A recent study published in Nature Communications validates MSIntuit CRC, an AI-driven digital pathology diagnostic, as a reliable pre-screening tool for colorectal cancer. The diagnostic accurately rules out nearly 50% of MSS patients while correctly classifying over 96% of MSI patients.
The MOSAIC project will use cutting-edge spatial omics technologies to map cancer cells and their immune environment in high resolution. By analyzing thousands of patient samples, researchers aim to unlock novel cancer treatments and biomarkers through AI-powered analytics.
The study, published in Nature Medicine, demonstrates the first-ever use of federated learning to train deep learning models on histopathology data from multiple hospitals without compromising data privacy. This breakthrough has the potential to unlock precision medicine through secure and AI-powered medical research.
A collaborative research between Owkin and Cleveland Clinic has developed an AI model that predicts liver cancer recurrence following liver transplantation. The model outperformed traditional scoring systems and showed promise for improving patient outcomes.
Researchers developed an AI model to classify patients with localized breast cancer as high or low risk of metastatic relapse. The study shows that AI can accurately assess relapse risk with an AUC of 81%, providing a valuable aid for therapeutic decisions and avoiding unnecessary chemotherapy.
A new AI-based tool has been developed to predict genomic subtypes of pancreatic cancer using histology slides, offering a potential solution for patient molecular stratification. The tool, trained and validated on machine learning models, can be used in clinical practice worldwide.
A new machine learning model, integrated with CT scans of the lungs, surpasses benchmarks in predicting disease severity and supports hospital resource management. Additionally, Owkin develops models to discover coronavirus epitopes that may improve future vaccine efficacy.