Effective Prediction of Drug Transport in a Partially Liquefied Vitreous Humor: Physics-informed Neural Network Modeling for Irregular Liquefaction Geometry.

Autor: Zhang S; University of Southern California, Los Angeles, CA, USA.; Convergent Science, Madison, WI, USA., Penkova A; University of Southern California, Los Angeles, CA, USA., Jia X; University of Cincinnati, Cincinnati, OH., Sebag J; University of California, Los Angeles, Los Angeles, CA, USA., Sadhal SS; University of Southern California, Los Angeles, CA, USA.
Jazyk: angličtina
Zdroj: Engineering applications of artificial intelligence [Eng Appl Artif Intell] 2024 Dec; Vol. 138 (Pt A). Date of Electronic Publication: 2024 Sep 19.
DOI: 10.1016/j.engappai.2024.109262
Abstrakt: As the medium for intravitreal drug delivery, the vitreous body can significantly influence drug delivery because of various possible liquefaction geometries. This work innovatively proposes a varying-porosity approach that is capable of solving the pressure and velocity fields in the heterogeneous vitreous with randomly-shaped liquefaction geometry, validated with a finite difference model. Doing so enables patient-specific treatment for intravitreal drug delivery and can significantly improve treatment efficacy. A physics-informed neural network (PINN) model is also established for the simulation, and three cases are used for validation. Despite limited information, the PINN model, together with the varying-porosity approach, captured fluid and drug transport in the partially liquefied vitreous. This opens the possibility for optimizing intravitreal drug delivery based on ultrasonography in clinical practice.
Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Databáze: MEDLINE
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