Digital Twins in Pathology

A Digital Twin is an intricately detailed digital model that acts as a real-time counterpart to a physical object, process, or system. This virtual duplicate is dynamic, evolving alongside its physical peer, enriched continuously with new live data. The other data sources can come from various sources: sensors, videos, satellites, files, etc. In any format or any mode, even in real-time.

A digital twin recreates a reliable “other self” of a sample or a process, to replicate, prognosticate, and manipulate aspects of the physical domain, facilitating deeper comprehension and improved decision-making processes.

What is the purpose of Digital Twins?

The essence of a Digital Twin lies in creating an exhaustive and precise representation of a physical entity within a digital framework. This enables detailed analysis, experimentation, and observation without limitations or expenses involved in altering the physical entity itself. Digital Twins enable enhanced prediction accuracy, elevated operational efficiency, and innovation much faster than previously thought possible. It offers a significant reduction in costs and environmental footprints by optimizing processes and minimizing the need for physical prototypes much faster than any other analysis. The ability to visualize the future gives us the power to change it, speeding the identification of diseases and efficacy of various treatments. Especially for cancers and other diseases.

What results are the companies using digital twins looking for?  

According to recent McKinsey research, “increasing efficiency, reducing costs and building effective treatments and products. Digital twins speed up product and process development, optimizing performance and enhancing predictive maintenance”.

In the opinion of industry guru’s:
  • Digital twins provide a risk-free product development environment, allowing design and engineering teams to explore more options without the cost associated with producing and testing physical prototypes.
  • Digital twins improve testing and validation, allowing new solutions to be evaluated quicker in a wide range of realistic scenarios, including new and unusual conditions.
  • Digital twins provide deeper insights into product or treatment behavior. Knowledge Engineers and SME’s use digital twin models to monitor the state of any part of a process at any time and track complex interactions between elements.
  • Digital twins allow real data to drive product improvements, simulating the impact of proposed treatment changes using data collected from products operating in the field.

Representative Case Studies:

  • Roseaid’s strategic partner RYLTI is currently collaborating with leading SMEs and organizations within the life sciences sector to enhance the potential of drug discovery and precision therapies for any disease. Distinguished geneticists, such as Dr. William G. Kearns, Co-founder/CEO of molecular genetic laboratory Genzeva and LumaGene, and esteemed academic institutions have recently produced insights that were previously unattainable.
  • In a recent endometriosis study, Genzeva, RYLTI, Harvard Medical School, and QIAGEN collaborated to explore a breakthrough process, employing whole-genome or clinical exome sequencing, a phenotype-driven variant analysis, and the RKE Platform. The digital twin ecosystem was created by uploading all patient metadata, medical history, pathology reports, and transcriptomics. The dynamically adaptive platform used Real-World Methods and simulations, assigning varied rules to the data, recalculating and recontextualizing results. DNA variants were identified in nearly all patient samples, but not in matched controls in four genes classified as variants of unknown clinical significance (VUS).
The findings demonstrate how artificial intelligence and advanced genomics, alongside RYLTI’s knowledge engineering platform and its biomimetic digital twin ecosystem, can precisely delineate the molecular mechanism of diseases. This approach is applicable across various disease types, predicting severity, conducting virtual clinical trials, and facilitating the swift identification of new effective therapies for treatment.
As part of this initiative, ROSEAId is constructing the largest library of use cases supporting data and diagnostic information for pathologists, as well as the thousands of healthcare professionals seeking enhanced disease detection, treatments and cures.

About ROSAId

ROSEAId is a leading provider of AI-powered solutions for pathologists, scientists, and healthcare professionals for cancer detection and treatment efficacy. Our platforms enable real-time, accurate quantitative evaluation of tissues using advanced artificial intelligence (AI) algorithms and knowledge engineering supporting remote analysis, collaboration, and streamlined processes for better outcomes. Our Multi-Modal Large Language Foundational Model (MLLFM) transforms the way data is analyzed and simulated for faster and more profound investigation into the intricacies of biological complexities and associations.