Digital twins are virtual replicas of physical entities and are poised to transform personalized medicine through the real-time simulation and prediction of human physiology. Translating this paradigm from engineering to biomedicine requires overcoming profound challenges, including anatomical variability, multi-scale biological processes, and the integration of multi-physics phenomena. This survey systematically reviews methodologies for building digital twins of human organs, structured around a pipeline decoupled into anatomical twinning (capturing patient-specific geometry and structure) and functional twinning (simulating multi-scale physiology from cellular to organ-level function). We categorize approaches both by organ-specific properties and by technical paradigm, with particular emphasis on multi-scale and multi-physics integration. A key focus is the role of artificial intelligence (AI), especially physics-informed AI, in enhancing model fidelity, scalability, and personalization. Furthermore, we discuss the critical challenges of clinical validation and translational pathways. This study not only charts a roadmap for overcoming current bottlenecks in single-organ twins
Objective: A digital twin of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions. However, it is crucial that the patient's surface and internal organs are of high quality for any pose and shape estimates. At present, the majority of statistical shape models (SSMs) are restricted to a small number of organs or bones or do not adequately represent the general population. Method: To address this, we propose a deformable human shape and pose model that combines skin, internal organs, and bones, learned from CT images. By modeling the statistical variations in a pose-normalized space using probabilistic PCA while also preserving joint kinematics, our approach offers a holistic representation of the body that can benefit various medical applications. Results: We assessed our model's performance on a registered dataset, utilizing the unified shape space, and noted an average error of 3.6 mm for bones and 8.8 mm for organs. To further verify our findings, we conducted additional tests on publicly available datasets with
An overview of the applications of control theory to prosthetic sense organs including the senses of vision, taste and odor is being presented in this paper. Simulation aspect nowadays has been the centre of research in the field of prosthesis. There have been various successful applications of prosthetic organs, in case of natural biological organs dis-functioning patients. Simulation aspects and control modeling are indispensible for knowing system performance, and to generate an original approach of artificial organs. This overview focuses mainly on control techniques, by far a theoretical overview and fusion of artificial sense organs trying to mimic the efficacies of biologically active sensory organs. Keywords: virtual reality, prosthetic vision, artificial
The understanding of the mechanisms driving vascular development is still limited. Techniques to generate vascular trees synthetically have been developed to tackle this problem. However, most algorithms are limited to single trees inside convex perfusion volumes. We introduce a new framework for generating multiple trees inside general nonconvex perfusion volumes. Our framework combines topology optimization and global geometry optimization into a single algorithmic approach. Our first contribution is defining a baseline problem based on Murray's original formulation, which accommodates efficient solution algorithms. The problem of finding the global minimum is cast into a nonlinear optimization problem (NLP) with merely super-linear solution effort. Our second contribution extends the NLP to constrain multiple vascular trees inside any nonconvex boundary while avoiding intersections. We test our framework against a benchmark of an anatomic region of brain tissue and a vasculature of the human liver. In all cases, the total tree energy is improved significantly compared to local approaches. By avoiding intersections globally, we can reproduce key physiological features such as par
There is an increase in demand for organs as transplantation is becoming a common practice to elongate human life. To reach this demand, three-dimensional bioprinting is developing from prior knowledge of scaffolds, growth factors, etc. This review paper aims to determine the current status and future possibilities of three-dimensional bioprinting of organs and evaluate the benefits and challenges, along with the history of its development. Prior research has viewed three-dimensional bioprinting as a technology that will enable safer transplantation without graft rejection and provide demand-based production. However, it faces challenges such as the need to improve biocompatibility and biofunctionality, legal and ethical issues, and the need to improve the technology itself. While the development of three-dimensional printing organs is not yet completed, we are seeing improvements and expecting it to be clinically applied soon.
