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The CFD and ML workflow. Step 1) Run 90 simulations with a combination of fifteen intra-oral pressure datasets, six viscosity values, and density values. Step 2) Select 27 of the CFD simulations for training the model and use 63 of the simulations for testing the model. Step 3) Test the robustness of the ML model.
Published Online: March 26, 2025
Fig. 2 The CFD and ML workflow. Step 1) Run 90 simulations with a combination of fifteen intra-oral pressure datasets, six viscosity values, and density values. Step 2) Select 27 of the CFD simulations for training the model and use 63 of the simulations for testing the model. Step 3) Test the rob... More about this image found in The CFD and ML workflow. Step 1) Run 90 simulations with a combination of f...
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Computational fluid dynamics simulations results. (a) Contour plot showing a sample of the VFR computed in the transverse slice before the bifurcation point. (b) Circumferentially averaged WSS computed in the transverse slice before the bifurcation point at a density of 1028 and viscosity of 0.01 Pa·s. ((c) and (e)) Contour plots showing how VFR relates to viscosity and density. ((d) and (f)) Contour plots showing how WSS relates to viscosity and density.
Published Online: March 26, 2025
Fig. 3 Computational fluid dynamics simulations results. ( a ) Contour plot showing a sample of the VFR computed in the transverse slice before the bifurcation point. ( b ) Circumferentially averaged WSS computed in the transverse slice before the bifurcation point at a density of 1028 and viscosi... More about this image found in Computational fluid dynamics simulations results. ( a ) Contour plot showin...
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Model architecture ((a) and (c)) Detailed architecture of the type 1 depth 1 model (T1D1). T1 model has one hidden layer containing 20 neurons. ((b) and (d)) Detailed architecture of the type 2 depth 3 model (T2D3). The T2 model has three hidden layers with a total of 128, 32, and 8 neurons for each layer. The shape of the output depends on the number of neurons specified in the dense layer.
Published Online: March 26, 2025
Fig. 4 Model architecture (( a ) and ( c )) Detailed architecture of the type 1 depth 1 model (T1D1). T1 model has one hidden layer containing 20 neurons. (( b ) and ( d )) Detailed architecture of the type 2 depth 3 model (T2D3). The T2 model has three hidden layers with a total of 128, 32, and 8... More about this image found in Model architecture (( a ) and ( c )) Detailed architecture of the type 1 de...
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(a) and (b) VFR results from machine learning compared to CFD simulations with an associated Blandt–Altman plot for the candidate model from T1 cases (no early stopping point with mini-batching and decaying learning rate case). ((c) and (d)) VFR results from machine learning compared to CFD simulations with an associated Blandt–Altman plot for the candidate model for the candidate model from T2 cases (no early stopping point with mini-batching and decaying learning rate case).
Published Online: March 26, 2025
Fig. 5 ( a ) and ( b ) VFR results from machine learning compared to CFD simulations with an associated Blandt–Altman plot for the candidate model from T1 cases (no early stopping point with mini-batching and decaying learning rate case). (( c ) and ( d )) VFR results from machine learning compare... More about this image found in ( a ) and ( b ) VFR results from machine learning compared to CFD simulatio...
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((a) and (b)) WSS results from machine learning compared to CFD simulations with an associated Blandt–Altman plot for the candidate model from T1 cases (no early stopping point with mini-batching and decaying learning rate case). ((c) and (d)) WSS results from machine learning compared to CFD simulations with an associated Blandt–Altman plot for the candidate model from T2 cases (no early stopping point with no batching and decaying learning rate case).
Published Online: March 26, 2025
Fig. 6 (( a ) and ( b )) WSS results from machine learning compared to CFD simulations with an associated Blandt–Altman plot for the candidate model from T1 cases (no early stopping point with mini-batching and decaying learning rate case). (( c ) and ( d )) WSS results from machine learning compa... More about this image found in (( a ) and ( b )) WSS results from machine learning compared to CFD simulat...
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(a) Morphological features of a typical aneurysm. (b) Instantaneous WSS vector field, colored by magnitude, on aneurysm sac. (c) Line integral convolution of the corresponding WSS vector field, with spherical glyphs highlighting selected locations and types of fixed points.
Published Online: March 26, 2025
Fig. 1 ( a ) Morphological features of a typical aneurysm. ( b ) Instantaneous WSS vector field, colored by magnitude, on aneurysm sac. ( c ) Line integral convolution of the corresponding WSS vector field, with spherical glyphs highlighting selected locations and types of fixed points. More about this image found in ( a ) Morphological features of a typical aneurysm. ( b ) Instantaneous WSS...
