Periodic Reporting for period 3 - OACTIVE (Advanced personalised, multi-scale computer models preventing OsteoArthritis)
Periodo di rendicontazione: 2020-11-01 al 2021-04-30
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.
Despite the outbreak of COVID19 pandemic, OACTIVE consortium managed to achieve its objectives by adopting a concrete and well designed work plan.
The major achievements during the 2nd reporting period can be summarized as follows:
1. Development of a multi-pararametric, multi-center datasbase for KOA diagnosis and prediction comprising of risk factors from different levels and data sources. The OACTIVE database will become publicly available to the scientific community and forms one of the key achievements of the project
2. Development of an integrated system for remote monitoring. Two versions available: one for monitoring in controlled environment and one for remote monitoring to be used autonomously by the OA patient
3. Development of AI models capable of diagnosis and predicting KOA parameters (KL, pain, JSN). The multi-parametric AI models, that are built on heterogeneous data sources, will also become publicly available to the scientific community.
4. Development of a gait retraining system based on AI and augmented reality that is capable to (i) enhance the knowledge base of the clinicians and serve as a DSS platform; (ii) to increase the engagement of the OA patients to the gait retraining procedure through AR gamification elements
5. New functional bioreactor batches successfully printed and assembled. Development of a functional model of a mechanoactivator device that allows the study of cartilage response to sex hormones in a more biologically relevant model of the joint.
6. The progression of the recruitment for the Clinical studies, after the initial delay, was affected by the COVID pandemic. However significant progress was made with 98 patients recruited by HULAFE, 130 patients recruited by UNIC and 110 by ANIMUS.
8. Significant progress has been achieved for OACTIVE validation activities in big data registries that are still ongoing and will be finished by the end of the project.
The major achievements during the 2nd reporting period can be summarized as follows:
1. Development of a multi-pararametric, multi-center datasbase for KOA diagnosis and prediction comprising of risk factors from different levels and data sources. The OACTIVE database will become publicly available to the scientific community and forms one of the key achievements of the project
2. Development of an integrated system for remote monitoring. Two versions available: one for monitoring in controlled environment and one for remote monitoring to be used autonomously by the OA patient
3. Development of AI models capable of diagnosis and predicting KOA parameters (KL, pain, JSN). The multi-parametric AI models, that are built on heterogeneous data sources, will also become publicly available to the scientific community.
4. Development of a gait retraining system based on AI and augmented reality that is capable to (i) enhance the knowledge base of the clinicians and serve as a DSS platform; (ii) to increase the engagement of the OA patients to the gait retraining procedure through AR gamification elements
5. New functional bioreactor batches successfully printed and assembled. Development of a functional model of a mechanoactivator device that allows the study of cartilage response to sex hormones in a more biologically relevant model of the joint.
6. The progression of the recruitment for the Clinical studies, after the initial delay, was affected by the COVID pandemic. However significant progress was made with 98 patients recruited by HULAFE, 130 patients recruited by UNIC and 110 by ANIMUS.
8. Significant progress has been achieved for OACTIVE validation activities in big data registries that are still ongoing and will be finished by the end of the project.
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
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