Systems and methods for multi-dimensional fluid modeling of an organism or organ
A multiple fluid model tool for multi-dimensional fluid modeling of a biological structure is presented. For example, a system includes a modeling component, a machine learning component, and a three-dimensional health assessment component. The modeling component generates a three-dimensional model of a biological structure based on multi-dimensional medical imaging data. The machine learning component predicts one or more characteristics of the biological structure based on input data and a machine learning process associated with the three-dimensional model. The three-dimensional health assessment component that provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the biological structure based on the input data and the one or more characteristics of the biological structure on the three-dimensional model.
1. A system, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
generating a three-dimensional model of a biological structure based on medical imaging data, wherein generating the three-dimensional model comprises integrating (i) a first three-dimensional model associated with a first portion of the biological structure and a first machine learning process, and (ii) a second three-dimensional model associated with a second portion of the biological structure and a second machine learning process to generate a further three-dimensional model, the first three-dimensional model and the second three-dimensional model defining one or more physical characteristics of a plurality of connected chambers of the biological structure, and wherein the first machine learning process is different from the second machine learning process;
predicting one or more flow or transfer characteristics from the first portion of the biological structure to the second portion of the biological structure based on input data, the first machine learning process, and the second machine learning process; and
providing a three-dimensional health assessment environment associated with the further three-dimensional model, wherein the three-dimensional health assessment environment renders physics modeling data of the biological structure based on the input data and the one or more flow or transfer characteristics of the biological structure on the further three-dimensional model.
2. The system of claim 1 , the first machine learning process is performed based on data associated with the second three-dimensional model.
3. The system of claim 1 , wherein the second machine learning process is performed based on data associated with the first three-dimensional model.
4. The system of claim 1 , wherein the machine learning component determines one or more first characteristics of the first portion of the biological structure via the first machine learning process and one or more second characteristics of the second portion of the biological structure via the second machine learning process.
5. The system of claim 4 , wherein the one or more characteristics of the biological structure are predicted based on the one or more first characteristics of the first portion of the biological structure and the one or more second characteristics of the second portion of the biological structure.
6. The system of claim 1 , the operations further comprising facilitating interaction between the first three-dimensional model and the second three-dimensional model based on the input data.
7. The system of claim 1 , the operations further comprising rendering the physics modeling data on the further three-dimensional model.
8. The system of claim 1 , wherein input data comprises fluid data indicative of a fluid received by the biological structure, and
wherein the operations further comprise rendering the physics modeling data of the biological structure based on the fluid data and the one or more physical characteristics of the plurality of connected chambers of the biological structure on the further three-dimensional model.
9. A method, comprising:
generating, by a system comprising a processor, a three-dimensional model of a biological structure based on medical imaging data, wherein generating the three-dimensional model comprises integrating (i) a first three-dimensional model associated with a first portion of the biological structure and a first machine learning process and (ii) a second three-dimensional model associated with a second portion of the biological structure and a second machine learning process to generate a further three-dimensional model, the first three-dimensional model and the second three-dimensional model defining one or more physical characteristics of a plurality of connected chambers of the biological structure, and wherein the first machine learning process is different from the second machine learning process;
predicting, by the system, one or more flow or transfer characteristics from the first portion of the biological structure to the second portion of the biological structure based on input data, the first machine learning process, and the second machine learning process; and
providing, by the system, a three-dimensional health assessment environment associated with the further three-dimensional model, wherein the three-dimensional health assessment environment renders physics modeling data of the biological structure based on the input data and the one or more flow or transfer characteristics of the biological structure on the further three-dimensional model.
10. The method of claim 9 , wherein the rendering comprises providing a three-dimensional design environment associated with the further three-dimensional model.
11. The method of claim 9 , wherein the predicting comprises predicting thermal characteristics and predicting physics behavior based on the input data and the machine learning process, and wherein the rendering comprises rendering the physics modeling data of the biological structure based on the thermal characteristics and the physics behavior.
12. A non-transitory computer readable storage medium comprising instructions that, in response to execution, cause a system comprising a processor to perform operations, comprising:
generating, by a system comprising a processor, a three-dimensional model of a biological structure based on medical imaging data, wherein generating the three-dimensional model comprises integrating (i) a first three-dimensional model associated with a first portion of the biological structure and a first machine learning process and (ii) a second three-dimensional model associated with a second portion of the biological structure and a second machine learning process to generate a further three-dimensional model, the first three-dimensional model and the second three-dimensional model defining one or more physical characteristics of a plurality of connected chambers of the biological structure, and wherein the first machine learning process is different from the second machine learning process;
predicting, one or more flow or transfer characteristics from the first portion of the biological structure to the second portion of the biological structure based on input data, the first machine learning process, and the second machine learning process; and
providing, a three-dimensional health assessment environment associated with the further three-dimensional model, wherein the three-dimensional health assessment environment renders physics modeling data of the biological structure based on the input data and the one or more flow or transfer characteristics of the biological structure on the further three-dimensional model.
13. The non-transitory computer readable storage medium of claim 12 , wherein the performing the first machine learning process comprises performing the first machine learning process based on data associated with the second machine learning process.