Adult Spine Deformity (ASD) is a degenerative condition of the adult spine leading to altered spine curvatures and mechanical balance. Computational approaches, like Finite Element (FE) Models have been proposed to explore the etiology or the treatment of ASD, through biomechanical simulations. However, while the personalization of the models is a cornerstone, personalized FE models are cumbersome to generate. To cover this need, we share a virtual cohort of 16807 thoracolumbar spine FE models with different spine morphologies, presented in an online user-interface platform (SpineView). To generate these models, EOS images are used, and 3D surface spine models are reconstructed. Then, a Statistical Shape Model (SSM), is built, to further adapt a FE structured mesh template for both the bone and the soft tissues of the spine, through mesh morphing. Eventually, the SSM deformation fields allow the personalization of the mean structured FE model, leading to generate FE meshes of thoracolumbar spines with different morphologies. Models can be selectively viewed and downloaded through SpineView, according to personalized user requests of specific morphologies characterized by the geometrical parameters: Pelvic Incidence; Pelvic Tilt; Sacral Slope; Lumbar Lordosis; Global Tilt; Cobb Angle; and GAP score. Data quality is assessed using visual aids, correlation analyses, heatmaps, network graphs, Anova and t-tests, and kernel density plots to compare spinopelvic parameter distributions and identify similarities and differences. Mesh quality and ranges of motion have been assessed to evaluate the quality of the FE models. This functional repository is unique to generate virtual patient cohorts in ASD.

Acknowledgements: European Commission (MSCA-TN-ETN-2020-Disc4All-955735, ERC-2021-CoG-O-Health-101044828)

For decades, universities and research centers have been applying modeling and simulation (M&S) to problems involving health and medicine, coining the expression in silico clinical trials. However, its use is still limited to a restricted pool of specialists.

It is here proposed an easy-to-use cloud-based platform that aims to create a collaborative marketplace for M&S in orthopedics, where developers and model creators are able to capitalize on their work while protecting their intellectual property (IP), and researcher, surgeons and medical device companies can use M&S to accelerate time and to reduce costs of their research and development (R&D) processes.

Digital libraries on InSilicoTrials.com are built on collaborations among first-rate research center, model developers, software, and cloud providers (partners). Their access is provided to life science and healthcare companies, clinical centers, and research institutes (users), offering them with several solutions for the different steps of the orthopedics and medical devices R&D process. The platform is built using the Microsoft Azure cloud services, conforming to global standards of security and privacy for healthcare, ensuring that clinical data is properly managed, protected, and kept private. The environment protects the IP of partners against the downloading, copying, and changing of their M&S solutions; while providing a safe environment for users to seamlessly upload their own data, set up and run simulations, analyze results, and produce reports in conformity with regulatory requirements.

The proposed platform allows exploitation of M&S through a Software-as-a-Service delivery model. The pay-per-use pricing: 1. provide partners with a strong incentive to commercialize their high-quality M&S solutions; 2. enable users with limited budget, such as small companies, research centers and hospitals, to use advanced M&S solutions. Pricing of the M&S tools is based on specific aspects, such as particular features and computational power required, in agreement with the developing partner, and is distinct for different types of customers (i.e., academia or industry).

The first medical devices application hosted on InSilicoTrials.com is NuMRis (Numerical Magnetic Resonance Implant Safety), implemented in collaboration with the U.S. F.D.A. Center for Devices and Radiological Health, and ANSYS, Inc. The automatic tool allows the investigation of radiofrequency (RF)-induced heating of passive medical implants, such as orthopedic devices (e.g., rods and screws), pain management devices (e.g., leads), and cardiovascular devices (e.g., stents), following the ASTM F2182-19e2 Standard Test Method. NuMRis promotes the broader adoption of digital evidence in preclinical trials for RF safety analysis, supporting the device submission process and pre-market regulatory evaluation.

InSilicoTrials.com aims at defining a new collaborative framework in healthcare, engaging research centers to safely commercialize their IP, i.e., model templates, simulation tools and virtual patients, by helping clinicians and healthcare companies to significantly expedite the pre-clinical and clinical development phases, and to move across the regulatory approval and HTA processes.

Regulatory bodies impose stringent pre-market controls to certify the safety and compatibility of medical devices. However, internationally recognized standard tests may be expensive, time consuming and challenging for orthopedic implants because of many possible sizes and configurations. In addition, cost and time of standard testing may endanger the feasibility of custom-device production obtained through innovative manufacturing technologies like 3d printing.

Modeling and simulation (M&S) tools could be used by manufactures and at point-of-care to improve design confidence and reliability, accelerate design cycles and processes, and optimize the amount of physical testing to be conducted.

We propose an integrated cloud platform to perform in silico testing for orthopedic devices, assessing mechanical safety and electromagnetic compatibility, in line with recognized standards and regulatory guidelines.

The InSilicoTrials.com platform contains two M&S tools for orthopedic devices: CONSELF and NuMRis.

CONSELF (conself.com) uses Salome-Meca 2017 to compute static implant stresses and strains on metallic orthopedic devices, following the requirements and considerations of ASTM F2996-20 for non-modular hip femoral stems and ASTM F3161-16 for total knee femoral components. Simulation results were consistent with those reported in the two standards.

NuMRis (numris.insilicomri.com) uses ANSYS HFSS and ANSYS Mechanical 2019R3 to compute radio-frequency energy absorption and induced heating in 1.5T and 3T MRI coils, replicating the ASTM F2182-19e2 Standard Test Method. Simulation results were validated against in vitro measurements.

The integrated M&S workflow on the cloud platform allows the user to upload the 3D geometry and the material properties of the orthopedic device to be tested, automatically set up the standard testing scenarios, run simulations and process outcome, with the option to summarize the results in accordance with current FDA guidance on M&S reporting.

The easy-to-use interfaces of InSilicoTrials tools run through commercial web browsers, requiring no specific expertise in computational methods or additional on-premise software and hardware resources, since all simulations are run remotely on cloud infrastructure.

The integrated cloud platform can be used to evaluate design alternatives, test multi-configuration devices, perform multi-objective design optimization and identify worst-case scenarios within a family of implant sizes, or to assess the safety and compatibility of custom-made orthopedic devices.

InSilicoTrials.com is the first cloud platform offering a collection of M&S tools to perform in silico standard testing for orthopedic devices. The proposed tools allow to assess mechanical safety and electromagnetic compatibility before prototyping, preventing risks and criticalities for the patient, and helping manufacturers and point-of-care to accelerate time and reduce costs during the device development.

The proposed platform promotes the broader adoption of digital evidence in preclinical trials, supporting the device submission process and pre-market regulatory evaluation, and helping secure regulatory approval.