Systems, methods, computer programs, and computer program products for automated planning of clinical interventions for heart valve implantation.

Abstract

The present invention relates to a system, method, computer program, and computer program product for automated planning of clinical interventions for implanting heart valves in patients. The system (100) comprises an input interface (101) for inputting data and a computing unit (102). The computing unit (102) receives input data relating to patient characteristics via the input interface (101) and automatically selects surgical instruments (VT, DT) and surgical method characteristics (AS, VP, PS) based on the patient characteristics, and the surgical instruments (VT, DT) and surgical method characteristics (AS, VP, PS) are suitable for implanting heart valves in patients and are adapted to provide output data relating to at least the selected surgical instruments (VT, DT) and selected surgical method characteristics (AS, VP, PS).

Description

Technical Field

[0001] The present invention relates to a system, a method, a computer program, and a computer program product for automatically planning a clinical intervention for implanting a heart valve in a patient, as described in the independent claims.

Background Art

[0002] The implantation of a heart valve may relate to aortic valve replacement, mitral valve replacement, tricuspid valve replacement, or pulmonary valve replacement with an artificial heart valve. Current aortic valve replacement approaches include techniques that do not involve open heart surgery. Trans-catheter aortic valve implantation (TAVI) or trans-catheter aortic valve replacement (TAVR) is a minimally invasive procedure for treating a dysfunctional aortic valve. Inside the catheter, a valve implant (e.g., a bioprosthesis made of porcine pericardium sutured to a metal stent) is crimped. The catheter is inserted, for example, via the femoral artery and pushed upstream along the aorta to the native valve of the disease, where the valve implant is deployed. The main complications during implantation are vascular injury, stroke, cardiac injury (heart block, coronary artery occlusion, cardiac perforation), aortic valve regurgitation, cardiac conduction abnormalities, and valve displacement. Typically, the implantation process is image-guided and utilizes patient images taken and annotated during the planning stage. Identifying the best way to perform the procedure is time-consuming, requires considerable skill from the operator, and bears the risk of errors.

[0003] The size of the valve implant is related to the outcome of the surgical procedure. Making the valve implant smaller may cause paravalvular aortic regurgitation, while making it larger may result in rupture of the aortic valve annulus or suboptimal functional behavior of the implant.

[0004] International Publication No. 2019 / 179793 discloses a method and system for automatically selecting a cardiac implant from a set of cardiac implants, as well as a method for estimating the risk of complications.

[0005] International Publication No. 2006 / 092600 relates to the automatic planning of at least a portion of surgical procedures, in which a model is instantiated and surgical planning information is generated that is adapted to the actual anatomical structure of the patient.

[0006] International Publication No. 2014 / 139024 discloses a planning, navigation, and simulation system for minimally invasive treatment. The planning system allows for the development of multiple pathways from preoperative images and, in specific cases of brain surgery, scores the pathways according to the desired surgical outcome.

[0007] Known automated systems often relate to the relationship between the patient's anatomical structure and either the procedure or the hardware used. [Overview of the project] [Problems that the invention aims to solve]

[0008] The objective of the present invention is to overcome the shortcomings of the prior art. In particular, the systems and methods according to the present invention provide accurate preoperative planning in various aspects to improve the possibility of optimal clinical outcomes in clinical interventions for cardiac valve implantation. [Means for solving the problem]

[0009] These and other objectives are addressed by the systems, methods, and computer programs described in the independent claims.

[0010] According to the present invention, a system for automated planning of clinical interventions for implanting heart valves in patients comprises an input interface for inputting data and a computing unit.

[0011] The computing unit is adapted to receive input data regarding patient characteristics via an input interface.

[0012] The computing unit will be further adapted to automatically select surgical instruments and surgical method characteristics based on the patient's characteristics. These surgical instruments and surgical method characteristics will be suitable for implanting a heart valve in the patient.

[0013] Preferably, the computing unit is adapted to adjust the selection of surgical instruments and surgical method characteristics, that is, to select one considering the selection of options provided.

[0014] The computing unit is further adapted to provide output data relating at least to selected surgical instruments and selected surgical method characteristics.

