Our vision consists of revolutionizing dental and orthopedic surgery by introducing a new paradigm, model-based theranostics, which consists of an integrative coupling of therapeutics, diagnostics and numerical simulation in order to optimize the performances of the surgical protocol and to predict its clinical outcome. The success of surgical protocols involving endosseous implants is limited by i) the empirical methods employed to assess implant stability, which is a strong determinant of the surgical outcome, ii) the absence of therapeutic approaches to stimulate osseointegration phenomena and iii) the difficulty of predicting the implant outcome. The aim of UltraSimplant is to develop a radically new unified model-based theranostic concept using innovative ideas in the domain of quantitative ultrasound (QUS). The new concept will combine characterization, simulation and stimulation of osseointegration phenomena, leading to the foundation of a revolutionary approach capable of providing a decision support system to the surgeon, to improve osseointegration in a patient specific manner and to predict the surgical outcome, thus leading to a drastic decrease of the implants failure rate. We will conceive and validate (in vitro, in silico, in vivo and in a clinical trial) a minimum viable product and consisting of a medical device using QUS techniques to assess dental implant stability. A validated model of the evolution of the bone-implant system will take into account the complex multiscale nature of the interface in order to validate in silico the QUS device and to predict the effect of ultrasound stimulation and implant outcome. The model will be used in order to optimize the parameters to be employed in the stimulation. UltraSimplant will first focus on dental implants because of the important failure rate and to the easy access of the implant. In the long term, model-based theranostic approaches will be applied to other implants in orthopedic surgery.

Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease (prevalence 1:200 - 1:500), manifested by thickening of cardiac walls, increasing risks of arrhythmia, and sudden cardiac death. HCM affects all ages - it is the leading cause of death among young athletes. Comorbidities due to gene mutations include altered vascular control, and, caused by HCM, ischemia, stroke, dementia, or psychological and social difficulties. Multiple causal mutations and variations in cellular processes lead to highly diverse phenotypes and disease progression. However, HCM is still diagnosed as one single disease, leading to suboptimal care. SMASH-HCM will develop a digital-twin platform to dramatically improve HCM stratification and disease management, both for clinicians and patients. Multilevel and multiorgan dynamic biophysical and data-driven models are integrated in a three-level deep phenotyping approach designed for fast uptake into the clinical workflow. SMASH-HCM unites 8 research partners, 3 hospitals, 3 SMEs, and a global health-technology corporation in collaboration with patients to advance the state of the art in human digital-twins: including in-vitro tools, in-silico from molecular to systemic level models, structured and unstructured data analysis, explainable artificial intelligence - all integrated into a decision support solution for both healthcare professionals and patients. SMASH-HCM delivers new insights into HCM, improved patient care and guidance, validated preclinical tools, and above all, a first HCM stratification and management strategy, validated in a pilot clinical trial, and tested with end users. Thus providing a cost efficient and effective solution for this complex disease. SMASH-HCM develops a strategy towards fast regulatory approval. In reaching its goals, SMASH-HCM serves as a basis for future digital-twin platforms for other cardiac diseases integrating models and data from various scales and sources.

Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) are chronic diseases characterized by progressive or alternate impairment of neurological functions (motor, sensory, visual, cognitive). Patients have to manage alternated periods in hospital with care at home, experiencing a constant uncertainty regarding the timing of the disease acute phases and facing a considerable psychological and economic burden that also involves their caregivers. Clinicians, on the other hand, need tools able to support them in all the phases of the patient treatment, suggest personalized therapeutic decisions, indicate urgently needed interventions. Artificial Intelligence is the key to successfully satisfy these needs to: i) better describe disease mechanisms; ii) stratify patients according to their phenotype assessed all over the disease evolution; iii) predict disease progression in a probabilistic, time dependent fashion; iv) investigate the role of the environment; v) suggest interventions that can delay the progression of the disease. BRAINTEASER will integrate large clinical datasets with novel personal and environmental data collected using low-cost sensors and apps. Software and mobile apps will be designed embracing an agile and user-centred design approach, accounting for the technical, medical, psychological and societal needs of the specific users. BRAINTEASER will implement a system able to guarantee cybersecurity and data ownership to the patients; will provide quantitative evidence of benefits and effectiveness of using AI in health-care pathways implementing a proof-of-concept of its use in real clinical setting. Procedural requirements that support Software as Medical Device certification will be used involving clinicians and patients stakeholders and producing a set of recommendations for public health authorities. Results will be disseminated accordingly to an open science paradigm under the European Open Science Cloud initiative.

The overall aim of the In Silico World project is to accelerate the uptake of modelling and simulation technologies for the development and regulatory assessment of all kind of medical products. This will be achieved by supporting the trajectory of a number of In Silico Trials solutions through development, validation, regulatory approval, optimisation, and commercial exploitation. These solutions, already developed to different stages, target different medical specialities (endocrinology, orthopaedics, infectiology, neurology, oncology, cardiology), different diseases (osteoporosis, dynapenia-sarcopenia, tuberculosis, multiple sclerosis, mammary carcinoma, arterial stenosis, etc.), and different types of medical products (medicinal products, medical devices, and Advanced Therapeutic Medicinal Products). In parallel the consortium will work with a large multi-stakeholder advisory board to form a community of Practice around In Silico Trials, where academics, industry experts, regulators, clinicians, and patients can develop consensus around Good modelling Practices. As the solutions under development move toward their commercial exploitation, the ISW consortium will make available to the Community of Practice a number of resources (technologies, validation data, first in kind regulatory decisions, technical standardisation plans, good modelling practices, scalability and efficiency-improving solutions, exploitation business models, etc.) that will permanently lower barriers to adoption for any future development.

Cancer patients (2.7M in Europe) with a positive prognosis are exposed to a high incidence of secondary tumours (≈1M). Bone metastases spread to the spine in 30-70% cases, reducing the load bearing capacity of the vertebrae and triggering fracture in 30% cases. Clinicians have only two options: either operate to stabilise the spine, or leave the patient exposed to a high fracture risk. The decision is highly subjective and can either lead to unnecessary surgery, or a fracture significantly affecting the quality of life and cancer treatment. The standard-of-care to classify patients with vertebral metastasis are scoring systems based on radiographic images, with little consideration of the local biomechanics. Current scoring systems are unable to establish an indication for surgery in around 60% of cases. Thus, there is an unmet need to accurately and timely quantify the risk of fracture to improve patient stratification and identify the best personalised treatment. This interdisciplinary project will develop Artificial Intelligence (AI)- and Physiology-based (VPH) biomechanical computational models to stratify patients with spine metastasis who are at high risk of fracture and to identify the best personalised surgical treatment. After rigorous model training with clinical (2000 retrospective cases) and biomechanical (120 ex vivo specimens) data, the new approach will be tested in a multicentric prospective observational study (200 patients). The models will be combined in a decision support system (DSS) enabling clinicians to successfully stratify metastatic patients. The models and the DSS will be designed so as to be suitable for regulatory requirements and future exploitation. METASTRA will propose new guidelines for the stratification and management of metastatic patients. METASTRA approach is expected to cut the uncertain diagnoses from the current 60% down to 20% of cases. This will reduce patient suffering, and allow cutting expenditure by 2.4B€/year.