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!
Medical Image Analysis: Virtual lung screening trial (VLST): An in silico study inspired by the national lung screening trial for lung cancer detection
Fakrul Islam Tushar et al
Abstract
Clinical imaging trials play a crucial role in advancing medical innovation but are often costly, inefficient, and ethically constrained. Virtual Imaging Trials (VITs) present a solution by simulating clinical trial components in a controlled, risk-free environment. The Virtual Lung Screening Trial (VLST), an in silico study inspired by the National Lung Screening Trial (NLST), illustrates the potential of VITs to expedite clinical trials, minimize risks to participants, and promote optimal use of imaging technologies in healthcare. This study aimed to show that a virtual imaging trial platform could investigate some key elements of a major clinical trial, specifically the NLST, which compared Computed tomography (CT) and chest radiography (CXR) for lung cancer screening. With simulated cancerous lung nodules, a virtual patient cohort of 294 subjects was created using XCAT human models. Each virtual patient underwent both CT and CXR imaging, with deep learning models, the AI CT-Reader and AI CXR-Reader, acting as virtual readers to perform recall patients with suspicion of lung cancer. The primary outcome was the difference in diagnostic performance between CT and CXR, measured by the Area Under the Curve (AUC). The AI CT-Reader showed superior diagnostic accuracy, achieving an AUC of 0.92 (95 % CI: 0.90–0.95) compared to the AI CXR-Reader's AUC of 0.72 (95 % CI: 0.67–0.77). Furthermore, at the same 94 % CT sensitivity reported by the NLST, the VLST specificity of 73 % was similar to the NLST specificity of 73.4 %. This CT performance highlights the potential of VITs to replicate certain aspects of clinical trials effectively, paving the way toward a safe and efficient method for advancing imaging-based diagnostics.
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Philosophical transactions. Series A, Mathematical, physical, and engineering sciences: Challenges and opportunities in uncertainty quantification for healthcare and biological systems (Part 1)
Louise M. Kimpton et al
Abstract
Uncertainty quantification (UQ) is an essential aspect of computational modelling and statistical prediction. Multiple applications, including geophysics, climate science and aerospace engineering, incorporate UQ in the development and translation of new technologies. In contrast, the application of UQ to biological and healthcare models is understudied and suffers from several critical knowledge gaps. In an era of personalized medicine, patient-specific modelling, and digital twins, a lack of UQ understanding and appropriate implementation of UQ methodology limits the success of modelling and simulation in a clinical setting. The main contribution of our review article is to emphasize the importance and current deficiencies of UQ in the development of computational frameworks for healthcare and biological systems. As the introduction to the special issue on this topic, we provide an overview of UQ methodologies, their applications in non-biological and biological systems and the current gaps and opportunities for UQ development, as later highlighted by authors publishing in the special issue.
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Philosophical transactions. Series A, Mathematical, physical, and engineering sciences: Preface to the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)
Louise M. Kimpton et al
Abstract
Biological and healthcare system models are beginning to be used for decision support in the form of medical simulations and digital twins. These simulations and digital twins can explain the driving mechanisms behind physiological processes and predict outcomes thus having the potential to revolutionize personalized healthcare. However, when these models are used to make patient- or population-specific statements, it is crucial to quantify the different sources of uncertainty in the system to enable reliable model-based inference and clinical decision support. Our special issue aims to demonstrate, on biological systems and healthcare real-world applications, how to identify and account for uncertainties in a model-based analysis. Our special issue strives to help researchers in the healthcare/biological modelling field understand the significance of uncertainty quantification (UQ) and encourage the adoption of UQ as part of the credibility assessment, as recommended by regulatory agencies in the US and EU.
Here we provide an introduction to the theme issue ‘Uncertainty quantification for healthcare and biological systems (Part 2)’. The articles in volume 2 of our special issue continue to address multiple challenges in the application of UQ to biological and healthcare models raised in our review article on ‘Challenges and opportunities in uncertainty quantification for healthcare and biological systems, which is the introduction to volume 1 of our theme issue.
