Computer-implemented method for computational simulation surgical planning for left atrial appendage occlusion

Abstract

The present disclosure relates to a computer-implemented method, system and computer program for computational simulation of surgical planning for left atrial appendage occlusion (LAAO) wherein the risk of device-related thrombosis occurring prior to LAAO surgical intervention is assessed. The positioning of the 3D model of the left atrial appendage occluder is determined from the anatomical features of the patient's left atrium, a first compression value and a structural simulation providing a second compression value. The method further calculates a risk score of device-induced thrombosis by analyzing computational fluid dynamics calculations in combination with the patient's medical parameters. This risk assessment helps to optimize the positioning of the LAAO device, potentially reducing the incidence of postoperative thrombosis.

Description

Technical Field

[0001] This invention relates to the fields of medical technology and diagnostics, and more particularly to a computer-aided method for cardiovascular surgical planning that includes risk assessment. More specifically, this invention relates to a computer-based method for computational simulation surgical planning for left atrial appendage occlusion. Background Technology

[0002] Atrial fibrillation (AF) is the most common arrhythmia in clinical practice and has a significant impact on public health due to its association with ischemic stroke. The left atrial appendage (LAA) is a common site of thrombosis in AF patients, and if the clot breaks off and travels to the brain, it can lead to stroke. The complex anatomy of the LAA, including its diverse morphologies, complicates stroke risk management in AF patients.

[0003] When anticoagulation therapy is contraindicated or poses a significant risk, clinicians may resort to left atrial appendage occlusion (LAAO) surgery. These interventions involve the transvenous implantation of devices such as the commercially available Watchman or Amplatzer Amulet, guided by imaging modalities such as fluoroscopy and echocardiography.

[0004] The success of LAAO depends on the accurate selection and positioning of the device to ensure complete occlusion, which requires careful planning and detailed analysis of the patient's left atrial anatomy, as well as the device's geometry and physical properties. This planning process typically requires intensive computational resources to perform adequate simulations, making it impractical to test different configurations. Another approach, in an effort to accelerate the process, has oversimplified it, at the cost of failing to adequately reproduce the physical interaction between the LAA occluder and the patient's left atrial appendage. Therefore, current planning procedures cannot provide adequate LAAO surgical planning.

[0005] Furthermore, implantation of a left atrial appendage occluder carries an inherent risk of thrombosis due to device-related factors. Incorrect device size selection, suboptimal placement, or inappropriate device morphology can lead to inadequate closure of the atrial appendage and / or turbulent blood flow, such as recirculation and stasis. These hemodynamic alterations around the device can then become sites of thrombus formation. Additionally, the material or shape of the occluder can induce clot formation by activating the coagulation cascade. Therefore, precise device selection and accurate placement are crucial for preventing post-implantation thrombotic complications. Moreover, achieving successful closure without residual blood flow into the LAA is also critical; inadequate closure may require further intervention and poses a continuing risk of thrombosis. Currently, all these aspects are not considered in the surgical planning for LAAO. Instead, only simple geometric relationships are considered when selecting the location and type of occluder for LAAO surgery, neglecting the step of finding the optimal device location to minimize the risk of associated hemodynamic complications.

[0006] Current attempts to address these problems often rely on simplified blood flow velocity measurements, which fail to adequately represent the complexity of atrial hemodynamics. For example, state-of-the-art computational fluid dynamics (CFD) simulations take only a limited number of parameters as input, which cannot accurately, nor in a personalized and patient-specific manner, characterize blood flow in the left atrium of a patient. Similarly, analyses of imaging techniques used to obtain a 3D model representation of a patient's left atrium (LAA) are incomplete and omit relevant features for properly assessing key mechanical and hemodynamic characteristics in LAAO surgical procedure planning. Therefore, these oversimplified and generic CFD simulations and imaging analyses fail to extract all the information needed to assess the most relevant parameters when evaluating the durability of device placement and / or the risk of device-related thrombosis in a particular patient. These limited and non-specific CFD simulations and 3D analyses, in turn, result in a lack of output parameters that could serve as reliable indicators of thrombosis and device safety.

[0007] Furthermore, the technical requirements of LAAO necessitate advanced training and skills, highlighting the gaps in current approaches. There is an unmet need for computational tools that can provide detailed, rapid, objective, and patient-specific characterizations of LAA morphology and its associated hemodynamics to guide the selection and planning of LAAO interventions, while considering associated risks such as LAAO-related clot formation or potential damage to the LAA wall due to interaction with the occluder.

[0008] Therefore, a planning method for LAAO surgery that takes all these important aspects into account is needed. This method should provide a rapid, but most importantly, reliable estimate of the safe location of the device structure, enabling clinicians to evaluate the optimal approach, and should also accurately simulate blood flow, considering the most relevant parameters to estimate the associated risk of thrombosis. Summary of the Invention

[0009] A first aspect of the present invention relates to a computer-implemented method for computational simulation surgical planning of left atrial appendage (20) occlusion (LAAO), the method comprising the steps of:

[0010] a. Generate a 3D model of the patient's left atrium (30) based on the patient's medical images, or, in some embodiments, receive a 3D model of the patient's left atrium (30);

[0011] b. Calculate one or more internal diameters of the lumen of the left atrial appendage (LAA) (20), wherein the left atrial appendage (20) is contained in a 3D model of the patient's left atrium (30) generated in step a);

[0012] c. Positioning at least partially within the left atrial appendage occluder (10) using a 3D model of the occluder (10), wherein the position is further determined based on one or more parameters selected from a list including: one or more diameters selected from one or more diameters calculated in step b), the location of the landing zone contained in the 3D model of the left atrial appendage (20), the first compression value of the occluder (10), and anatomical features extracted from a 3D model of the patient's left atrium (30), preferably wherein the anatomical features include the shape and location of the opening and / or the shape and location of the fossa ovalis;

[0013] d. Perform structural simulation to calculate a second compression value of the left atrial appendage occluder (10) based on the interaction between the left atrial appendage occluder (10) and the inner wall of the left atrial appendage (20);

[0014] e. If the second compression value calculated in step d) is within an acceptable range, proceed to step f); and if the value of the second compression value calculated in step d) is outside the acceptable range, return to step c) and perform step c) again with one or more modified parameters, wherein the parameters are preferably the landing area, the first compression value and / or the one or more diameters.

[0015] f. Using parameters derived from computational fluid dynamics (CFD) calculations and the patient's medical parameters, calculate a set of thrombosis risk factors;

[0016] g. Calculate a risk score for thrombosis associated with the left atrial appendage (20) occlusion location in step c), wherein the risk score is based at least on the thrombosis risk factors calculated in step f);

[0017] In a preferred embodiment of the method of the first aspect of the present invention, if the risk score calculated in step g) is higher than the risk threshold, the landing area location, the first compression value, and / or one or more diameters selected from one or more diameters calculated in step b) are modified, and step c) is executed again.

[0018] In a preferred embodiment of the method of the present invention, if the risk score calculated in step g) is higher than the risk threshold, the 3D model of the left atrial appendage occluder (10) is shifted to a different location based on a database of CFD simulations performed on LAAO from other patients, and step d) is repeated.

[0019] In a preferred embodiment of the method of the present invention, the patient's medical image is generated from one or more imaging techniques from a list including: ultrasound (US) imaging, transthoracic echocardiography (TTE), computed tomography (CT) imaging, magnetic resonance imaging (MRI) imaging, 3D rotational angiography (3DRA) imaging, mitral valve pulsation ultrasound imaging, and pulmonary vein pulsation ultrasound imaging; preferably, the 3D model of the patient's left atrium (30) is generated using an input image generated by mitral valve pulsation US imaging and any one of MRI, 3DRA, or CT imaging; more preferably, the 3D model of the patient's left atrium (30) is generated using an input image generated by mitral valve pulsation US, pulmonary valve pulsation US, and any one of MRI, 3DRA, or CT imaging.

[0020] In a preferred embodiment of the method of the present invention, the first compression value is based on the volume relationship between the volume defined by the unmodified 3D model of the occluder (10) and the volume defined by the 3D model of the occluder (10) when the volume of the 3D model of the occluder (10) is modified to fit the inner wall size of the 3D model of the patient's left atrium (30).

[0021] In a preferred embodiment of the method of the present invention, the location of the LAA occluder determined in step c) is further determined based on the patient's clinical data, preferably wherein the patient's clinical data includes one or more elements selected from a list including: CHADS2-VASc, HAS-Bleed, and ultrasound data; more preferably the clinical data also includes one or more elements from a list including: left atrial pressure, hemoglobin level, BNP, C-protein level, troponin level, eGFR, ECG data, and creatinine level; and even more preferably wherein the patient's clinical data includes all elements from both lists.

[0022] In a preferred embodiment of the method of the present invention, the location of the LAA occluder determined in step c) is further determined based on at least one or more elements from a list including: the landing zone location contained in a 3D model of the left atrial appendage (20), a database of left atrial appendage occluders (10) located in the left atrial appendage (20) of other patients, the mechanical and volumetric characteristics of the catheter associated with the location of the left atrial appendage occluder (10), and ultrasound images; preferably wherein the location of the LAA occluder is determined based on all elements of the list.

[0023] In a preferred embodiment of the method of the present invention, the patient's medical parameters include CHADS2-VASc and / or HAS-Bleed.

[0024] In a preferred embodiment of the method of the present invention, the thrombosis risk factors derived from CFD calculations include one or more elements from a list comprising: mean blood flow velocity in the pulmonary ridge region, presence and mean blood flow velocity of eddies, and / or stagnant flow in the pulmonary ridge region and on the device surface, amount of particulate adhesion, and thrombogenic hemodynamic parameters; preferably wherein the thrombosis risk factors derived from CFD calculations include all elements from the list.

