Advancements in imaging techniques have led to a rapid growth of available imaging data. Interpretation of the imaging data and extraction of biologically, physiologically and/or medically relevant information, however, remains challenging. In contrast, mechanistic computational modelling provides a means to formalise and dissect mechanisms governing the behaviour of complex systems. However, its application often is limited due to the lack of relevant data for model building and validation. Exploitation of the imaging data to build, parameterise and validate computational models gives rise to an image-based modelling approach. In this chapter, we introduce the basics of the mechanistic image-based modelling approach and review its application in developmental biology and biomedical research as well as for medical device development and drug discovery and development. Implementation of image-based modelling in pharmaceutical industry holds promise to further advance model-informed drug discovery and development and aids substantially in our understanding of drug pharmacokinetic, pharmacodynamic and ultimately de-risk drug development.
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http://dx.doi.org/10.1007/164_2019_328 | DOI Listing |
Comput Biol Med
January 2025
LaBS, Dipartimento di Chimica, Materiali e Ingegneria Chimica "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, 20133, Italy. Electronic address:
In the context of dynamic image-based computational fluid dynamics (DIB-CFD) modeling of cardiac system, the role of sub-valvular apparatus (chordae tendineae and papillary muscles) and the effects of different mitral valve (MV) opening/closure dynamics, have not been systemically determined. To provide a partial filling of this gap, in this study we performed DIB-CFD numerical experiments in the left ventricle, left atrium and aortic root, with the aim of highlighting the influence on the numerical results of two specific modeling scenarios: (i) the presence of the sub-valvular apparatus, consisting of chordae tendineae and papillary muscles; (ii) different MV dynamics models accounting for different use of leaflet reconstruction from imaging. This is performed for one healthy subject and one patient with mitral valve regurgitation.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea.
Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the 'black-box' nature. In this study, we present SleepXViT, an automatic sleep staging system using Vision Transformer (ViT) that provides intuitive, consistent explanations by mimicking human 'visual scoring'.
View Article and Find Full Text PDFCan J Ophthalmol
January 2025
Faculty of Medicine, University of Montreal, Montreal, QB, Canada; Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal, Montreal, QB, Canada. Electronic address:
Objective: To evaluate the performance of large language models (LLMs), specifically Microsoft Copilot, GPT-4 (GPT-4o and GPT-4o mini), and Google Gemini (Gemini and Gemini Advanced), in answering ophthalmological questions and assessing the impact of prompting techniques on their accuracy.
Design: Prospective qualitative study.
Participants: Microsoft Copilot, GPT-4 (GPT-4o and GPT-4o mini), and Google Gemini (Gemini and Gemini Advanced).
Sensors (Basel)
January 2025
Space Robotics Research Group (SpaceR), Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg, Luxembourg.
Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification.
View Article and Find Full Text PDFCancers (Basel)
January 2025
Clinic for Radiology, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany.
Background/objectives: In recent years, numerous studies have been published on determining the WHO grade of central nervous system (CNS) tumors using machine learning algorithms. These studies are usually based on magnetic resonance imaging (MRI) and sometimes also on positron emission tomography (PET) images. To date, however, there are virtually no corresponding studies based on routinely generated computed tomography (CT) images.
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