In this paper, we investigate protection strategies of sensitive body anatomy against the irradiation to the cancerous moving tumors in intensity modulated radiation therapy. Inspired by optimization techniques developed in statistical physics and dynamical systems, we deploy a method based on variational principles and formulate an efficient genetic algorithm which enable us to search for global minima in a complex landscape of irradiation dose delivered to the radiosensitive organs at risk. We take advantage of the internal motion of body anatomy during radiation therapy to reduce the unintentional delivery of the radiation to sensitive organs. We show that the accurate optimization of the control parameters, compare to the conventional IMRT and widely used delivery based on static anatomy assumption, leads to a significant reduction of the dose delivered to the organs at risk.
Hutchinson-Gilford Progeria syndrome, Werner syndrome, and Cockayne syndrome are three genetic disorders, in which the children have premature aging features. To understand the phenomenon of premature aging, the similarity of aging features in these syndromes to that in normal aging is investigated. Although these three syndromes have different genetic backgrounds, all the patients have abnormal structures of tissues/organs like that in normal aging. Therefore, the abnormality in tissue structure is the common point in premature aging and normal aging. This abnormality links also a defective development and a defective repair, the Misrepair. Defective development is a result of Mis-construction of the structure of tissues and organs as consequence of genetic mutations. Aging is a result of Mis-reconstructions, the Misrepairs, for maintaining the structure of tissues/organs. Construction-reconstruction of the structure of an organism is thus the coupling point of development and aging. Mis- construction and Mis-reconstruction (Misrepair) are the essential processes for the development of aging-like feathers. In conclusion, premature aging is a result of Mis- construction of tissues
Counting plant organs such as heads or tassels from outdoor imagery is a popular benchmark computer vision task in plant phenotyping, which has been previously investigated in the literature using state-of-the-art supervised deep learning techniques. However, the annotation of organs in field images is time-consuming and prone to errors. In this paper, we propose a fully unsupervised technique for counting dense objects such as plant organs. We use a convolutional network-based unsupervised segmentation method followed by two post-hoc optimization steps. The proposed technique is shown to provide competitive counting performance on a range of organ counting tasks in sorghum (S. bicolor) and wheat (T. aestivum) with no dataset-dependent tuning or modifications.
Understanding biological phenomena requires a systemic approach that incorporates different mechanisms acting on different spatial and temporal scales, since in organisms the workings of all components, such as organelles, cells, and organs interrelate. This inherent interdependency between diverse biological mechanisms, both on the same and on different scales, provides the functioning of an organism capable of maintaining homeostasis and physiological stability through numerous feedback loops. Thus, developing models of organisms and their constituents should be done within the overall systemic context of the studied phenomena. We introduce such a method for modeling growth and regeneration of livers at the organ scale, considering it a part of the overall multi-scale biochemical and biophysical processes of an organism. Our method is based on the earlier discovered general growth law, postulating that any biological growth process comprises a uniquely defined distribution of nutritional resources between maintenance needs and biomass production. Based on this law, we introduce a liver growth model that allows to accurately predicting the growth of liver transplants in dogs and l
Unlike other organs, the thymus and gonads generate non-uniform cell populations, many members of which perish, and a few survive. While it is recognized that thymic cells are 'audited' to optimize an organism's immune repertoire, whether gametogenesis could be orchestrated similarly to favour high quality gametes is uncertain. Ideally, such quality would be affirmed at early stages before the commitment of extensive parental resources. A case is here made that, along the lines of a previously proposed lymphocyte quality control mechanism, gamete quality can be registered indirectly through detection of incompatibilities between proteins encoded by the grandparental DNA sequences within the parent from which haploid gametes are meiotically derived. This 'stress test' is achieved in the same way that thymic screening for potential immunological incompatibilities is achieved - by 'promiscuous' expression, under the influence of the AIRE protein, of the products of genes that are not normally specific for that organ. Consistent with this, the Aire gene is expressed in both thymus and gonads, and AIRE deficiency impedes function in both organs. While not excluding the subsequent emerge
At the basic geometric level, the distribution networks carrying vital materials in living organisms, and the units such as the nephrons and alveoli, form a scaling structure named here the site model. This unified view of the allometry of the kidney and lung of mammals is in agreement with the existing data, and in some cases, improves the predictions of the previous fractal model of the lung. Allometric scaling of surfaces and volumes of relevant organ parts are derived from a few simple propositions about the collective characteristics of the sites. New hypotheses and predictions are formulated, some verified by the data, while others remain to be tested, such as the scaling of the number of capillaries and the site model description of other organs.
