Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - OACTIVE (Advanced personalised, multi-scale computer models preventing OsteoArthritis)

Teaser

The current OACTIVE project intents to make a significant leap forward adopting a multi-scale holistic analysis where patient-specific information from various levels, including molecular, cell, tissue and whole body, will be integrated and combined with information from other...

Summary

The current OACTIVE project intents to make a significant leap forward adopting a multi-scale holistic analysis where patient-specific information from various levels, including molecular, cell, tissue and whole body, will be integrated and combined with information from other sources such as, environmental, behavioural and social risk factors to generate robust predictors for new personalised interventions for delaying onset and/or slowing down progression of osteoarthritis (OA). OACTIVE targets patient-specific OA prediction and interventions by using a combination of mechanistic computational models, simulations and big data analytics. Once constructed, these models will be used to simulate and predict optimal treatments, better diagnostics, and improved patient outcomes. Overcoming the limitation of the current treatment interventions, Augmented Reality (AR) empowered interventions will be developed in a personalised framework allowing patients to experience the treatment as more enjoyable, resulting in greater motivation, engagement, and training adherence. The AR element will also be helpful for the therapists for validating the patients’ progress and allow them more adaptive rehabilitation therapy in terms of flexible interactive content. OACTIVE’s mission is to improve healthcare by transforming and accelerating the OA diagnosis and prediction based on a more comprehensive and holistic understanding of disease pathophysiology, dynamics, and patient outcomes.

Work performed

The specific objectives and the progress performed to meet those objectives are summarized below:
- Mechanistic modelling framework of the musculoskeletal system: Examinations were carried out to investigate the best choice of a generic model and methods of modelling the knee joint. Development of the Finite Element (FE) model focused on evaluating the best methods for segmentation, response of cartilage models to varying loading and determining appropriate boundary conditions. Knee joint kinetics and kinematics were evaluated from 2 subjects, one from KUL and one from ANIMUS, and FE analyses were used to evaluate cartilage stresses during gait. Tools were developed to allow the plotting of key output variables such that they can be compared to other subjects.
- Systemic health and inflammation modelling framework including the quantification of already known serum biomarkers together with the discovery of useful new biomarkers coming from the study of the content of exosomes from blood, urine and faecal samples. To carry out this approach, each clinical partner had to submit their own clinical studies to the Ethical Committee and elaborate a data collection protocol. At this time, all clinical partners involved (UNIC, HULAFE and ANIMUS) have their respective bioethical approvals and are actively recruiting patients and collect samples for biomarker analysis.
- Behavioural, social, environmental modelling framework: A preliminary overview of the performances of IMUs systems available off the shelf has been investigated. The IMUs platforms have been implemented starting from a Smartex hardware platform. A framework to define the socioeconomic and environmental factors was developed through a systematic literature review which is ongoing. A protocol compliant to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was written to file at the PROSPERO database and a protocol publication is anticipated.
- Hypermodelling framework empowered by big data and machine learning: Work has been performed towards designing and developing the infrastructures needed for effective data management of information. Data integration techniques were considered in order to combine data from multiple sources into a coherent data store. The preliminary part of the data pre-processing and data reduction techniques regarding the MR images has been performed. The framework for knowledge discovery employing data mining was built but is still under refinement. A deep learning approach was proposed to enable automated, non-invasive identification of risk factors from self-reported clinical data about joint symptoms, disability, function and general health. The results suggest that deep learning may facilitate clinical evaluation, monitoring and even prediction of knee osteoarthritis.
- Personalised interventions using Augmented Reality: The software and hardware requirements related to the personalized intervention through augmented reality system were established. The architecture of the gait retraining, and game development system have been defined and implemented. The system was successfully tested at the ANIMUS center in a collaboration between UPAT and CERTH. Finally, the consortium is working on the development of a prototype real-time gait retraining system, based on IMU sensors that can be used for an outdoor environment.
- Validation: Multiple in vitro clinical trials were performed on human osteochondral plugs from waste tissues of patients undergoing joint replacement using an experimental approach that selects and distinguishes both macroscopically healthy and diseased tissue. We are also carrying out the ELISA assays of the efflux media to evaluate the level of selected biochemical markers linked to osteoarthritic environment and will be associating those with the tissue-specific gene expression. Furthermore, we developed an in vitro osteoarthritic model based on native porcine tissue as pro-inflammatory

Final results

The OACTIVE project is expected to offer the medical care sector a solution that will predict, delay the onset and slow down the progression of OA offering patients an increased quality of life. The adoption of the novel patient-specific predictive computer-based models by the health care community is expected to create a multidimensional impact on European economy, society and healthcare industry addressing in parallel EU priorities, as creating forefront knowledge, supporting job growth and competitiveness and improving EU citizens’ quality of life.
Expected impacts:
a.Benefit for health and well-being: new personalised interventions for increasing resilience and recovery.
b.Advancements in medical computer-modelling and simulation that takes into account time scale.
c.Supporting predictive and preventive approaches in medicine, neurosciences and life sciences.
d.Improving knowledge about well-being and association with life circumstances.
e.Direct savings for the Health system and indirect savings for the EU economy by reduction of work-related losses due to sick-leave days and home-care costs
f.Improving the innovation capacity and the integration of new knowledge

Website & more info

More info: https://www.oactive.eu/.