[0025] In a more preferred embodiment of the method of the present invention, the thrombotic hemodynamic parameters include endothelial cell activation potential value, and preferably also include hypercoagulable state and / or blood retention time value.

[0026] In one or even a more preferred embodiment of the method of the present invention, the one or more thrombotic risk factors calculated based on the patient's thrombogenicity index are determined by comparing each thrombogenicity index value with one or more risk thresholds for each thrombogenicity index.

[0027] In a preferred embodiment of the method of the present invention, the CFD calculation takes one or more physiological parameters from a list including: pulmonary venous pressure, left atrial pressure, Doppler ultrasound data, and hematocrit level as input, preferably wherein the CFD calculation takes all elements in the list as input.

[0028] In a preferred embodiment of the method of the present invention, the CFD calculation is performed under simulated motion of the patient's left atrium (30), preferably wherein the motion is simulated using the Arbitrary Lagrange-Euler method (ALE).

[0029] In a more preferred embodiment of the method of the present invention, the simulation of motion includes the following steps:

[0030] h. Compare the 3D model of the patient's left atrium (30) with a database of 3D models of the left atrium (30) in motion of other patients obtained from dynamic computed tomography images of the left atrium (30) in motion of other patients;

[0031] i. Select the most similar 3D model of the left atrium (30) from the database;

[0032] j. Motion interpolation extracted from the 3D model of the most similar left atrium (30) selected in step b) is applied to the geometry of the patient's left atrium (30); and

[0033] k. The motion of the patient's left atrium (30) is simulated using the interpolation from step c) to the motion of the patient's left atrium (30), the arbitrary Lagrange-Euler method (ALE), and the patient's CFD physiological parameters as inputs.

[0034] A second aspect of the invention relates to a computer program comprising instructions that, when executed by a computing device, cause the computing device to perform the method described in any of the preceding claims.

[0035] A third aspect of the present invention relates to a data processing apparatus, comprising a processing unit and a storage unit, wherein the storage unit includes instructions that, when executed by the processing unit, configure the data processing apparatus to perform the method of any one of claims 1 to 14. Attached Figure Description

[0036] To facilitate a better understanding of this disclosure and to demonstrate how to implement it, reference will now be made only by way of example to the accompanying schematic diagrams, in which:

[0037] Figure 1 A 3D model of the left atrium and occluder according to one or more embodiments of the present invention is shown.

[0038] Figure 2 shows a 3D model of an occluder positioned within a 3D model of the left atrial appendage according to one or more embodiments of the present invention. More specifically, Figure 2A and Figure 2B An example of a plugger placed inside the LAA based on a first compression value is shown. The selected location of the LAA plugger (10) may be modified based on changes in the landing zone, selection of different calculated diameters of the LAA (20), or different calculated values ​​of the first compression value associated with different locations.

[0039] Figure 3A A simulation of blood flow in the left atrium is shown according to one or more embodiments of the present invention, wherein the proximal / distal end of the occluder is positioned inside the left atrial appendage.

[0040] Figure 3B A blood flow simulation is shown with the distal end of the occluder positioned within the left atrium inside the left atrial appendage, according to one or more embodiments of the present invention.

[0041] Figure 4 Three different views (4a, 4b and 4c) of a 3D model of a left atrial appendage occluder having vertical and horizontal sections forming a 3D mesh, according to one or more embodiments of the present invention, are shown.

[0042] Figure 5 A flowchart of a method according to one or more embodiments of the present invention is shown.

[0043] Figure 6 A flowchart of a method according to one or more embodiments of the present invention is shown. Detailed Implementation

[0044] definition

[0045] It should be noted that, as used herein, the singular forms “a,” “an,” and “the” include plural references unless the context clearly indicates otherwise. Furthermore, unless otherwise stated, the term “at least” preceding a series of elements should be understood to refer to each element in that series. Those skilled in the art will recognize or be able to determine many equivalents of the specific embodiments of the invention described herein using no more than conventional experimentation. These equivalents are intended to be covered by the invention.

[0046] As used herein, the conjunction “and / or” connecting multiple listed elements is understood to encompass both individual options and combined options. For example, when two elements are connected by “and / or”, the first option refers to the applicability of the first element without the second element. The second option refers to the applicability of the second element without the first element. The third option refers to the applicability of both the first and second elements. Any of these options is understood to fall within the meaning of this term and therefore satisfies the requirement of the term “and / or” as used herein. The simultaneous applicability of more than one option is also understood to fall within the meaning of this term and therefore satisfies the requirement of the term “and / or”.

[0047] Throughout this specification and the following claims, unless the context otherwise requires, the word “comprising” and its variations such as “comprising” (third person singular) and “comprising” (present participle) shall be understood to imply inclusion of the stated integers or steps or a group of integers or steps, but not to exclude any other integers or steps or a group of integers or steps. When used herein, the term “comprising” may be replaced by the terms “including” or “containing” or sometimes, when used herein, by the term “having”. Any of the foregoing terms (comprising, including, containing, having) may be replaced by the term “consisting of” when used herein in the context of aspects or embodiments of the invention, but is less preferred.

[0048] When used herein, "consisting of" excludes any element, step, or component not specified in the elements of the claim. When used herein, "consisting substantially of" does not exclude materials or steps that do not substantially affect the essential and novel features of the claim.

[0049] The term "left atrial appendage (LAA)" refers to a small sac within the wall of the left atrium of the heart. It is known to be a potential site of thrombus formation, particularly in patients with atrial fibrillation. The structure and size of the LAA can vary significantly between individuals, thus influencing the selection and planning of occlusion devices.

[0050] The term "left atrial appendage occlusion (LAAO)" refers to a medical procedure or its effect aimed at closing the left atrial appendage to isolate it from the left atrium, thereby preventing thrombus migration into the bloodstream and reducing the risk of stroke. In the context of this invention, occlusion is achieved by deploying an occluder device.

[0051] The term "computational simulation-based left atrial appendage occlusion (LAAO) surgical planning" in the context of this invention refers to the preoperative process of developing an LAAO strategy using computer-based simulations. This involves using computational models and algorithms to analyze patient-specific cardiac anatomy and predict optimal occluder placement and size selection, thereby facilitating a personalized and precise approach to the occlusion procedure and aiming to execute it prior to the actual surgical intervention.

[0052] In the context of this invention, the term "compression value" refers to a numerical value indicating the degree to which the volume of an object is compressed or condensed. This value can be derived from various evaluation methods; for example, it can be obtained by calculating a ratio indicating volume change to approximate compression, or alternatively, it can be determined by a physical simulation that calculates the forces and pressures applied to the object to achieve a specific level of compression.

[0053] In the context of this invention, the term "structural simulation" refers to a computational analysis method for predicting how a physical structure will behave under various conditions, such as applied pressure or force. It involves using mathematical models and algorithms to replicate the structural characteristics and responses of a device in a virtual environment, while considering a set of boundary conditions and parameters crucial to the accuracy of the simulation. This simulation can, for example, be used to calculate compression values ​​by simulating the physical interactions and deformations that occur during compression.

[0054] In the context of this invention, the term "occluder" refers to a left atrial appendage occluder, a device designed to close the left atrial appendage (LAA) of the heart to reduce the risk of thrombosis and subsequent stroke in patients, particularly those with atrial fibrillation. The occluder is positioned inside the left atrial appendage to prevent blood flow into at least a portion of it. Commercial examples of occluders include the Watchman and Amplatzer Amulet models.

[0055] In the context of this invention, the term "acceptable range" refers to a defined numerical range for a given parameter, established based on standards related to health, safety, or compliance with medical guidelines and recommendations. This range is determined by upper and lower thresholds to ensure that the parameter remains within levels considered safe and effective in medical practice. Acceptable ranges are not static; they can evolve over time as medical standards are reviewed and updated, reflecting the latest research, clinical findings, or regulatory requirements.

[0056] In the context of this invention, the term "thrombosis risk factor" refers to any variable or condition that may affect the likelihood of thrombosis specifically related to the placement of an occluder in the left atrial appendage. Within the scope of the LAAO program, such factors can be derived from, but are not limited to, hemodynamic parameters obtained from computational fluid dynamics simulations and patient clinical data. Therefore, the thrombosis risk factor can be valued based on the parameters or clinical data from which it originates, wherein such values ​​may be, for example, weighted values ​​or binary values.

[0057] In the context of this invention, the term "risk score" refers to a value calculated based on a set of thrombosis risk factors. This score provides a measure of the potential risk of thrombosis associated with LAAO. A high-risk score can prompt clinicians to consider different occluder sizes, types, or locations to optimize patient outcomes. Methods for calculating the risk score may involve, for example, summarizing individual risk factors, but the precise algorithm can vary depending on specific clinical protocols, medical standards, and research data.

[0058] In the context of this invention, the term "landing zone" refers to the designated area within the left atrial appendage where an occluder device is initially intended to be placed. The final positioning of the occluder can be adjusted during LAAO procedure planning to ensure occluder stability and minimize the risk of thrombosis. The landing zone serves as the initial target for the intervention, providing a reference point for potential adjustments during LAAO procedure. During LAAO planning, the landing zone may be repositioned once or multiple times based on various factors, such as calculated compression values ​​and / or risk scores.