The hearts, kidneys, livers, spleens and brains of ${}^57$Fe enriched wild-type and heterozygous $β$-thalassaemic mice at 1, 3, 6 and 9 months of age were studied by means of Mössbauer Spectroscopy at 80K. Ferritin-like iron depositions in the heart and the brain of the thalassaemic mice were found to be slightly increased while significant amounts of Ferritin-like iron were found in the kidneys, liver and spleen. The ferritin-like iron doublet, found in the organs, could be further separated into two sub-doublets representing the inner and surface structures of ferritin mineral core. Surface iron sites were found to be predominant in the hearts and brains of all mice and in the kidneys of the wild-type animals. Ferritin rich in inner iron sites was predominant in the kidneys of the thalassaemic mice, as well as in the livers and in the spleens. The inner-to-surface iron sites ratio was elevated in all thalassaemic samples indicating that besides ferritin amount, the disease can also affect ferritin mineral core structure.
Organ transplantation, which is the utilization of codes directly related to some specific functionalities to complete ones own program, provides more convenience for developers than traditional component reuse. However, recent techniques are challenged with the lack of organs for transplantation. Hence, we conduct an empirical study on extracting organs from GitHub repository to explore transplantation based on large-scale dataset. We analyze statistics from 12 representative GitHub projects and get the conclusion that 1) there are abundant practical organs existing in commits with add as a key word in the comments; 2) organs in this repository mainly possess four kinds of contents; 3) approximately 70% of the organs are easy-to-transplant. Implementing our transplantation strategy for different kinds of organs, we manually extract 30 organs in three different programming languages, namely Java, Python, and C, and make unit tests for them utilizing four testing tools (two for Java, one for Python, and one for C). At last, we transplant three Java organs into a specific platform for a performance check to verify whether they can work well in the new system. All the 30 organs extrac
The emergence of spatial patterns and organized growth is a hallmark of developing tissues. While symmetry-breaking and scaling laws govern these processes, how cells coordinate spatial patterning with size regulation remains unclear. Here, we combine quantitative imaging, a Turing activator-repressor model with self-organized reactive boundaries, and in vitro models of early mouse development to study mesodermal pattern formation in two-dimensional (2D) gastruloids. We show that colony size dictates symmetry: small colonies (radius approximately 100 micrometers) spontaneously break symmetry, while larger ones remain centro-symmetric, consistent with size-dependent positional information and model predictions. The mesodermal domain area scales robustly with colony size following a power law, independent of cell density, indicating that cells sense and respond to gastruloid size. Time-lapse imaging reveals a biphasic growth law: an early power-law expansion followed by exponential arrest, marking a dynamical phase transition. These dynamics, conserved across sizes, reflect features of criticality seen in physical systems, where self-organization, scaling, and boundary feedback conve
Organ segmentation in Positron Emission Tomography (PET) plays a vital role in cancer quantification. Low-dose PET (LDPET) provides a safer alternative by reducing radiation exposure. However, the inherent noise and blurred boundaries make organ segmentation more challenging. Additionally, existing PET organ segmentation methods rely on coregistered Computed Tomography (CT) annotations, overlooking the problem of modality mismatch. In this study, we propose LDOS, a novel CT-free ultra-LDPET organ segmentation pipeline. Inspired by Masked Autoencoders (MAE), we reinterpret LDPET as a naturally masked version of Full-Dose PET (FDPET). LDOS adopts a simple yet effective architecture: a shared encoder extracts generalized features, while task-specific decoders independently refine outputs for denoising and segmentation. By integrating CT-derived organ annotations into the denoising process, LDOS improves anatomical boundary recognition and alleviates the PET/CT misalignments. Experiments demonstrate that LDOS achieves state-of-the-art performance with mean Dice scores of 73.11% (18F-FDG) and 73.97% (68Ga-FAPI) across 18 organs in 5% dose PET. Our code will be available at https://githu
As epithelial development or wound closure approaches completion, cell proliferation progressively slows via contact inhibition of proliferation (CIP) - a mechanism understood as being strictly local. Here we report the discovery of inhibition of proliferation through an unanticipated mechanism that is non-local. As a confluent epithelial layer becomes progressively more jammed, two interpenetrating networks emerge: islands of mechanically compressed non-cycling cells percolating within an ocean of mechanically tensed cycling cells. The evolution of the compression network was found to be susceptible to both specific molecular stimulus and to injury-induced unjamming. Yet, in all circumstances, the size of compressed islands followed a power-law distribution that was well-captured by preferential network theory. Together, these findings demonstrate the existence of a network-based inhibition of proliferation (NIP) that is self-organizing and poised in proximity to criticality.
