Patient-specific Modeling
Patient-specific Modeling - 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 data provide complementary views of the same patient: imaging characterizes anatomy, while clinical and biological measurements add information about physiology or disease. Computational methods can integrate these inputs so the resulting analysis reflects more than one measurement type. This multimodal approach supports simulations that are tailored to the individual rather than based on a single observation.
They translate patient measurements into simulations of disease behavior, treatment responses, or changes over time. Their role is not merely to store clinical information; they allow investigators to examine how the represented anatomy, physiology, or disease may respond under different conditions. This makes computational analysis useful for evaluating possible interventions before clinical application.
Individual variation can influence how disease develops and how a treatment may work. By incorporating information from a particular patient, these models help researchers examine differences in disease behavior and outcomes rather than treating every case as identical. That focus connects modeling with precision medicine and can improve the basis for personalized therapy and clinical decision-making.
By combining a patient’s measurements and simulating relevant disease behavior, the approach can add a computational perspective to diagnosis and risk assessment. It may help clinicians organize information about anatomy, physiology, and disease, then consider how those features relate to possible outcomes. This analysis can inform clinical decision-making and the evaluation of personalized treatment options.
Useful inputs include medical images, laboratory results, genomic information, and other clinical data. The selection reflects what aspect of the patient is being studied: imaging contributes anatomical information, while laboratory, genomic, and clinical measurements contribute biological or disease-related context. Integrating these sources gives the computational analysis a broader patient-specific basis.
A typical workflow begins by gathering the patient’s clinical, biological, and imaging measurements. These data are then integrated into a computational representation, after which mathematical or computational methods simulate disease behavior, treatment responses, or changes over time. The outputs can be used to compare possible interventions and examine how individual characteristics may influence predicted outcomes.
Clinicians may use the approach for diagnosis, risk assessment, and treatment planning, especially when comparing possible interventions could inform personalized therapy. Researchers can also use it to investigate disease mechanisms and study how patient differences influence outcomes. Thus, the same modeling framework connects clinical decisions with research questions about disease and treatment response.