[0059] describe

[0060] Current LAAO surgical planning methods are limited by their oversimplification, often neglecting complex hemodynamics and patient-specific anatomical details. This simplification leads to inaccurate assessment of thrombotic risk during LAAO planning. Furthermore, current computational models, including state-of-the-art computational fluid dynamics (CFD) simulations, lack sufficient precision to accurately reflect individualized hemodynamics within the left atrium and fail to precisely assess the risk of thrombosis resulting from LAAO surgery during the planning phase, which could potentially lead to stroke.

[0061] In this invention, as supported by Example 1, we provide a computational simulation planning method for left atrial appendage occlusion procedures that addresses the aforementioned problems and avoids adverse consequences such as device-associated thrombosis (DRT) or peri-device leakage (PDL). As shown in Example 1, the method according to the invention allows clinicians to select low-risk locations for device placement through analysis and calculation of anatomical, hemodynamic, and clinical data. Specifically, Example 1 shows that a lower risk of device-associated thrombosis corresponds to a proximal placement (…). Figure 3A ), while configuring on the remote end ( Figure 3B Blood clots are more likely to form in these environments. For example, Figure 3B The stagnation lines shown indicate recirculation and a lighter color, which indicates a decrease in velocity near the device wall and is associated with risk factors that may indicate a higher probability of thrombosis.

[0062] This method is personalized based on each patient's morphological and clinical data and allows clinicians to interact with the device (LAA occluder) positioning. This is made possible by an algorithm capable of rapidly and reliably calculating a first compression value, a significant innovation in LAAO surgical planning. Furthermore, a mechanically more precise second compression value is calculated via structural simulation to validate the location, and computational fluid dynamics simulations are performed to assess relevant parameters that will be used as thrombosis risk factors. Both steps are performed in a patient-person-specific manner, and, most importantly, this yields unparalleled results by taking into account a novel combination of relevant morphological and preferably clinical parameters as input. Equally important is selecting the correct boundary conditions for the simulations performed in this method, which may also involve using a database of previous LAAO procedures. Finally, the method uses the combination of thrombosis risk factors to assess the thrombosis risk associated with a specific LAA occluder location in a manner far more precise than current or even experimental methods.

[0063] Each embodiment disclosed herein is considered applicable to every other disclosed embodiment. Therefore, all combinations of the various elements described herein are within the scope of this invention. It should also be understood that, unless expressly indicated to the contrary, in any method claimed herein that includes more than one step or action, the order of the steps or actions of the method is not necessarily limited to the order in which the steps or actions of the method are listed.

[0064] A first embodiment of the first aspect of the present invention relates to a computer-implemented method (see...) Figure 5 The flowchart shown (with numbered steps) illustrates the computational simulation surgical planning for left atrial appendage (20) occlusion surgery. See also Figure 1 Figures 2 and 3 show 3D representations of the left atrial appendage (20) and occluder (10) according to one or more embodiments of the present invention. The method includes the following steps (according to...) Figure 5 Numbered 1, 2, 3, 4, 5, 6, and 7, which are equivalent to steps a, b, c, d, e, f, g, and h, respectively.

[0065] Step (1) (or a) includes generating a 3D model of the patient's left atrium (30) based on the patient's medical images, or receiving a 3D model of the patient's left atrium (30). It should be noted that the 3D model of the patient's left atrium (30) is always generated based on the patient's medical images; however, for the purposes of this invention, the 3D model may be generated in situ or pre-generated from the medical images and then provided for the methods of implementing this invention. It should also be noted that the process of generating a 3D model of the patient's left atrium (30) includes acquiring medical images from which a 3D representation can be constructed. In some embodiments, computed tomography (CT) or magnetic resonance imaging (MRI) may be utilized, wherein the images are preferably acquired no more than one year prior to model generation to ensure the relevance and accuracy of the data. However, alternative imaging modalities known to those skilled in the art (e.g., but not limited to X-ray angiography or 3D echocardiography) are also within the scope of this invention, which may also provide sufficient anatomical detail for the reconstruction of the 3D model. In generating a 3D model of the left atrium (30), the method may involve using artificial intelligence, particularly neural network algorithms, to process the acquired images. These neural networks are trained to recognize anatomical structures and are able to accurately segment the left atrium (30) from the surrounding cardiac anatomy. The use of neural networks not only improves the accuracy of the model but also significantly reduces the time required to generate the model, enabling the model to be completed in minutes. The algorithm may include various forms of machine learning algorithms or neural networks, such as convolutional neural networks (CNNs), feedforward networks (FFNs), or recurrent neural networks (RNNs). Alternatively, the method may employ manual, semi-automatic, or fully automatic segmentation techniques to assist in creating the 3D model. The segmentation process may include, for example, thresholding, region growing, or edge detection techniques. In some embodiments of the method of the present invention, the 3D model may be presented to a user or clinician on any display device, and the computing device performing the method may also provide the user or clinician with the possibility of modifying the 3D model and / or the segmentation.

[0066] Step (2) (or b) includes calculating one or more internal diameters of the lumen of the left atrial appendage (LAA) (20), wherein the left atrial appendage (20) is contained in a 3D model of the patient's left atrium (30) generated in step (a). Preferably, it also includes calculating a central line along the left atrial appendage (20).

[0067] It should be noted that the lumen refers to the internal space through which blood flows within the LAA (20); characterized in that, due to the unique anatomical structure of the LAA (20), the lumen may have an irregular shape and different diameters along its length. In some embodiments, the method may calculate the diameter at multiple cross-sections along the calculated centerline of the LAA (20). The calculated diameter may include, but is not limited to, the shortest and longest diameters intersecting the centerline at a given point. This calculation may be repeated at multiple points along the centerline, thereby providing a detailed profile of the lumen dimensions. Furthermore, the diameter may also be calculated as a series of symmetrically spaced radii extending from the centerline to the inner wall of the LAA (20), capturing the geometry of the lumen from multiple angles.

[0068] Furthermore, given the irregularity of the LAA(20) lumen, the obtained diameter can represent the average diameter, which can be calculated using statistical methods that provide representative diameters for different parts of the lumen. Alternatively, statistically significant weighted diameters can be calculated, taking into account variations in lumen size due to physiological or pathological conditions. These diameters can be determined using algorithms capable of handling non-uniform geometries and adapted to calculate diameters from irregular shapes.

[0069] Step (3) (or c) involves positioning the 3D model of the left atrial appendage occluder (10) at least partially within the left atrial appendage (20). The positioning or location is determined based on one or more parameters selected from a list including: one or more diameters calculated in step b), the location of the landing zone contained in the 3D model of the left atrial appendage (20), a first compression value of the left atrial appendage occluder (10), and anatomical features extracted from a 3D model of the patient's left atrium (30), preferably wherein the anatomical features include the shape and location of the opening and / or the shape and location of the fossa ovalis.

[0070] It should be noted that the optimal location of the LAA occluder (10) is determined based on a variety of parameters, including one or more inner diameters calculated in step (b), the landing zone location within the 3D model of the LAA (20), the first compression value of the LAA occluder (10), and various anatomical features identifiable from the 3D model of the left atrium (30). It should also be noted that the landing zone may be characterized as a specific surface or point within the LAA (20), or, in some other embodiments, a suitable volume within the LAA (20). The landing zone may be used as a starting point and may be identified and selected by manual intervention by a user or clinician or automatically (e.g., by referring to a pre-established database of landing zones used in previous LAA occlusion (LAAO) procedures). It should also be noted that the first compression value may be a volumetric approximation, i.e., an approximation in the sense of the relationship between volumes or sub-volumes, or between points contained within said volumes, rather than a mechanical simulation. For example, the first compression value may be determined by the interaction between the occluder (10) and the LAA (20) in terms of volume, sub-volume, or specific points therein. Importantly, this first compression value does not indicate mechanical compression simulation, but rather affects the geometry of the initial placement of the plug (10). Figure 2A and 2B An example of an occluder placed inside the LAA based on a first compression value is shown. The selected location of the LAA occluder (10) may be modified based on changes in the landing zone, selection of different calculated diameters of the LAA (20), or different calculated values ​​of the first compression value associated with different locations. In some embodiments, a series of first compression values ​​may be calculated to evaluate different potential locations of the occluder (10). The final selection of a particular compression value may be guided by criteria regarding the adequacy, safety, or performance predicted by these values. Furthermore, in some embodiments, the computing device may provide a series of alternative locations for the LAA occluder (10) for clinicians to choose from. These proposed locations may be based on the diameter ratio between the LAA (20) and the LAA occluder (10). The diameter of the LAA (20) considered in this context may be an average, minimum, maximum, or other statistically representative measure. Furthermore, for each proposed location, there may be associated distal and proximal proposals relative to the opening of LAA(20), where "proximal" refers to a location closer to the opening of LAA(20) and "distal" refers to a location farther away, as shown below. Figure 3A and 3B As depicted. This dual positioning suggestion adapts to variations in the anatomy of the LAA (20) and aims to ensure the most effective engagement of the LAA occluder (10).

[0071] Step (4) (or d) includes performing a structural simulation to calculate a second compression value of the left atrial appendage occluder (10) based on the interaction between the occluder (10) and the inner wall of the left atrial appendage (20). Preferably, the structural simulation is performed by the finite element method (FEM), more preferably it provides values ​​for pressure and applied force.