Multi-organ segmentation has extensive applications in many clinical applications. To segment multiple organs of interest, it is generally quite difficult to collect full annotations of all the organs on the same images, as some medical centers might only annotate a portion of the organs due to their own clinical practice. In most scenarios, one might obtain annotations of a single or a few organs from one training set, and obtain annotations of the the other organs from another set of training images. Existing approaches mostly train and deploy a single model for each subset of organs, which are memory intensive and also time inefficient. In this paper, we propose to co-train weight-averaged models for learning a unified multi-organ segmentation network from few-organ datasets. We collaboratively train two networks and let the coupled networks teach each other on un-annotated organs. To alleviate the noisy teaching supervisions between the networks, the weighted-averaged models are adopted to produce more reliable soft labels. In addition, a novel region mask is utilized to selectively apply the consistent constraint on the un-annotated organ regions that require collaborative tea
Annotating multiple organs in medical images is both costly and time-consuming; therefore, existing multi-organ datasets with labels are often low in sample size and mostly partially labeled, that is, a dataset has a few organs labeled but not all organs. In this paper, we investigate how to learn a single multi-organ segmentation network from a union of such datasets. To this end, we propose two types of novel loss function, particularly designed for this scenario: (i) marginal loss and (ii) exclusion loss. Because the background label for a partially labeled image is, in fact, a `merged' label of all unlabelled organs and `true' background (in the sense of full labels), the probability of this `merged' background label is a marginal probability, summing the relevant probabilities before merging. This marginal probability can be plugged into any existing loss function (such as cross entropy loss, Dice loss, etc.) to form a marginal loss. Leveraging the fact that the organs are non-overlapping, we propose the exclusion loss to gauge the dissimilarity between labeled organs and the estimated segmentation of unlabelled organs. Experiments on a union of five benchmark datasets in mult
In the last decades, many computational models have been developed to predict soft tissue growth and remodeling (G&R). The constrained mixture theory describes fundamental mechanobiological processes in soft tissue G&R and has been widely adopted in cardiovascular models of G&R. However, even after two decades of work, large organ-scale models are rare, mainly due to high computational costs (model evaluation and memory consumption), especially in long-range simulations. We propose two strategies to adaptively integrate history variables in constrained mixture models to enable large organ-scale simulations of G&R. Both strategies exploit that the influence of deposited tissue on the current mixture decreases over time through degradation. One strategy is independent of external loading, allowing the estimation of the computational resources ahead of the simulation. The other adapts the history snapshots based on the local mechanobiological environment so that the additional integration errors can be controlled and kept negligibly small, even in G&R scenarios with severe perturbations. We analyze the adaptively integrated constrained mixture model on a tissue pat
We propose a simulation-optimization-based methodology to improve the way that organ transplant offers are made to potential recipients. Our policy can be applied to all types of organs, is implemented starting at the local level, is flexible with respect to simultaneous offers of an organ to multiple patients, and takes into account the quality of the organs under consideration. We describe in detail our simulation-optimization procedure and how it uses underlying real-world transplant data to inform the decision-making process. We present results using our liver and kidney models, where we show that, under our policy recommendations, more organs are utilized and the required times to allocate the organs are reduced -- sometimes dramatically.