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Classification of fixed points based on the eigenvalues of the linearized vector field. Outward arrow directions for (b) and (c) correspond to unstable (cf. inward for stable) node and focus, respectively. (a) Saddle, (b) node, and (c) focus.
Published Online: March 26, 2025
Fig. 2 Classification of fixed points based on the eigenvalues of the linearized vector field. Outward arrow directions for ( b ) and ( c ) correspond to unstable (cf. inward for stable) node and focus, respectively. ( a ) Saddle, ( b ) node, and ( c ) focus. More about this image found in Classification of fixed points based on the eigenvalues of the linearized v...
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Impact of flat mapping scheme on fixed points: (a) Sac showing isodistances from the neck plane, with artificially placed fixed points, (b) aneurysm-sac top-view, (c) LSCM, and (d) SLIM
Published Online: March 26, 2025
Fig. 3 Impact of flat mapping scheme on fixed points: ( a ) Sac showing isodistances from the neck plane, with artificially placed fixed points, ( b ) aneurysm-sac top-view, ( c ) LSCM, and ( d ) SLIM More about this image found in Impact of flat mapping scheme on fixed points: ( a ) Sac showing isodistanc...
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Preservation of surface morphology on flat maps. (a) Original sac surface with artificial fixed points placed on/near surface features; (b) Voronoi diagram of the sac surface, with red highlighting the Voronoi core; (c) smoothed sac reconstructed from the Voronoi core (red), inside original sac surface; (d) distance field between original and smoothed sac, color-coded using a terrain analog scale; and (e) flattened sac with distance field colored and elevated. Note location of artificial fixed points near the surface features preserved from (a).
Published Online: March 26, 2025
Fig. 4 Preservation of surface morphology on flat maps. ( a ) Original sac surface with artificial fixed points placed on/near surface features; ( b ) Voronoi diagram of the sac surface, with red highlighting the Voronoi core; ( c ) smoothed sac reconstructed from the Voronoi core (red), inside or... More about this image found in Preservation of surface morphology on flat maps. ( a ) Original sac surface...
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(a) Visualization of fixed points at three different time points: early systole (t1); peak systole (t2); and diastole (t3). Each time point is represented in 3D with at least two orientations, alongside their corresponding flattened views for clarity. The notation cn indicates cluster numbers. (b) Conventional linear spacetime representation maintaining a few selected planes for reference. (c) Carousel representation, retaining a single reference plane. Colors identify fixed-point types: saddle (pink); node (cyan); and focus (yellow).
Published Online: March 26, 2025
Fig. 5 ( a ) Visualization of fixed points at three different time points: early systole (t1); peak systole (t2); and diastole (t3). Each time point is represented in 3D with at least two orientations, alongside their corresponding flattened views for clarity. The notation cn indicates cluster n... More about this image found in ( a ) Visualization of fixed points at three different time points: early s...
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Illustration of work flow in creating the background torus: (a) initial view, (b) ray-casting to mark the camera-facing faces, (c)marked faces, (d) opened torus after removal of camera-facing faces (side-on), and in (e) final front-on view of the torus. (a) Initial background torus, (b) side-view of camera ray tracing, (c) marked faces for removal, (d) side-view post face removal, and (e) front view of the final torus.
Published Online: March 26, 2025
Fig. 6 Illustration of work flow in creating the background torus: ( a ) initial view, ( b ) ray-casting to mark the camera-facing faces, ( c )marked faces, ( d ) opened torus after removal of camera-facing faces (side-on), and in ( e ) final front-on view of the torus. ( a ) Initial background to... More about this image found in Illustration of work flow in creating the background torus: ( a ) initial v...
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Fixed-point carousels for cases A, B, and C. Shown from left to right for each case are the original lumen geometries with sac highlighted (note that case A has more surface undulations compared to cases B and C); the carousel viewed from the “orbital” plane slightly tilted; and an overhead view. Note the alphanumeric glyph labels, which are referred to in the text. See text for description of flow waveform and its colored phases, shown at top right. (a) Case A, (b) case B, and (c) case C.
Published Online: March 26, 2025
Fig. 7 Fixed-point carousels for cases A, B, and C. Shown from left to right for each case are the original lumen geometries with sac highlighted (note that case A has more surface undulations compared to cases B and C); the carousel viewed from the “orbital” plane slightly tilted; and an overhead... More about this image found in Fixed-point carousels for cases A, B, and C. Shown from left to right for e...
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