[0072] It should be noted that those skilled in the art can perform structural simulations to determine the mechanical behavior of the occluder (10) using other methods, such as the finite difference method (FDM), finite volume method (FVM), spectral methods, boundary element method (BEM), and / or meshless methods. It should also be noted that when calculating the interaction between the LAA occluder (10) and the inner wall of the left atrial appendage (20), the inner wall can be considered as non-deformable as a boundary condition. The simulation may take into account the material properties of the occluder (10), which may include biocompatible metals, such as, but not limited to, titanium, nitinol, stainless steel, or cobalt-chromium alloys, or polymers, including but not limited to silicone, polyurethane, or PTFE. The properties of these materials, such as elasticity, plasticity, and viscoelasticity, can be modeled to predict the deformation of the occluder (10) under physiological conditions. Furthermore, the structural simulation may consider the inner wall of the left atrial appendage (20) as either non-deformable or deformable with varying degrees of stiffness. This takes into account patient-specific variations that can alter the interaction between the occluder (10) and the atrial tissue. In some embodiments, the inner wall may be endowed with material properties similar to those of heart tissue to simulate real interactions. According to some embodiments, structural simulation can provide values ​​of pressure and applied force that are important for assessing the stability and sealing capability of the occluder (10). Such information can be critical for determining the effectiveness of the occlusion and the likelihood of post-implantation migration or leakage. In other embodiments of the invention, the simulation may also take into account dynamic conditions, such as the pulsatile nature of blood flow or the motion of the left atrial appendage (20) wall induced by the cardiac cycle, thereby ensuring a comprehensive assessment under near-physiological conditions.

[0073] In step (5) (or e), if the second compression value previously calculated in step d) is within an acceptable range, the method continues to step f). However, if the value of the second compression value calculated in step d) is outside the acceptable range, the method continues by returning to step c) and performing step c) again with one or more modified parameters, wherein the parameters are preferably the landing area, the first compression value, and / or the one or more diameters.

[0074] It should be noted that the acceptable range of the second compression value may be derived from the compilation of clinical data, expert consensus, or defined by the specifications of the left atrial appendage occluder (10) manufacturer. Those skilled in the art will recognize that these ranges are typical ranges for ensuring the effectiveness and safety of LAAO procedures. It should also be noted that the parameters may be automatically modified when step c) is repeated, and in some other embodiments, the parameters may be manually modified. These parameters may include, but are not limited to, the landing area, the first compression value, and one or more diameters of the left atrial appendage (20). Modification of these parameters (e.g., the landing area and one or more diameters of the LAA (20)) may result in a new set of first compression values, thereby affecting the positioning of the occluder (10) and the second compression value subsequently obtained in step d). Similarly, selecting different first compression values ​​will result in different occluder (10) positioning, as each first compression value may be associated with a specific positioning of the occluder (10). In embodiments where the system is readily automatable, algorithms may be employed to optimize the parameters based on historical data, simulation results, or a database containing a range of parameters from successful LAAO procedures. The system can utilize artificial intelligence to suggest parameter modifications to maximize the likelihood of falling within acceptable ranges during reassessment in steps c) and / or e). On the other hand, manual adjustments allow clinicians to apply nuanced expertise and make informed decisions regarding parameter changes. This approach can integrate patient-specific considerations that automated systems may not fully capture. Furthermore, when repeating step c), combinations of modified parameters can be employed, providing a multifaceted approach to optimization. Modifications can follow deterministic, randomized, or similarity-based patterns, referencing a comprehensive database of LAAO surgeries and simulations to guide the selection process.

[0075] Step (6) (or d) involves using parameters derived from computational fluid dynamics (CFD) calculations and the patient's medical parameters to calculate a set of thrombosis risk factors.

[0076] It should be noted that thrombosis risk factors can be individual values ​​associated with specific parameters derived from CFD calculations. In some embodiments, multiple CFD-derived parameters may be combined to constitute a single risk factor. The methods for combining these parameters can be varied and may include weighted averaging, statistical modeling, or any correlation deemed appropriate by those skilled in the art. Similarly, risk factors derived from patient medical parameters may also consist of a single value or a set of values ​​that are collectively considered to provide a comprehensive risk assessment. In some embodiments, the grouping and / or determination of these medical parameters as risk factors is based on medical guidelines, protocols, or standard practices established by national or international medical authorities, such as the European Society of Cardiology (ESC) or the American Heart Association (AHA). In other embodiments of the invention, CFD calculations may also take into account dynamic conditions, such as the pulsatile nature of blood flow or the motion of the left atrial appendage (20) wall induced by the cardiac cycle, thereby ensuring a comprehensive assessment under near-physiological conditions.

[0077] Step (7) (or e) includes calculating a risk score for thrombosis associated with the left atrial appendage (20) occlusion location in step c), wherein the risk score is based at least on the thrombosis risk factors calculated in step f).

[0078] It should be noted that the risk score can be calculated as the sum of risk factors, a weighted sum of risk factors, or following any mathematical relationship between risk factors, wherein the risk factors may also interact synergistically to increase the risk factor to a value higher than its individual contribution, or alternatively, they may interact synergistically to decrease or divide the risk factor to a value lower than its individual contribution. It should also be noted that the risk score can be provided to the user or clinician via a display device and can be associated with recommendations regarding the placement of the LAA occluder (10) or with recommendations to repeat the method by changing certain parameters.

[0079] It should also be noted that the disclosed computational simulation planning method for left atrial appendage occlusion (LAAO) surgical procedures can be implemented through various computational frameworks. In some embodiments, the method can be integrated into a web-based platform, allowing for both remote and centralized access, thereby facilitating collaborative and multidisciplinary planning. Furthermore, the method can be deployed on cloud systems, providing scalable computing resources and storage capabilities to ensure accessibility and efficiency regardless of the user's local hardware limitations. Alternatively, in some other embodiments, the method can be encapsulated in a standalone software application designed to execute on a local computing system, providing users with the ability to perform the planning process without an internet connection. Such standalone applications can be customized to be compatible with various operating systems and hardware specifications, ensuring broad usability. It should also be noted that the method is clearly external, designed to be performed outside the human body, and is not intended for intraoperative use. Therefore, the method is envisioned for use in the preoperative phase of the surgical planning process. By executing the method through computational simulation, detailed and precise surgical plans can be developed by taking into account patient-specific anatomical and physiological data. This advance planning aims to improve surgical outcomes by enabling the surgical team to anticipate potential challenges and tailor interventions to the individual characteristics of each patient's left atrial appendage (20).

[0080] It should also be noted that in some embodiments, the method of the present invention can be integrated into a web-based platform, cloud system, standalone software, or any other suitable computer device. It should also be noted that the method of the present invention is performed insilico, i.e., it is extracorporeal, and therefore it is not intended to be performed during surgery. Rather, it is intended to be performed in advance, i.e., during the planning phase of LAAO surgery.

[0081] Advantageously, the claimed method for computer-based implementation of computational simulation surgical planning for left atrial appendage (20) occlusion (LAAO) presents several significant benefits. For example, the method provides rapid and accurate calculation of the first compression value, which allows for interactive visualization and modification of the occluder (10) position, or the acquisition of a positioning recommendation based on this rapid and reliable first compression value within minutes or seconds. The first compression value is crucial for selecting a location that does not damage the LAA and where there is no risk of displacement or failure to seal the LAA. Therefore, an improved method that provides a rapid and safe positioning recommendation using patient parameters (e.g., ostium and / or fossa ovalis) and geometric considerations is highly advantageous. This ensures that the occluder is properly placed within the LAA (20), achieving the first compression value that supports the stability and effectiveness of the occluder. Furthermore, by using computational fluid dynamics calculations and calculating a set of thrombotic risk factors based on the patient's medical parameters, the method provides a precise prediction of the risk of thrombosis, an increasing concern in LAAO procedures. Subsequently, a risk score for thrombosis is calculated based on the location of the occluder (10), providing a quantifiable assessment of surgical safety and aiding clinicians in making informed decisions. In summary, the claimed approach offers a comprehensive, customized surgical planning tool that maximizes the effectiveness and safety of LAAO procedures. The integration of precise anatomical modeling with dynamic occluder location and thrombosis risk assessment ensures high-quality, patient-specific, and reliable results, thereby reducing overall surgical risk and facilitating successful interventions.

[0082] According to another embodiment of the method of the present invention, if the risk score calculated in step g) is higher than the risk threshold, the next step is to modify the landing area location, the first compression value, and / or one or more diameters selected from one or more diameters calculated in step b), and then perform step c) again.

[0083] It should be noted that this method allows for an adaptive response when the calculated risk score exceeds a specified risk threshold, indicating a need to modify key parameters. This risk threshold may be based on recommendations from medical authorities such as the ESC or AHA, and therefore may vary over time, or may be adjusted by those skilled in the art based on research or hospital practice. In some embodiments, changes to the landing zone location, the first compression value, and / or the selected diameter can be performed manually, providing clinicians with the flexibility to apply their judgment based on experience and specific patient anatomy. In some other embodiments, adjustments to these parameters can be performed automatically by the system, employing an algorithm that adaptively seeks the optimal configuration. This can improve the accuracy of surgical planning by utilizing a systematic computational approach to determine the most favorable adjustments. In some other embodiments, modifications can be performed randomly to thoroughly investigate various potential outcomes, or based on a comprehensive historical database of successful placements, providing empirical evidence to guide the modification process. Parameters can be adjusted individually or collectively, enabling a multifaceted optimization approach to address the complex interactions between different factors influencing closure outcomes.

[0084] Advantageously, this step allows clinicians or users to repeat some steps of the method by modifying key parameters until a safe location for the LAA occluder is found.

[0085] According to another embodiment of the method of the present invention, if the risk score calculated in step g) is higher than the risk threshold, the next step is to shift the 3D model of the left atrial appendage occluder (10) to different locations based on a database of CFD simulations performed on LAAO from other patients, and to repeat step d), wherein preferably the different locations are said to minimize the risk score according to a matching algorithm that extracts the different locations by matching the occluder (10) with another similar left atrial appendage occluder (10) from the simulation database.