[0015] Therefore, the system is well-suited to automatically associating patient characteristics with surgical instrument and surgical method characteristics while suggesting treatment techniques appropriate to the specific patient's anatomical structure.

[0016] Alternatively or additionally, the computing unit may be adapted to receive input data relating to patient characteristics and input data relating to surgical instrument and / or surgical method characteristics via an input interface. The computing unit may then be adapted to automatically determine risk values ​​based on patient characteristics and at least one of the surgical instruments and surgical methods, and the computing unit may be further adapted to provide output data relating to risk values.

[0017] The system could be a system for automated planning of transcatheter aortic valve implantation (TAVI).

[0018] Furthermore, the computing unit may be adapted to automatically select an imaging device and at least one imaging method characteristic, and the computing unit may be adapted to provide output data relating to the imaging device and imaging method characteristic.

[0019] The computing unit may be adapted to provide further outputs, such as ranking lists of different surgical instruments and surgical method characteristics, statistical data, or final reports.

[0020] For example, in the context of TAVI procedures, the computing unit can provide a list of several valve types and sizes ranked in terms of the risk of paravalvular leakage or conduction problems after implantation.

[0021] Furthermore, the computing unit can provide a ranking of access sites. In some patients, femoral access may carry a higher risk of vascular perforation than carotid artery access, or vice versa.

[0022] The input interface may include a communication interface for communicating with external devices such as an imaging device or an external expert system, and / or the input interface may enable communication with the user.

[0023] The input interface is preferably selected from the group consisting of a keyboard, voice interface, mechanical buttons or switches, mouse, joystick, tactile glove, graphical user interface, particularly touchscreen, motion detector, particularly eye-tracker or head motion detector, virtual reality or augmented reality interface, and 3D monitor, particularly tactile glove or holographic presentation combined with motion detection.

[0024] The computing unit can be adapted to provide output data before and / or during partially or fully automated surgical interventions.

[0025] The risk value may be related to or at least partially based on a valve, human error in the plan (due to obvious errors detected in the input), human error in the intervention (e.g., a selected valve or surgical method related to the high risk of each patient, based on the statistics of the patient group and / or a specific procedure), substances other than the valve, specific anatomical structures of the patient, e.g., reported postoperative complications due to abnormal meandering paths.

[0026] The risk value may also be based on or related to complications estimated, for example, based on information extracted from medical images, especially a large number of images, e.g., based on the average duration of the procedure. A large number of images may indicate difficult situations and time-consuming procedures.

[0027] The risk value may also be based on or related to complications estimated, for example, based on imaging parameters during the procedure. Sophisticated medical image information, such as the need for repeated C-arm angle changes, may be an indicator of complications.

[0028] The risk value can be evaluated by using training data based on previous interventions, which may provide clues about difficulties not explicitly reported. END]]

[0029] The training data may be based on physicians' reports stored in a database. The physicians' reports may be stored in a schematic form, or as a text file, or as an image file processed by OCR.

[0030] The computing unit may be adapted to determine the risk value based on at least one of imaging such as biomechanical modeling and US imaging of CT scans.

[0031] Generally, input data for risk determination can be obtained from databases independent of individual patients (model databases) and databases specific to individual patients (new case databases).

[0032] Model database data could include, for example, a statistical atlas of aortic anatomical structures built on a collection of CT images, a list of the types and sizes of implanted transcatheter heart valves, a list of instruments used during implantation procedures, and lists of short-term and long-term results.

[0033] A new case database may include CT images of the specific patient's anatomical structure, the patient's medical history, and the patient's medical history, including preceding conduction disorders, preceding strokes, arrhythmias, grade of stenosis, and / or other comorbidities. The new case database may also include a list of already selected instruments to be used in the procedure.

[0034] The system may include an output interface for receiving output data from a computing unit and providing that output data to the user and / or external devices.

[0035] The output interface can be selected from a group of options including optical or visual output interfaces, augmented reality displays, virtual reality displays, haptic feedback devices, or acoustic output interfaces.

