Citation: | LIU Yonggang, SU Lijun. Responses of Ureteral Pain Caused by Small-Sized Kidney Stones Under Fluid-Structure Coupling[J]. Applied Mathematics and Mechanics, 2024, 45(6): 735-752. doi: 10.21656/1000-0887.450095 |
The ureteral pain caused by kidney stones has long tormented humans and seriously affected their quality of life. However, currently, in clinical practice, due to the lack of quantitative analysis of the interaction between kidney stones and ureters, urologists are unable to develop precise personalized treatment and pain relief plans for different patients. In response to this issue, small-sized kidney stones were taken as an example and to analyze the interaction behavior between small-sized kidney stones entering the ureteral lumen and the ureter with a fluid-structure coupling finite element method based on the coupled Eulerian-Lagrangian (CEL) algorithm. With the established ureteral pain model, the ureteral pain caused by small-sized kidney stones was quantitatively studied. The finite element analysis results indicate that, when the stone diameter is smaller than the inner diameter of the ureter, the stone will dynamically contact the ureter under peristalsis of the ureter wall, causing dynamic stress on the inner wall of the ureter. The stone moving speed will increase with the peristaltic amplitude of the ureteral wall, but the contacting probability between the stone and the ureter will decrease, and the contacting stress on the ureteral wall will decrease as well. The stress results were input into the ureteral pain model to calculate the corresponding central transmission neuron cell membrane potential. The model results show that, the change in the pain level over time was similar to the trend of dynamic stresses over time. In the case of alternating stress changes, the pain level would not decrease below the pain threshold as the stress drops to 0, showing inconformity between the pain level and the stress level. The results can be combined with existing medical imaging technologies in clinical practices, as well as big data and artificial intelligence technologies in the field of computer science. The research provides a theoretical basis for personalized and accurate diagnosis of the condition of stone patients, quantitative evaluation of patient pain levels, and the development of personalized treatment plans for precise medical clinical strategies.
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