[0086] It should also be noted that this method preferably utilizes a matching algorithm that helps minimize the risk score by selecting alternative locations for the occluder (10). This algorithm operates by comparing the current patient's LAA anatomy and the location of the occluder (10) with archived simulation data and parameters of similar anatomy and occluder locations. Through this comparison, the algorithm can extract locations in the database that have previously resulted in reduced risk scores, potentially providing a safer and more effective occlusion strategy for the current patient.

[0087] Advantageously, this approach leverages historical data and advanced matching techniques to enhance the decision-making process, thereby reducing the trial-and-error aspect of occluder (10) positioning. By aligning the occluder (10) position with data-supported simulations, this method significantly increases the likelihood of achieving optimal occlusion outcomes with a lower risk of thrombosis. This evidence-based relocation approach not only personalizes the procedure based on the patient's specific anatomy but also integrates collective insights from past successful interventions, potentially leading to improved surgical success rates and patient outcomes.

[0088] According to another embodiment of the method of the present invention, the patient's medical image is generated from one or more imaging techniques from a list including: ultrasound (US) imaging, transthoracic echocardiography (TTE), computed tomography (CT) imaging, magnetic resonance imaging (MRI) imaging, 3D rotational angiography (3DRA) imaging, mitral valve pulsation ultrasound imaging, and pulmonary vein pulsation ultrasound imaging; preferably wherein the 3D model of the patient's left atrium (30) is generated using an input image generated by mitral valve pulsation US imaging and any one of MRI, 3DRA, or CT imaging; more preferably wherein the 3D model of the patient's left atrium (30) is generated using an input image generated by mitral valve pulsation US, pulmonary valve pulsation US, and any one of MRI, 3DRA, or CT imaging.

[0089] Advantageously, the preferred combination of these imaging techniques balances the high temporal resolution of pulsatile ultrasound imaging with the spatial resolution and anatomical detail provided by MRI, 3DRA, or CT imaging, resulting in a comprehensive representation of the left atrium (30). Furthermore, using input images from both mitral valve pulsatile ultrasound and pulmonary valve pulsatile ultrasound to generate a 3D model of the left atrium (30) further enriches the 3D model with dynamic information about blood motion, which is crucial for accurate device placement and risk assessment. On the other hand, the use of multiple imaging techniques in generating the 3D model of the left atrium (30) significantly improves the accuracy and reliability of the model. This multimodal imaging strategy allows for a detailed understanding of the patient's unique cardiac anatomy and dynamics, which is helpful in creating customized surgical plans. In addition, the use of pulsatile ultrasound imaging provides real-time data on blood and cardiac motion, which may be critical for the accurate placement of the occluder (10) and assessment of potential risks, such as device interference with valve function. This flexibility in image sourcing and model creation inherently improves the fidelity of the surgical planning process, thereby increasing the probability of successful occlusion surgery and patient outcomes.

[0090] According to another embodiment of the method of the invention, the first compression value is based on the volume relationship between the volume defined by the unmodified 3D model of the occluder (10) and the volume defined by the 3D model of the occluder (10) when the volume of the 3D model of the occluder (10) is modified to fit the inner wall size of the 3D model of the patient's left atrium (30), preferably wherein the 3D model of the occluder (10) is virtually divided according to one or more sections (12, 14) perpendicular to or offset from the centerline (18) of the 3D model of the occluder (10), wherein the sections (12, 14) define two or more sub-volumes (16), and each section includes one or more points along each section, and wherein the first compression value is approximated as the relationship between the position of the point when the occluder (10) is not compressed and the position of the point when the occluder (10) is compressed to fit the inner wall size of the 3D model of the patient's left atrium (30).

[0091] It should be noted that the volume relationship refers to a comparative analysis between two volumes: the inherent volume of the unmodified 3D model of the occluder (10) and the altered volume when the occluder (10) is fitted to conform to the spatial constraints of the left atrial appendage (20). This relationship quantifies how much the occluder (10) must be compressed or expanded to fit within the unique anatomical features of the patient's left atrium (30). It should also be noted that the unmodified 3D model of the occluder (10) represents the geometry of the occluder (10) as it was originally designed or manufactured, before any alterations are made to fit within the left atrial appendage (20). In contrast, the modified volume of the occluder (10) corresponds to its altered state after fitting, where its dimensions are conformed to the internal contours of the left atrium (30). Regarding cross-sections, it should be noted that the 3D model of the occluder (10) can be segmented into discrete cross-sections (12, 14), which may be perpendicular to the centerline (18) of the occluder (10) or at varying angles to it. This segmentation can take various forms, including but not limited to radial, axial, or arbitrary cross-sections, adapting to any geometric possibility known to those skilled in the art. This flexibility helps simplify the complexities associated with deformation analysis of the occluder (10) because it facilitates the decomposition of the structure into manageable segments that can be evaluated and adjusted individually. It should also be noted that the sub-volumes (16) are individual volumes divided by the cross-sections (12, 14) of the occluder (10). These sub-volumes (16) represent partitions of the total volume of the occluder (10) and are essential for calculating how each cross-section (12, 14) contributes to the total compression value. It should also be noted that the “points” located along each cross-section (12, 14) serve as reference coordinates for tracking the deformation of the occluder (10). These points establish a relationship before and after compression or modification that provides the degree of displacement required by the occluder (10) to achieve optimal conformation with the inner wall of the left atrium (30). The positional offset of these points from an uncompressed state to a compressed state defines the first compression value. This value is crucial for determining the suitability of the plug (10) for effective plugging while maintaining its structural integrity.

[0092] Advantageously, using volumetric assessment to determine the initial compression value improves the accuracy of occluder (10) size selection and fitting, and significantly increases computational speed compared to mechanical simulation. Segmentation into sections (12, 14) provides a detailed approach to fitting the occluder (10), allowing adjustments to account for the complex geometry of the left atrial appendage (20). This refined approach to fitting the occluder (10) ensures that the device effectively conforms to the patient's specific anatomy, which can mitigate complications such as device embolism or peria-device leakage, thereby improving patient outcomes. The use of sub-volumes (16) and sections (12, 14) results in computational models that are accurate in predictive power, fast in application, and versatile, thus enhancing the operational success of the occlusion method.

[0093] According to another embodiment of the method of the present invention, the location of the LAA occluder determined in step c) is further determined based on the patient's clinical data, preferably wherein the patient's clinical data includes one or more elements selected from a list including: CHADS2-VASc, HAS-Bleed, and ultrasound data; more preferably, the clinical data also includes one or more elements from a list including: left atrial pressure, hemoglobin level, BNP, C-protein level, troponin level, eGFR, ECG data, and creatinine level; and even more preferably wherein the patient's clinical data includes all elements from both lists.

[0094] It should be noted that patient clinical data, such as CHADS2-VASc scores, are not static and may change over time according to guidelines from authoritative bodies such as the European Society of Cardiology (ESC) and the American Heart Association (AHA). It should also be noted that the inclusion of such clinical data can be achieved through computation or algorithms that incorporate these variables into the occluder (10) placement process.

[0095] Advantageously, the use of a broad array of clinical data, including but not limited to CHADS2-VASc, HAS-Bleed, ultrasound data, and preferably other physiological biomarkers, facilitates a comprehensive, evidence-based approach to LAA occluder (10) placement. These elements provide a multifaceted view of the patient's health condition, which may be crucial for accurate placement of the occluder (10). This approach, by utilizing comprehensive patient-specific data, aims to maximize the therapeutic benefits of LAA occlusion surgery, potentially reducing the incidence of adverse events and optimizing long-term patient outcomes.

[0096] According to another embodiment of the method of the present invention, the location of the LAA occluder determined in step c) is further determined based on at least one or more elements from a list including: the location of the landing zone contained in a 3D model of the left atrial appendage (20), a database of left atrial appendage occluders (10) located in the left atrial appendage (20) of other patients, the mechanical and volumetric characteristics of the catheter associated with the location of the left atrial appendage occluder (10), and ultrasound images; preferably wherein the location of the LAA occluder is determined based on all elements of the list.

[0097] It should also be noted that a database or repository containing records of LAA occluders (10) located in the LAAs (20) of multiple other patients can be used as a reference for determining the optimal location of the occluder (10). This database may include data points from previously successful implantations, thus providing an empirical basis to support the location decision. The database and matching algorithm can be used to determine the optimal configuration of the patient's left atrial appendage occluder (LAAO) based on the patient's individual characteristics and features of previously implanted LAAOs in other patients. The algorithm first extracts the patient's morphological and clinical features, such as hematocrit levels and creatinine levels. These features are then compared with a database of previously implanted LAAOs in other patients using a similarity metric based on Euclidean distance. Euclidean distance is a measure of the distance between two points in space, and in this case, it is used to measure the difference between the patient's features and the LAAO features in the database. In some embodiments, the algorithm may calculate the sum of the absolute differences between the patient's features and each LAAO feature in the database and select the LAAO with the smallest sum as the optimal configuration for the patient. It should also be noted that the mechanical and voluminous characteristics of the catheter used during the location process are also considered as part of the occluder placement strategy. These characteristics can affect the operability and final positioning of the occluder (10), and therefore their impact on the procedure can be taken into account in this method.

