AI-assisted Continuous Stratification in Neurorehabilitation of Stroke Using Personalized Digital Twins

Outline of research This project is designed to assess the feasibility of the STRATIF-AI rehabilitation application (app), alongside its accessibility, usefulness and personalisation to patients. The purpose of the app is to support patients in their recovery from stroke, however, since it is at an early stage of development, the main aims for this study are testing the feasibility of having such an app involved in a patient's recovery and if patients find it useful.

State-of-the-art stratification technology today is based on machine learning (ML) algorithms, trained on large cohort data. This has two main limitations: a) such ML models cannot use all the variety of different data that are generated about a patient, b) stratification is thus only done intermittently, implying outdated and suboptimal care decisions. To remedy this, a new concept and technology - continuous stratification, using the STRATIF-AI platform was developed. In continuous stratification, all data generated about a patient are cumulatively stored in a Personal Data Vault, controlled by the patient. This personal data continuously updates the digital twin platform. The unique potential with the platform comes from the hybrid architecture, combining mechanistic, multiscale, and multi-organ models with ML and bioinformatics.

This technology to allows simulation of patient-specific responses to changes in treatment, and identify changes on intracellular, organ, and whole-body levels, ranging from seconds to years for patients following a stroke. Semantic harmonisation is combined with federated learning to securely re-train the various sub-models when new data become available in one of the cohort databases. This project will explore the use of digital twins in the rehabilitation phase of recovery following stroke and will be carried out in the rehabilitation service at Chapel Allerton Hospital in Leeds. It is one of six simultaneous studies involving eight hospitals across Europe with the aim to refine and validate the models and demonstrate how digital twins can follow patients across different apps, covering all phases of stroke: from prevention to acute treatment and rehabilitation. The scalable platform for continuous stratification forms the foundation for a new interconnected and patient-centric healthcare system.

This project aims to evaluate the feasibility of utilising a novel app to develop a continuous stratification method, using all available data for real-time patient assessment by consolidating patient data into an evolving digital twin. It is hypothesized that the use of this technology compared to standard post-stroke rehabilitation care will improve patient engagement with post-stroke rehabilitation care and create opportunities for future work in the use of digital twins in healthcare.

Background and Rationale Motivation to engage in a rehabilitation programme and concordance with the rehabilitation programme are two major limitations to people's recovery following stroke and which can lead to suboptimal outcomes. A related, and central, problem in all phases of stroke care is that data lie in silos, patients are not provided with their data, and stratification of treatment is made only intermittently. For these reasons, the STRATIF-AI platform, which is based on digital twins - a digital copy of a patient - which facilitates continuous stratification, within and across all phases of stroke care was created. Different studies run by partner hospitals across the consortium will focus on the earlier stages of stroke care such as preventative measures and the acute monitoring of patients. In these studies, data will be collected to allow for the continuous models to be developed. In this clinical study, how patients and medical staff experience the usage of the new platform in rehabilitation will be studied. This is a pilot study, which will examine proof-of-principle, and collect initial feasibility and acceptability data.