[0036] The output interface can enable the presentation of output data so that the user can react to the output data. The output device may be part of the input interface, or it may be integrated into an interface device that includes an input interface.

[0037] The output interface may or may be connected to a surgical robot for performing surgical procedures.

[0038] The input data can be selected from a group consisting of imaging data, particularly 2D and / or 3D imaging data and / or 2D slice imaging data, characteristics related to access to and closure of blood vessels, characteristics related to pacing, characteristics related to imaging, characteristics related to navigation, characteristics related to valve positioning and placement, characteristics related to commissure alignment, and characteristics related to surgical instruments and / or surgical methods.

[0039] 3D imaging data may include, for example, one or a combination of MRI (Magnetic Resonance Imaging) data, CBCT (Cone-Beam Computed Tomography) data, multi-view ultrasound data, 3D ultrasound data, and surface scan data, obtained using a pressure sensor or balloon.

[0040] The imaging data may be provided directly by the imaging device, or it may be provided as preoperative images acquired from a storage device.

[0041] Characteristics related to access and closure may include data on the presence of femoral artery disease, e.g., calcification or plaque, e.g., the degree, location and dimensions of assumed calcium deposition; data on the presence of previous access; data on the distance of the vessel from the skin; and data on the presence of aneurysms and / or thrombosis.

[0042] Pacing-related characteristics may include data on calcification, such as a history of conduction disturbances if an existing pacemaker is present, or data on the size and location of the intermembrane septum. Pacing-related characteristics may also include data on the shape of the aortic root and left ventricle. For a given shape, pacing may be more or less efficient. For example, anatomical constraints may limit contact with the pacing wire, and therefore, optimal pacing may not be achieved.

[0043] Characteristics related to imaging, for example, imaging during navigation, or interpretation of input images may relate to (i) the shape of the aorta, such as tortuosity, and / or (ii) the condition of the aorta, such as the presence of aneurysms and / or thrombosis, calcification and / or plaque.

[0044] Characteristics related to imaging may also relate to limited access to positioning the imaging device due to anticipated contrast agents, such as contrast agent intolerance, or the need for specific patient positioning. Characteristics related to imaging may also relate to conditions that necessitate or desirable the introduction of contrast agents to visualize more complex structures, such as anatomical conditions.

[0045] Navigation-related characteristics may relate to (i) the shape of the aorta, such as tortuosity, diameter, and morphological abnormalities; (ii) the condition of the aorta, such as the presence of aneurysms and / or thrombosis, calcification and / or plaque, and the impedance, volume, stiffness and shape of calcifications; and / or (iii) strain in the assumed contact area with the valve.

[0046] Characteristics related to valve implantation may include the characteristics of the innate valve being functionally replaced, such as its shape (e.g., diameter), as well as the condition of the innate valve, such as the type of dysfunction or grade of calcification and commissure orientation.

[0047] Characteristics related to valve implantation may include the type of valve to be implanted, and the target position and / or target orientation of the implanted valve relative to the original valve that is functionally replaced, in particular the intended implantation depth, inclination, and orientation of the implanted valve.

[0048] Characteristics related to valve placement may include, in particular, the shape of the left ventricular outflow tract, which can affect the depth of the implanted valve.

[0049] Characteristics related to valve retention may relate to the shape, stress and strain at the intended contact surface with the valve retention, anticipated conduction disturbances, and / or anticipated leaks.

[0050] If the implant size is inappropriate, conduction problems and leakage may occur. Ideally, conduction problems and / or leakage can be predicted at the planning stage based on the patient's anatomical structure, particularly based on strain and stress at the contact surface. Therefore, predicted conduction problems and / or leakage are secondary parameters.

[0051] The properties associated with valve placement may also relate to the risk of rupture and / or migration, which are secondary parameters that can be calculated from stress and strain.

[0052] Characteristics related to commissure alignment may relate to the intended orientation of the implant valve at the implant site and / or the orientation of the implant valve within the delivery device, for example, the orientation relative to the valve compression position which may be adjustable and / or must be considered.