[0098] Advantageously, incorporating a diverse set of parameters, including specific landing zones, a database of previous occluder placements, catheter characteristics, and real-time ultrasound imaging, into determining the location of the occluder (10) enables a detailed and highly informed approach to occluder (10) localization. Utilizing this combination of factors enhances the customizability of the procedure to individual patient anatomy and conditions, thereby improving patient-specific outcomes and reducing the risk of complications associated with suboptimal occluder localization.

[0099] In another embodiment of the method according to the invention, the patient's medical parameters include CHADS2-VASc and / or HAS-Bleed.

[0100] It is important to note that CHADS2-VASc represents congestive heart failure, hypertension, age ≥75 years, diabetes, stroke / transient ischemic attack, vascular disease, age 65-74 years, and sex classification. Each of these factors is assigned a score, and the total score is used to estimate the stroke risk in patients with atrial fibrillation. On the other hand, HAS-BLED represents hypertension, abnormal renal / hepatic function, stroke, history of bleeding or susceptibility, unstable international normalized ratio, advanced age (>65 years), and concomitant use of medications / alcohol. Each of these factors is assigned a score, and the total score is used to estimate the bleeding risk in patients taking anticoagulants.

[0101] It should also be noted that these scores allow for a customized approach to the placement of the left atrial appendage occluder (10). This individualized strategy ensures that the risk of thromboembolic events and bleeding complications is minimized by taking into account the unique risk profile of each patient as determined by these scores.

[0102] Advantageously, incorporating the CHADS2-VASc and HAS-Bleed scores into the medical parameters considered in this approach provides a way to align therapeutic interventions with patient clinical needs. This approach may reduce the likelihood of postoperative complications, enhance the effectiveness of occluder (10) placement, and may contribute to a more favorable patient outcome.

[0103] According to another embodiment of the method of the present invention, the thrombosis risk factors derived from CFD calculations include one or more elements from a list comprising: mean blood flow velocity in the pulmonary ridge region, presence and mean blood flow velocity of eddies, and / or stagnant flow in the pulmonary ridge region and on the device surface, amount of particulate adhesion, and thrombogenic hemodynamic parameters; preferably wherein the thrombosis risk factors derived from CFD calculations include all elements from the list.

[0104] Advantageously, this approach proactively reduces the risk of thrombosis by evaluating a comprehensive set of thrombotic risk factors derived from CFD calculations. Thorough analysis of these factors allows for optimization of the design and placement of the occluder (10), thereby improving the safety and effectiveness of the procedure. Incorporating these thrombotic risk factors ensures that the procedure is tailored not only to the patient's anatomical needs but also to the dynamic conditions within the LAA, which can vary significantly between individuals. This holistic approach can improve patient outcomes by reducing the incidence of procedure-related thrombotic events.

[0105] According to another embodiment of the method of the present invention, the thrombotic hemodynamic parameters include endothelial cell activation potential values, and preferably also include hypercoagulable state and / or blood retention time values.

[0106] Advantageously, incorporating endothelial cell activation potential, hypercoagulable state, and blood retention time into thrombogenic hemodynamic parameters enables a more detailed and comprehensive assessment of thrombotic risk. This facilitates the optimization of the positioning and design of LAA occluders (10), potentially leading to more personalized and effective thrombotic prevention strategies.

[0107] According to another embodiment of the method of the present invention, one or more thrombotic risk factors calculated based on the patient's thrombogenicity index are determined by comparing each thrombogenicity index value with one or more risk thresholds for each thrombogenicity index.

[0108] It should be noted that these risk thresholds serve as key reference points, indicating an elevated risk of thrombosis when measured values ​​exceed them. In some embodiments, different risk levels or thresholds may exist instead of a single threshold, defining different risk levels and thus contributing progressively or differentially to the value of each risk factor.

[0109] The risk thresholds for each thrombogenic marker can be established by medical authorities such as the European Society of Cardiology (ESC) or the American Heart Association (AHA). Alternatively or additionally, these thresholds can be defined by hospital practices based on clinical studies or patient outcomes. These thresholds can evolve over time as new research provides deeper insights into the factors influencing thrombosis. Furthermore, differences may exist between regions due to varying medical protocols, population genetics, and prevailing local practices.

[0110] Advantageously, methods for determining the location and deployment of LAA occluders (10) based on these comparative analyses could lead to a more standardized and potentially safer patient experience.

[0111] According to another embodiment of the method of the present invention, the CFD calculation takes one or more physiological parameters from a list including: pulmonary venous pressure, left atrial pressure, Doppler ultrasound data and hematocrit level as input, preferably wherein the CFD calculation takes all elements in the list as input.

[0112] Advantageously, by incorporating a wide range of physiological parameters, this method can produce a more detailed simulation of the patient's cardiac environment. This comprehensive approach can help identify optimal occluder placement and configuration, enhancing the predictability of surgical outcomes. Incorporating such diverse inputs also allows simulations to be personalized to the patient's unique physiological context, potentially improving the accuracy of occluder fitting and reducing the likelihood of complications such as thrombosis or device migration.

[0113] According to another embodiment of the method of the present invention, the CFD calculation is performed under simulated motion of the patient's left atrium (30), preferably said motion is simulated using the arbitrary Lagrange-Euler method (ALE).

[0114] Advantageously, incorporating simulated motion of the left atrium (30) via the ALE method into CFD calculations allows for precise analysis of the mechanical stresses and fluid-structure interactions that the occluder (10) will experience in vivo. This significantly improves the predictive power of the simulation and may reduce the risk of complications by ensuring that the device's behavior under physiological conditions is fully understood and taken into account. This leads to a more personalized and potentially safer approach to device implantation for each individual patient.

[0115] According to a preferred embodiment of the method of the present invention, the simulation of motion includes the following steps:

[0116] a) Compare the 3D model of the patient's left atrium (30) with a database of 3D models of the left atrium (30) in motion of other patients obtained from dynamic computed tomography images of the left atrium (30) in motion of other patients;

[0117] b) Select the most similar 3D model of the left atrium (30) from the database;

[0118] c) The motion interpolation extracted from the 3D model of the most similar left atrium (30) selected in step b) is applied to the geometry of the patient's left atrium (30); and

[0119] d) The motion of the patient's left atrium (30) is simulated using the interpolation from step c) to the motion of the patient's left atrium (30), the arbitrary Lagrange-Euler method (ALE), and the patient's CFD physiological parameters as inputs.

[0120] It should be noted that other alternatives for representing dynamic computed tomographic images of the left atrium (30) in motion may include, but are not limited to, magnetic resonance imaging (MRI) sequences, echocardiography (especially 3D echocardiography) and cardiac catheterization data.

[0121] It should also be noted that in some embodiments, comparing a 3D model of the patient's left atrium (30) with models in a database can be performed using various computational techniques known to those skilled in the art, such as geomorphometry (which analyzes shape) or registration algorithms (which attempt to superimpose two models to quantify their similarity). In some embodiments, parameters such as the size, shape, motion pattern, and / or wall thickness of the left atrium (30) may also be considered in the comparison. It should also be noted that when selecting the most similar 3D model of the left atrium (30), the term "most similar" may refer to the model with the highest consistency across a range of metrics, including geometric dimensions, physiological motion patterns, and / or tissue features. This evaluation can be qualitative or quantitative and may involve machine learning algorithms trained to identify patterns consistent with patient data. Additionally, in some embodiments, interpolation may involve techniques such as spline interpolation or shape-based averaging. As an alternative to interpolation, some embodiments may include statistical shape modeling or the use of deep learning models that can predict patient-specific motions based on patterns learned from extensive prior analyses of motion. It should also be noted that motion simulation using the aforementioned inputs can employ a physics-based model to replicate the mechanical behavior of the left atrium (30) under cardiac load conditions. In some embodiments, other simulation techniques may be employed, such as finite element analysis or fluid-structure interaction models, which may also take into account the complex interactions between blood flow and atrial wall motion.

[0122] Advantageously, this process enables the creation of patient-specific simulations that take into account the unique motion of the left atrium (30), thereby providing a more accurate prediction of the behavior of the left atrial appendage occluder (10) within a dynamic cardiac environment. This can significantly help optimize the fitting and placement of the occluder (10), potentially reducing the risk of displacement or other complications, leading to more effective treatment tailored to the individual patient's anatomy and cardiac dynamics.

[0123] Figure 6 An exemplary embodiment of the invention is depicted (where the numbers 41, 42, 43, 44, 45, 46, 47, 48, 49 and 50 correspond to the letters a, b, c, d, e, f, g, h, i, j, respectively). Figure 6 A method for computer-based implementation of computational simulation surgical planning for left atrial appendage (LAA) occlusion (LAAO) is shown, comprising the following steps:

[0124] a. Upload data including the patient's medical images and clinical data;

[0125] b. Generate a three-dimensional (3D) model of the patient's left atrium (30) from the uploaded medical images;

[0126] c. Calculate the various anatomical features of the left atrium (30) and left atrial appendage (LAA) (20) within the 3D model generated in step b);

[0127] d. Perform a compression approximation method to estimate the first compression value of the left atrial appendage occluder (10);

[0128] e. Based on a matching algorithm, a suggested location for at least one left atrial appendage occluder (10) is provided, the matching algorithm taking into account the diameter from step c), the landing area, anatomical features from the 3D model, and the first compression value from step d);

[0129] f. Perform structural simulation to determine the second compression value of the left atrial appendage occluder (10); if the second compression value is not within an acceptable range, modify one or more parameters and repeat step e);

[0130] g. Flow simulations were performed using computational fluid dynamics (CFD) and arbitrary Lagrange-Euler (ALE) methods, with clinical data, ultrasound imaging, and simulated motion of the left atrium (30) as inputs;

[0131] h. Calculate the device-related thrombosis (DRT) risk score based on the parameters derived from the simulation performed in step g);

[0132] i. If the DRT risk score is higher than the risk threshold, the optimal location of the left atrial appendage occluder (10) is found using a simulation database and an additional matching algorithm, and then the process returns to step f with the newly suggested location. If the DRT risk score is equal to or lower than the risk threshold, step j is executed.

