Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

C Wu, G Lorenzo, DA Hormuth, EABF Lima… - Biophysics …, 2022‏ - pubs.aip.org
Digital twins employ mathematical and computational models to virtually represent a
physical object (eg, planes and human organs), predict the behavior of the object, and …

Cardiac healthcare digital twins supported by artificial intelligence-based algorithms and extended reality—a systematic review

Z Rudnicka, K Proniewska, M Perkins, A Pregowska - Electronics, 2024‏ - mdpi.com
Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital
Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which …

Novel approach to classify brain tumor based on transfer learning and deep learning

S Jain, V Jain - International Journal of Information Technology, 2023‏ - Springer
Transfer learning strategies were used to develop a unique method in the field of medicine.
Investigation in this study suggests an ensemble technique for early brain tumor detection …

Designing clinical trials for patients who are not average

TE Yankeelov, DA Hormuth, EABF Lima, G Lorenzo… - Iscience, 2024‏ - cell.com
The heterogeneity inherent in cancer means that even a successful clinical trial merely
results in a therapeutic regimen that achieves, on average, a positive result only in a subset …

Quantitative in vivo imaging to enable tumour forecasting and treatment optimization

G Lorenzo, DA Hormuth II, AM Jarrett… - Cancer, complexity …, 2022‏ - Springer
Current clinical decision-making in oncology relies on averages of large patient populations
to both assess tumour status and treatment outcomes. However, cancers exhibit an inherent …

Computer vision techniques for growth prediction: A prisma-based systematic literature review

Y Harie, BP Gautam, K Wasaki - Applied Sciences, 2023‏ - mdpi.com
Growth prediction technology is not only a practical application but also a crucial approach
that strengthens the safety of image processing techniques. By supplementing the growth …

SPBTGNS: Design of an Efficient Model for Survival Prediction in Brain Tumour Patients using Generative Adversarial Network with Neural Architectural Search …

R Zaitoon, SN Mohanty, D Godavarthi… - IEEE Access, 2024‏ - ieeexplore.ieee.org
The landscape of medical imaging, particularly in brain tumor analysis and survival
prediction, necessitates advancements due to the inherent complexities and life-threatening …

Ensemble inversion for brain tumor growth models with mass effect

S Subramanian, A Ghafouri… - IEEE transactions on …, 2022‏ - ieeexplore.ieee.org
We propose a method for extracting physics-based biomarkers from a single multiparametric
Magnetic Resonance Imaging (mpMRI) scan bearing a glioma tumor. We account for mass …

Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning

K Kuang, F Dean, JB Jedlicki… - Advances in …, 2025‏ - proceedings.neurips.cc
A digital twin is a virtual replica of a real-world physical phenomena that uses mathematical
modeling to characterize and simulate its defining features. By constructing digital twins for …

Bilo: Bilevel local operator learning for pde inverse problems

RZ Zhang, X **e, JS Lowengrub - arxiv preprint arxiv:2404.17789, 2024‏ - arxiv.org
We propose a new neural network based method for solving inverse problems for partial
differential equations (PDEs) by formulating the PDE inverse problem as a bilevel …