[0053] Another aspect of the present invention relates to a system, in particular to the aforementioned system comprising a control unit. The control unit may be adapted to receive pre-processed input data relating to patient characteristics via an input interface. The pre-processed input data is preferably selected from the group consisting of (i) 3D representations, particularly of the aortic root, left ventricle, sinus junction ring, aortic ring, and / or calcification; (ii) characteristics indicating recommended surgical instruments, recommended surgical method characteristics, and recommended risk values ​​based particularly recommended surgical instruments and recommended surgical method characteristics; (iii) characteristics related to the location and / or orientation of the coronary orifice; (iv) characteristics related to pacing; (v) characteristics related to imaging; (vi) characteristics related to navigation; (vii) characteristics related to valve selection; (viii) characteristics related to valve positioning and placement; and (ix) characteristics related to commissure alignment.

[0054] The valve selected may be an artificial valve, particularly an artificial catheter-based heart valve. This allows for the inclusion of both input and pre-processed input data regarding patient characteristics in order to more accurately select surgical instruments and surgical method characteristics, as well as to determine risk values ​​associated with surgical instruments and surgical methods. Using both input and pre-processed input data enables cross-referencing and validation of the provided output data by the computing unit, making the system more robust by compensating for potential biases and errors. In particular, the pre-processed data may be provided from other external sources.

[0055] The pre-processed input data can include multiple recommended surgical instruments, particularly recommended valves, and corresponding multiple recommended surgical procedure characteristics, as well as recommended risk values ​​associated with each of the particularly recommended surgical instruments and surgical procedure characteristics. This allows clinicians to make more informed decisions by comparing recommended instruments, surgical procedure characteristics, and risk values ​​to determine potential risks.

[0056] The characteristics of valve positioning and placement may include information regarding the contact between the valve and the patient's innate anatomical structures, particularly concerning paravalvular leakage and conduction disturbances resulting from the contact pressure of the valve against the innate anatomical structures.

[0057] The input interface can be adapted for bidirectional communication with an external processing system by transmitting input data to the processing system and receiving pre-processed input data from the processing system.

[0058] This allows existing external systems to be connected via an input interface, thus reducing system complexity and demanding computing resources. Furthermore, it provides simple feedback between the system and the external processing system, particularly in real time. The processing system can be a cloud-based system. The system and the external processing system can be adapted to communicate via a REST API.

[0059] The computing unit can use a fusion algorithm to align, integrate, and merge the 3D imaging data and pre-processed 3D representations of the input data to create a unified 3D representation of the patient's characteristics. This is particularly useful when the processed 3D information is received from an external system in a format that does not contain all the desired information, or is not suitable for displaying the data, for example, when an actual 3D display is desired.

[0060] Therefore, this integrated 3D representation enables a more comprehensive model that can provide more accurate output data based on both input data and pre-processed input data. The integrated 3D representation can facilitate surgical navigation of medical devices or navigation simulation of medical devices by providing, for example, 3D information of the aorta based on input data and 3D information of the aortic root based on pre-processed input data.

[0061] The computing unit may be further adapted to include two-dimensional or three-dimensional data, particularly two-dimensional fluoroscopy data showing the position of medical instruments relative to the patient's anatomical structure, in a real-time integrated 3D representation during surgery. The computing unit may be adapted to align, integrate, and merge the fluoroscopy data with the integrated 3D representation. The computing unit may be further adapted to adjust the integrated 3D representation based on the fluoroscopy data, particularly to account for patient movement due to the cardiac or respiratory cycle.

[0062] The computing unit may be adapted to provide output data for displaying an integrated 3D representation on a 3D graphical user interface on a 3D display device.

[0063] The 3D graphical user interface may be adapted to display output data regarding selected surgical instruments, selected surgical method characteristics, and in particular, associated risk values ​​or potential associated complications. The integrated 3D representation displayed by the 3D graphical user interface may preferably further include virtual visual representations of the surgical instruments and / or surgical method characteristics.