[0133] j. End the algorithm, preferably displaying the DRT risk score before ending the algorithm.

[0134] A second aspect of the invention relates to a computer program comprising instructions that, when executed by a computing device, cause the computing device to perform the method described in any of the preceding claims.

[0135] It should be noted that the program can be configured to process 3D models, assess volumetric relationships, integrate patient-specific clinical data, and perform complex computational fluid dynamics (CFD) calculations. Simultaneously, instructions within the program can be structured to interact with anatomical model databases, execute algorithms for comparing and interpolating physiological motion, and apply various simulation methods, such as the Arbitrary Lagrange-Euler (ALE) method. Additionally, the program may include modules for analyzing thrombosis risk factors and integrating multiple patient-specific medical parameters. Alternatives to the computing device on which the program runs may include a standalone workstation, an integrated system within a medical device, a cloud-based computing environment, or a distributed computing platform, allowing for deployment flexibility.

[0136] Advantageously, the execution of such computer programs facilitates highly individualized approaches to the treatment of conditions such as atrial fibrillation, enabling clinicians to make informed decisions based on a fusion of simulation and real-world data, potentially improving patient outcomes through the design of customized medical interventions.

[0137] A third aspect of the present invention relates to a data processing apparatus, including a processing unit and a storage unit, wherein the storage unit includes instructions that, when executed by the processing unit, configure the data processing apparatus to perform the method of the present invention.

[0138] It should be noted that data processing devices may include various forms of hardware, such as a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor, such as a digital signal processor (DSP) or an application-specific integrated circuit (ASIC). They may also be adapted to perform the high-speed calculations required for 3D modeling and computational fluid dynamics (CFD) simulations. Storage units may include non-volatile forms of storage, such as read-only memory (ROM), flash memory, or solid-state drives (SSDs), which retain necessary instructions. They may also include volatile memory, such as random access memory (RAM), to facilitate rapid access to and execution of instructions by the processing unit. Data processing devices may include additional components, such as input / output interfaces for interacting with peripheral devices, network connectivity modules for accessing patient data and databases, and user interface elements for displaying results and facilitating clinician interaction.

[0139] Advantageously, this configuration allows for a self-contained system capable of providing sophisticated analysis related to LAA occluder positioning, informed by patient-specific data and advanced modeling techniques. This optimizes surgical outcomes and supports personalized medical treatment strategies.

[0140] A fourth aspect of the invention relates to a computer-implemented method for calculating and simulating the risk of thrombosis associated with left atrial appendage occlusion. This fourth aspect is independent of the method of the first aspect of the invention, as it relates to a method for determining the risk of thrombosis, not to a planning method. The method includes the following steps:

[0141] a. Starting from a 3D model of the left atrium of a patient containing a 3D model of a left atrial appendage occluder, perform computational fluid dynamics (CFD) calculations on the blood flow through the 3D model of the left atrium, taking patient parameters as input, wherein the input patient parameters include at least hematocrit level and / or specific blood pressure.

[0142] b. Obtain a set of parameters from CFD calculations, wherein the set of parameters includes one or more parameters selected from a list including: mean blood flow velocity in the pulmonary crest region, mean blood flow velocity in eddies, streamlines and / or stagnant flow, particle adhesion amount, and thrombogenicity indicators, wherein the thrombogenicity indicators include at least one or more of the following parameters: hypercoagulable state, blood retention time and / or endothelial cell activation potential.

[0143] c. Calculate a risk score for thrombosis associated with left atrial appendage occlusion in the patient, based at least on each parameter extracted from the computational fluid dynamics (CFD) calculation in step b) and the patient’s clinical data, wherein the patient’s clinical data includes at least CHADS2-VASc and / or HAS-Bleed;

[0144] d. Compare the risk score with one or more predefined values ​​that distinguish different levels of risk of thrombosis;

[0145] e. Determine the risk of thrombosis associated with left atrial appendage occlusion based on the comparison performed in step e).

[0146] According to another embodiment of the method of the fourth aspect of the present invention, the risk score is calculated as the sum of partial thrombosis risk factors, wherein said partial thrombosis risk factors include at least one partial thrombosis risk factor from each parameter extracted from the computational fluid dynamics (CFD) calculation of step b), and at least one partial thrombosis risk factor from the patient's clinical data.

[0147] According to another embodiment of the method of the fourth aspect of the present invention, the risk factors for thrombosis calculated from the parameters extracted from CFD calculations are determined by comparing each parameter with one or more risk thresholds for each parameter.

[0148] According to another embodiment of the method of the fourth aspect of the present invention, the patient's clinical data includes at least one or more of the following parameters: CHADS2-VASc, HAS-Bleed, left atrial pressure, pulmonary venous pressure, hematocrit level, hemoglobin level, and creatinine level, preferably wherein the patient's clinical data includes all of the mentioned parameters.

[0149] According to another embodiment of the method of the fourth aspect of the present invention, the one or more thrombotic risk factors calculated from the patient's clinical data are determined by comparing each parameter included in the patient's clinical data with one or more risk thresholds for each parameter.

[0150] According to another embodiment of the method of the fourth aspect of the present invention, the thrombogenicity index includes one or more of the following parameters: hypercoagulable state, blood retention time, and endothelial cell activation potential, preferably wherein the patient's clinical data includes all of the mentioned parameters.

[0151] According to another embodiment of the method of the fourth aspect of the present invention, the one or more thrombotic risk factors calculated from the patient's thrombogenicity index are determined by comparing each thrombogenicity index value with one or more risk thresholds for each thrombogenicity index.

[0152] According to another embodiment of the method of the fourth aspect of the present invention, the input parameters for CFD calculation further include one or more of the following parameters: CHADS2-VASc, HAS-Bleed, left atrial pressure, pulmonary venous pressure, hemoglobin level, BNP, C-protein level, troponin level, eGFR, Doppler ultrasound data, ECG data and creatinine level, and even more preferably, wherein the CFD input includes all of the aforementioned parameters.

[0153] According to another embodiment of the method of the fourth aspect of the present invention, the CFD calculation is performed under simulated motion of the left atrium of the patient, preferably wherein the motion is simulated using the arbitrary Lagrange-Euler method (ALE).

[0154] According to another embodiment of the method of the fourth aspect of the present invention, the simulation of motion includes the following steps:

[0155] a. Compare the 3D model of the patient's left atrium with a database of dynamic computed tomographic images of the left atrium in motion from other patients;

[0156] b. Select the most similar dynamic CT image, preferably by using an artificial intelligence algorithm; and

[0157] c. Simulate the motion of the patient's left atrium using motion extracted from selected dynamic CT images, arbitrary Lagrange-Euler method (ALE), and the patient's CFD input.

[0158] According to another embodiment of the method of the fourth aspect of the present invention, a 3D model of the patient's left atrium is generated using one or more images from one or more imaging techniques selected from a list including: pulsatility imaging, computed tomography (CT) imaging, magnetic resonance angiography (MRA) imaging, 3D rotational angiography (3DRA) imaging, mitral valve pulsatility imaging, and pulmonary vein pulsatility imaging, preferably wherein the 3D model of the patient's left atrium is generated using images generated by CT imaging, mitral valve pulsatility imaging, and pulmonary vein pulsatility imaging as input.

[0159] According to another embodiment of the method of the fourth aspect of the present invention, the 3D model of the patient's left atrium is automatically generated using a neural network.

[0160] According to another embodiment of the method of the fourth aspect of the present invention, a 3D model of a left atrial appendage occluder is positioned within a 3D model of the patient's left atrial appendage, wherein the 3D model of the patient's left atrial appendage is part of a 3D model of the patient's left atrium, the positioning being based at least on the diameter of the 3D model of the left atrial appendage, the patient's anatomical data, and a deployment algorithm, preferably wherein the 3D model of the left atrial appendage occluder is also positioned within the 3D model of the patient's left atrium based on the patient's clinical data, and more preferably based on ultrasound imaging.

[0161] According to another embodiment of the method of the fourth aspect of the invention, the patient's anatomical data includes a region of a 3D model of the patient's left atrium corresponding to the opening and foramen ovale, preferably wherein the patient's anatomical data also includes a landing area, wherein the landing area corresponds to a selected point or subregion contained in the patient's left atrial appendage.

[0162] According to another embodiment of the method of the fourth aspect of the present invention, the diameter of the left atrial appendage is calculated based on a 3D model of the patient's left atrial appendage and a central line, wherein the central line is calculated based on a 3D model of the patient's left atrial appendage and the patient's anatomical data.

[0163] According to another embodiment of the method of the fourth aspect of the present invention, the deployment algorithm is configured to determine the position of the 3D model of the left atrial appendage occluder in the 3D model of the patient's left atrial appendage.

[0164] According to another embodiment of the method of the fourth aspect of the present invention, the 3D model of the left atrial appendage occluder is also located in the 3D model of the left atrial appendage occluder of the patient based on a database of 3D models of left atrial appendage occluders located in 3D models of the left atrial appendage of other patients.

[0165] According to another embodiment of the method of the fourth aspect of the present invention, the 3D model of the left atrial appendage occluder is selected from a predefined left atrial appendage occluder database based on the diameter of the 3D model of the left atrial appendage.