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CPT: Pharmacometrics & Systems Pharmacology: Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues
Giuseppe Pasculli et al
Abstract
With the recent and evolving regulatory frameworks regarding the usage of Artificial Intelligence (AI) in both drug and medical device development, the differentiation between data derived from observed (‘true’ or ‘real’) sources and artificial data obtained using process-driven and/or (data-driven) algorithmic processes is emerging as a critical consideration in clinical research and regulatory discourse. We conducted a critical literature review that revealed evidence of the current ambivalent usage of the term “synthetic” (along with derivative terms) to refer to “true/observed” data in the context of clinical trials and AI-generated data (or “artificial” data). This paper, stemming from a critical evaluation of different perspectives captured from the scientific literature and recent regulatory endeavors, seeks to elucidate this distinction, exploring their respective utilities, regulatory stances, and upcoming needs, as well as the potential for both data types in advancing medical science and therapeutic development.
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Annals of Biomedical Engineering: EMG-Informed Neuromusculoskeletal Simulations Increase the Accuracy of the Estimation of Knee Joint Contact Forces During Sub-optimal Level Walking
Domitille Princelle et al
Abstract
Personalized musculoskeletal models are crucial to get insights into the mechanisms underpinning neuromusculoskeletal disorders and have the potential to support clinicians in the daily management and evaluation of patients. However, their use is still limited due to the lack of validation studies, which hinders people’s trust in these technologies. The current study aims to assess the predictive accuracy of two common approaches to estimate knee joint contact forces, when employing musculoskeletal models.
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Journal of Biomechanics: Growth in a two-dimensional model of coarctation of the aorta: A CFD-informed agent based model
Nasonkwe Hampwaye et al
Abstract
In the individualized treatment of a patient with Coarctation of the Aorta (CoA), a non-severe case which initially exhibits no symptoms, and thus requires no treatment, could potentially become severe over time. This progression can be attributed to insufficient growth at the coarctation site relative to the overall growth of the child. Therefore, an agent-based model (ABM) to predict the aortic growth of a CoA patient is introduced. The multi-scale approach combines Computational Fluid Dynamics (CFD) and ABM to study systems that are influenced by both mechanical stimuli and biochemical responses characteristic of growth. Our focus is on ABM development; thus, CFD insights were applied solely to enhance the ABM framework. Comparative medicine was leveraged to develop a species-specific ABM by considering the rat and porcine species commonly used in cardiovascular research together with data from healthy human toddlers. The ABM luminal radius prediction accuracy was observed to be 79% for rat, above 95% for porcine and 91. 6% for the healthy toddler; while that observed for the growth rate was 38.7%, 90% and 64.3% respectively. Given its performance, the ABM was adapted to a 2.5-year-old patient-specific CoA. Subsequently, the model predicted that by age 3, the condition would worsen, marked by persistent CoA enhanced by the predicted least growth compared to growth predicted in the rest of the aorta, hypertension, and increased turbulent flow; thus, increased vessel injury risk. The findings advise for incorporating vascular remodelling into the ABM to enhance its predictive capability for intervention planning.
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Clinical and Translational Science: AI-Driven Applications in Clinical Pharmacology and Translational Science: Insights From the ASCPT 2024 AI Preconference
Mohamed H. Shahin et al
Abstract
Artificial intelligence (AI) is driving innovation in clinical pharmacology and translational science with tools to advance drug development, clinical trials, and patient care. This review summarizes the key takeaways from the AI preconference at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2024 Annual Meeting in Colorado Springs, where experts from academia, industry, and regulatory bodies discussed how AI is streamlining drug discovery, dosing strategies, outcome assessment, and patient care. The theme of the preconference was centered around how AI can empower clinical pharmacologists and translational researchers to make informed decisions and translate research findings into practice. The preconference also looked at the impact of large language models in biomedical research and how these tools are democratizing data analysis and empowering researchers. The application of explainable AI in predicting drug efficacy and safety, and the ethical considerations that should be applied when integrating AI into clinical and biomedical research were also touched upon. By sharing these diverse perspectives and real-world examples, this review shows how AI can be used in clinical pharmacology and translational science to bring efficiency and accelerate drug discovery and development to address patients' unmet clinical needs.
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