest models. Examples of the use of this system in a pharmaceutical development workflow include glomeruli segmentation, enumeration of podocyte count from WT-1 immuno-histochemistry, measurement of beta-1 integrin target engagement from immunofluorescence, digital glomerular phenotyping from periodic acid-Schiff histology, PD-L1 score prediction using multi-instance learning, and the deployment of the open-source Segment Anything model to speed up annotation.

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. A survey proforma was prepared with the help of WHO oral health assessment form (1997). Prosthetic status and prosthetic treatment need was recorded. Out of 510, any type of Edentulousness was 322 (63 %). Among them, 254 (49.8 %) were partially edentulous while 68 (13.3 %) were completely edentulous. Only 69 (13 %) were having any prosthesis in upper arch while only 80 (16 %) were having any prosthesis in lower arch. Need for any type of prosthesis in upper and lower arch was 55 and 60 % in males and females, respectively. In lower social class group need of prosthesis in upper and lower arch was 62 and 63 %, respectively. It was found that prosthetic status and prosthetic treatment need increased with increase in age. Steps should be taken to overcome this disparity and more emphasis should be given to meet the felt need of the people through government and non government organizations to improve the oral health. The unmet prosthetic treatment need should be met to rehabilitate needy people so that their disability may be limited.