[0166] According to another embodiment of the method of the fourth aspect of the present invention, the pressure applied by the 3D model of the left atrial appendage occluder to the inner wall of the 3D model of the left atrial appendage is calculated by structural simulation, and if the calculated pressure is greater than a certain threshold, the 3D model of the left atrial appendage occluder is automatically repositioned and the pressure applied by the 3D model of the left atrial appendage occluder to the inner wall of the 3D model of the left atrial appendage is recalculated, wherein the process can be repeated until a position where the applied pressure is lower than a certain threshold is found, preferably wherein the threshold is determined by the mechanical characteristics of the occluder.

[0167] According to another embodiment of the method of the fourth aspect of the present invention, if the calculated risk score is higher than a threshold, the 3D model of the left atrial appendage occluder is automatically repositioned in the 3D model of the patient's left atrium until the recalculated risk is lower than the threshold.

[0168] Example

[0169] Example 1

[0170] refer to Figure 3A and Figure 3B Computational fluid dynamics simulation of real patient interventions.

[0171] Materials and Methods

[0172] Two different device configurations for the pacifier-type device were proposed for this patient's anatomy. The device configurations were described as proximal implantation (i.e., the surface of the device disc lies on the interface plane between the left atrial cavity and the left atrial appendage) and distal implantation (i.e., the surface of the device disc lies 4 mm below the interface plane between the left atrial cavity and the left atrial appendage). Both configurations were determined to be 28 mm in size based on the maximum (D1) diameter and minimum (D2) diameter of the landing area (proximal position: D1 and D2 are 25 mm and 20 mm, respectively; distal position: D1 and D2 are 28 mm and 22 mm, respectively), anatomical features (i.e., posteroinferior puncture of the fossa ovalis, LAA type), and compression values ​​obtained through steps 3, 4, and 5 of the general procedure (proximal position: 18.52%; distal position: 27.39%).

[0173] result

[0174] Figure 3 shows the results of CFD+ALE simulations (step 7) performed using pulsatile echo Doppler ultrasound at the patient's mitral valve, hemorheological properties, and 4D wall LA motion extracted from the most similar patient in the dynamic CT database in both device configurations. Figure 3A High-velocity (i.e., >0.2 m / s) laminar blood flow behavior on the device surface in the proximal position was described. In the distal position, a low-velocity (i.e., <0.2 m / s) vortex blood flow pattern was formed on the upper part of the device disc, which persisted for the remainder of the cardiac cycle.

[0175] in conclusion

[0176] After analyzing several demographic, anatomical, and hemodynamic parameters of the device-related thrombosis risk score, and considering the low DRT risk score obtained, the proximal position should be considered the optimal configuration for this patient's LAAO procedure.

Claims

1. A computer-implemented method for computational simulation surgical planning of left atrial appendage (20) occlusion (LAAO), the method comprising the following steps: a. Generate a 3D model of the patient's left atrium (30) based on the patient's medical images; b. Calculate one or more internal diameters of the lumen of the left atrial appendage (LAA) (20), wherein the left atrial appendage (20) is contained in a 3D model of the left atrium (30) of the patient generated in step a); c. Positioning at least partially within the left atrial appendage occluder (10) using a 3D model of the occluder (10), wherein the position is further determined based on one or more parameters selected from a list including: one or more diameters selected from one or more diameters calculated in step b), the location of the landing zone contained in the 3D model of the left atrial appendage (20), the first compression value of the occluder (10), and anatomical features extracted from the 3D model of the patient's left atrium (30), preferably wherein the anatomical features include the shape and location of the opening and / or the shape and location of the fossa ovalis; d. Perform structural simulation to calculate a second compression value of the left atrial appendage occluder (10) based on the interaction between the left atrial appendage occluder (10) and the inner wall of the left atrial appendage (20); e. If the second compression value calculated in step d) is within an acceptable range, proceed to step f); and if the value of the second compression value calculated in step d) is outside the acceptable range, return to step c) and execute step c) again with one or more modified parameters, wherein the parameters are preferably the landing area, the first compression value, and / or the one or more diameters. f. Using parameters derived from computational fluid dynamics (CFD) calculations and the patient's medical parameters, calculate a set of thrombosis risk factors; g. Calculate a risk score for thrombosis associated with the left atrial appendage (20) occlusion location in step c), wherein the risk score is based at least on the thrombosis risk factors calculated in step f).

2. The computer-implemented method according to claim 1, wherein, If the risk score calculated in step g) is higher than the risk threshold, then modify the landing area location, the first compression value, and / or one or more diameters selected from the one or more diameters calculated in step b), and execute step c) again.

3. The computer-implemented method according to claim 1 or 2, wherein, If the risk score calculated in step g) is higher than the risk threshold, the 3D model of the left atrial appendage occluder (10) is shifted to a different location based on a database of CFD simulations performed on LAAO from other patients, and step d) is repeated.

4. The computer-implemented method according to any one of the preceding claims, wherein, The patient's medical images are generated from one or more imaging techniques from a list including: ultrasound (US) imaging, transthoracic echocardiography (TTE), computed tomography (CT) imaging, magnetic resonance imaging (MRI) imaging, 3D rotational angiography (3DRA) imaging, mitral valve pulsation ultrasound imaging, and pulmonary vein pulsation ultrasound imaging; preferably, the 3D model of the patient's left atrium (30) is generated using an input image generated by mitral valve pulsation US imaging and any of MRI, 3DRA, or CT imaging; more preferably, the 3D model of the patient's left atrium (30) is generated using an input image generated by mitral valve pulsation US, pulmonary valve pulsation US, and any of MRI, 3DRA, or CT imaging.

5. The computer-implemented method according to any one of the preceding claims, wherein, The first compression value is based on the volume relationship between the volume defined by the unmodified 3D model of the occluder (10) and the volume defined by the 3D model of the occluder (10) when the volume of the 3D model of the occluder (10) is modified to fit the inner wall size of the 3D model of the patient's left atrium (30).

6. The computer-implemented method according to any one of the preceding claims, wherein, The location of the LAA occluder determined in step c) is further determined based on the patient's clinical data, preferably wherein the patient's clinical data includes one or more elements selected from a list including: CHADS2-VASc, HAS-Bleed, and ultrasound data; more preferably the clinical data also includes one or more elements from a list including: left atrial pressure, hemoglobin level, BNP, C-protein level, troponin level, eGFR, ECG data, and creatinine level; and even more preferably wherein the patient's clinical data includes all elements from both lists.

7. The computer-implemented method according to any one of the preceding claims, wherein the location of the LAA occluder determined in step c) is further determined based on at least one or more elements from a list comprising: the location of the landing zone contained in a 3D model of the left atrial appendage (20), a database of left atrial appendage occluders (10) located in the left atrial appendage (20) of other patients, the mechanical and volumetric characteristics of the catheter associated with the location of the left atrial appendage occluder (10), and ultrasound images; preferably wherein the location of the LAA occluder is determined based on all elements of the list.

8. A computer-implemented method according to any one of the preceding claims, wherein, The patient's medical parameters include CHADS2-VASc and / or HAS-Bleed.

9. The computer-implemented method according to any one of the preceding claims, wherein, Thrombosis risk factors derived from CFD calculations include one or more elements from a list comprising: mean blood flow velocity in the pulmonary ridge region, presence and mean blood flow velocity of eddies, and / or stagnant flow in the pulmonary ridge region and on the surface of the equipment, amount of particulate adhesion, and thrombogenic hemodynamic parameters; preferably wherein the thrombosis risk factors derived from CFD calculations include all elements from the list.

10. The computer-implemented method according to claim 9, wherein, The thrombotic hemodynamic parameters include endothelial cell activation potential values, and preferably also include hypercoagulable state and / or blood retention time values.

11. The computer-implemented method according to claim 10, wherein, The one or more thrombotic risk factors calculated based on the patient's thrombotic markers are determined by comparing each thrombotic marker value with one or more risk thresholds for each thrombotic marker.

12. The computer-implemented method according to any one of the preceding claims, wherein, The CFD calculation takes one or more physiological parameters from a list including the following as input: pulmonary venous pressure, left atrial pressure, Doppler ultrasound data, and hematocrit level, preferably wherein the CFD calculation takes all elements in the list as input.

13. The computer-implemented method according to any one of the preceding claims, wherein, The CFD calculation is performed under simulated motion of the patient’s left atrium (30), preferably wherein the motion is simulated using the arbitrary Lagrange-Euler method (ALE).

14. When claim 12 is invoked, the computer-implemented method according to claim 13, wherein the simulation of motion comprises the following steps: a. Compare the 3D model of the left atrium (30) of the patient with a database of 3D models of the left atrium (30) in motion of other patients obtained from dynamic computed tomography images of the left atrium (30) in motion of other patients; b. Select the most similar 3D model of the left atrium (30) from the database; c. Motion interpolation extracted from the 3D model of the most similar left atrium (30) selected in step b) is applied to the geometry of the left atrium (30) of the patient; as well as d. The motion of the patient's left atrium (30) is simulated using the interpolation from step c) to the motion of the patient's left atrium (30), the arbitrary Lagrange-Euler method (ALE), and the patient's CFD physiological parameters as inputs.

15. A computer program comprising instructions that, when executed by a computing device, causes the computing device to perform the method of any one of the preceding claims.

16. A data processing apparatus comprising a processing unit and a storage unit, wherein the storage unit includes instructions that, when executed by the processing unit, configure the data processing apparatus to perform the method of any one of claims 1 to 14.

Images