Click here to read some interesting recently published papers from our community. If you have published an article in the field of in silico medicine, send it to us: we will include it in this section of the newsletter!
Biomechanics and Modeling in Mechanobiology: Comparing the predictions of CT-based subject-specific finite element models of human metastatic vertebrae with digital volume correlation measurements
Chiara Garavelli et al
Abstract
Several conditions can increase the incidence of vertebral fragility fractures, including metastatic bone disease. Computational tools could help clinicians estimate the risk of vertebral fracture in these patients; however, comparison with in vitro data is mandatory before using them in clinical practice. Nine spine segments were tested under compression and imaged with micro-computed tomography (µCT). The displacement field was calculated for each vertebra using a global digital volume correlation (DVC) approach. Subject-specific homogenised finite element models of each vertebra were built from µCT images, applying experimentally matched boundary conditions at the endplates. Numerical and experimental displacements, reaction forces, and locations showing higher strain concentrations were eventually compared. Additionally, given that µCT cannot be performed in clinical settings, the outcomes of a µCT-based model were also compared to those of a model built from clinical CT scans of the same specimen. Good agreement between DVC and µCT-based FE displacements was found, both for healthy (R2 = 0.69 ÷ 0.83, RMSE = 3 ÷ 22%, max error < 45 μm) and metastatic (R2 = 0.64 ÷ 0.93, RMSE = 5 ÷ 18%, max error < 54 μm) vertebrae. Strong correlations were found between µCT-based and clinical CT-based FE model outcomes (R2 = 0.99, RMSE < 1.3%, max difference = 6 μm). Furthermore, the models qualitatively identified the most deformed regions identified with the experiments. In conclusion, the combination of experimental full-field technique and in-silico modelling enabled the development of a promising pipeline to validate bone strength predictors in the elastic range. Further improvements are needed to analyse vertebral post-yield behaviour better.
---------------------------------------
PNAS Nexus: The future of in silico trials and digital twins in medicine
Ehsan Samei
Abstract
In silico trials and digital twins are emerging as transformative medical technologies, as they offer a unique way to design medical innovations, optimize their application, and evaluate their utility. Their use spans from individual care-appropriating the technology for personalized decision, to population care-presenting an alternative to design, supplement, or replace clinical trials. They effectually offer a new way to efficiently qualify, quantify, and personalize healthcare innovations in advance or in conjunction with clinical application. While much progress is underway to advance these technologies across diverse developments, realizing their full potential requires a cohesive goal to unify separate activities towards a common objective. Such a cohesive goal-moonshot-can be defined as forming and fostering a digital twin of every single human person, owned by the individual, progressively updated with new data, and used to deliver optimized care, technology assessment, and real-world evidence. The feasibility of such a vision builds upon a growing body of work in computational modeling, regulatory science, and digital healthcare. Bringing this vision to reality requires ownership and active engagement of all stakeholders to contribute diverse expertise and resources for transforming medicine and medical appropriation towards a more accurate, efficient, and quantitative future.
---------------------------------------
PNAS: Striking the balance: Complexity, simplicity, and credibility in mathematical biology
Cristobal Rodero, Steven A. Niederer
---------------------------------------
AMIA Annual Symposium proceedings: SeqTrial: Utility Preserving Sequential Clinical Trial Data Generator
Trisha Das et al
Abstract
Clinical trial data used to evaluate new treatments have value beyond the original studies, but limitations in data access due to privacy concerns make further use of these data challenging. Digital twins offer a solution by simulating patient outcomes, providing less restricted data access, reducing costs and increasing sample sizes. However, existing research focuses on synthetic Electronic Healthcare Records (EHRs) and lacks personalized patient record generation. This paper introduces SeqTrial, a framework for generating personalized digital twins for sequential clinical trial event data. The method uses BioBERT word embeddings to capture biomedical term semantics, an attention mechanism to understand visit relationships, and synthesizes digital twins for each patient. SeqTrial generates utility-preserving digital twins capable of estimating clinical outcomes, while addressing data scarcity through self-supervised pretraining. The method demonstrates high fidelity and utility in generating synthetic sequential clinical trial data for patient outcome prediction while ensuring privacy protection.
---------------------------------------
The Lancet Digital Health: Digital twins, synthetic patient data, and in-silico trials: can they empower paediatric clinical trials?
Mohan Pammi et al
Abstract
Randomised controlled trials are the gold standard to assess the effectiveness and safety of clinical interventions; however, many paediatric trials are discontinued early due to challenges in patient enrolment. Hence, most paediatric clinical trials suffer from lack of adequate power. Additionally, trials are expensive and might expose patients to unproven therapies. Alternatives to overcome these issues using virtual patient data—namely, digital twins, synthetic patient data, and in-silico trials—are now possible due to rapid advances in digital health-care tools and interventions. However, such digital innovations have been rarely used in paediatric trials. In this Viewpoint, we propose using virtual patient data to empower paediatric trials. The use of virtual patient data has the advantages of decreased exposure of children to potentially ineffective or risky interventions, shorter trial durations leading to more rapid ascertainment of safety and effectiveness of interventions, and faster drug approvals. Use of virtual patient data could lead to more personalised treatment options with low costs and could result in faster clinical implementation of interventions in children. However, ethical and regulatory concerns, including replacing humans with digital data, data privacy, and security should be addressed and the safety and sustainability of digital data innovation ensured before virtual patient data are adopted widely.
---------------------------------------
Frontiers in Digital Health: Beyond the gender data gap: co-creating equitable digital patient twins
Nora Weinberger et al
Abstract
Digital patient twins constitute a transformative innovation in personalized medicine, integrating patient-specific data into predictive models that leverage artificial intelligence (AI) to optimize diagnostics and treatments. However, existing digital patient twins often fail to incorporate gender-sensitive and socio-economic factors, reinforcing biases and diminishing their clinical effectiveness. This (gender) data gap, long recognized as a fundamental problem in digital health, translates into significant disparities in healthcare outcomes. This mini-review explores the interdisciplinary connections of technical foundations, medical relevance, as well as social and ethical challenges of digital patient twins, emphasizing the necessity of gender-sensitive design and co-creation approaches. We argue that without intersectional and inclusive frameworks, digital patient twins risk perpetuating existing inequalities rather than mitigating them. By addressing the interplay between gender, AI-driven decision-making and health equity, this mini-review highlights strategies for designing more inclusive and ethically responsible digital patient twins to further interdisciplinary approaches.
---------------------------------------
