Patient Specific Models
Patient Specific Models - Related Videos
12:09
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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Cited by 14 •
2013
A methodology to estimate ventricular fiber orientations from in vivo images of patient heart geometries for personalized modeling is described. Validation of the methodology performed using normal and failing canine hearts demonstrate that that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.
07:30
3D Printing Model of a Patient's Specific Lumbar Vertebra
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Cited by 4 •
2023
This study aims to create a 3D-printed model of a patient-specific lumbar vertebra, which contains both the vertebra and spinal nerve models fused from high-resolution computed tomography (HRCT) and MRI-Dixon data.
09:15
Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training
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Cited by 10 •
2022
Patient-specific models improve surgeon and fellow confidence when developing or learning surgical plans. Three-dimensional (3D) printers generate adequate detail for surgical preparation, but fail to replicate tissue haptic fidelity. A protocol is presented detailing the creation of patient-specific, silicone cardiac models, combining 3D printing precision with simulated silicone tissue.
07:56
Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models
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Cited by 9 •
2020
We present a high-throughput, in vitro method for quantifying regional pulmonary deposition at the lobe level using CT scan-derived, 3D printed lung models with tunable air flow profiles.
03:16
Patient-derived Xenograft Modeling: A Technique to Generate Melanoma Mouse Models
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2023
In this video, human-derived melanoma cells are implanted subcutaneously into the flank region of an immunocompromised mouse. The PDX model allows preclinical investigation of melanoma cells that better recapitulate the tumor heterogeneity and melanoma aggressiveness observed in in vivo conditions.
FAQs
These models preserve individual variation in molecular and physiological features, allowing researchers to compare responses under the same defined condition or treatment. Differences observed between models can then be linked to the corresponding patient data, helping investigate disease mechanisms and identify why a result may not be adequately represented by a generalized population-level pattern. This supports more individualized biological interpretation.
Clinical, genetic, cellular, and tissue data provide complementary views of the same biological system. Genetic information can represent individual variation, while cellular and tissue observations show how that variation is reflected in biological behavior; clinical data connect those findings to the person’s disease context. Combining these layers helps researchers interpret treatment responses rather than relying on a single measurement.
Unlike population-level models, these models are designed to retain features from a particular individual. That distinction lets researchers compare how different individuals’ biological representations respond to the same defined condition, rather than treating an average response as universally applicable. The comparison can expose patient-linked differences in disease mechanisms, therapeutic response, or potential biomarkers while preserving a connection to the original clinical data.
Developing a patient-specific model begins with selecting relevant clinical, genetic, cellular, or tissue data from the individual. Scientists then create an experimental or computational representation and examine its molecular or physiological features under defined conditions or treatments. Results are compared with the originating patient information and, when appropriate, with other models to evaluate disease-related patterns or treatment responses.
A measured response can provide evidence about how an individual’s biology behaves under that treatment and can be compared with responses from other patient-specific models. Such comparisons may help investigate disease mechanisms, evaluate therapeutic responses, and identify biomarkers. The value lies in linking the observed laboratory or computational pattern back to individual variation rather than interpreting it only as a population average.
They provide a way to study treatment behavior in representations connected to individual clinical and biological data. In drug development, this can support comparison of therapeutic responses and investigation of disease mechanisms; in precision medicine, it can inform research aimed at more individualized diagnosis and treatment. Their broader contribution is reducing reliance on generalized assumptions when individual variation is biologically important.