[0064] The 3D graphical user interface may be adapted to be operated by a clinician by (i) zooming in or out of sections of the integrated 3D representation, (ii) changing the field of view of the integrated 3D representation, and (iii) changing the selected surgical instrument and surgical method characteristics. The 3D graphical user interface may also be adapted to be adapted in an automated manner to select and display the respective risk values ​​or potential complications associated with the selection of surgical instruments and / or surgical method characteristics.

[0065] Another aspect of the present invention relates to the aforementioned 3D graphical user interface. The computing unit may be adapted to select surgical instruments from a group consisting of indwelling devices and valves.

[0066] The implantation device may include, in particular, guidewires, such as robotic guidewires, introducer kits, catheters, robotic manipulators such as growth robots, and / or delivery devices.

[0067] Furthermore, access devices, closure devices and / or imaging devices, as well as related characteristics such as the type, shape, and size of the access and closure devices, and the type and size of the imaging devices, can be selected.

[0068] Furthermore, preparatory devices such as an expansion balloon for dilation before implantation may be selected separately from the delivery device.

[0069] This system proposes optimized surgical instruments for safe and efficient intravascular intervention.

[0070] Output data for surgical instruments may include instrument characteristics selected from a group of parameters such as stiffness, length, diameter, brand, model and type of delivery devices such as TAVI catheters, shape, diameter and / or parameters of the development workflow of growing robots, shape, orientation, size, brand, model and / or type of valve implants, and size, shape, brand, model and / or type of puncture needles.

[0071] The orientation of a valve implant may relate to the orientation of the implanted valve within the delivery device, such as its orientation relative to the valve holder and / or delivery catheter.

[0072] The parameters of the development workflow for a growing robot can include the number and timing of steps, as well as the pressure applied.

[0073] Since surgical method characteristics may depend on surgical instruments, and vice versa, output data regarding surgical method characteristics can be correlated with output data regarding surgical instruments.

[0074] Similarly, the computing unit may be adapted to combine appropriate instrument and surgical method characteristics to minimize the risks of the procedure. The selection of the first instrument may depend on the selection of further instruments, and the selection of the first surgical method characteristics may depend on the selection of further surgical method characteristics.

[0075] Output data regarding surgical procedure characteristics may include simulations of valve implant access, navigation, and / or deployment, particularly the complete clinical intervention.

[0076] The output data regarding surgical method characteristics is: - Access site, - Information regarding the placement of temporary pacemakers.

[0077] - Information regarding stent pre-placement, - Information regarding the puncture route, - Information regarding the expansion process during the access phase, particularly information regarding the access port expansion process. - Information regarding navigation routes, - Information regarding the final position of valve implants, - Information regarding the optimized orientation and / or shape of valve implants, - Expansion parameters, -The method may include characteristics that are selected from a group of imaging commands.

[0078] Furthermore, the output data regarding surgical method characteristics may include method characteristics related to the closure process.

[0079] In a preferred embodiment, the output data relating to the surgical method characteristics includes navigation path information indicating how the valve is aligned / moved with respect to the congenital aortic valve / annulus. The navigation path information may include (i) the inclination of the valve's longitudinal axis relative to the congenital aortic valve / annulus, (ii) the circumferential orientation of the valve relative to the congenital aortic valve / annulus / coronary artery, and / or (iii) instructions regarding how to adjust the translational motion of the valve across the congenital aortic valve / annulus, particularly regarding valve advancement on medical devices such as guidewires that may be placed in the left ventricle.

[0080] The instructions may be commands for the robot controller's actuators or instructions for the clinician.

[0081] By determining the tilt adjustment, it may be possible to optimize alignment and position the prosthetic valve at an angle corresponding to the innate valve / aortic annulus orientation to prevent, for example, paravalvular leakage or valve migration.

[0082] Alignment of the circumferential orientation of the valve relative to the natural valve / aortic annulus / coronary artery can further optimize the alignment of the prosthetic valve, especially for asymmetrical natural valves, such as those with non-circular cross-sectional dimensions. This alignment of the circumferential orientation of the valve can also enable optimized alignment for coronary orifice eccentricity. This allows for precise deployment of the valve to the patient-specific non-circular symmetric cross-section of the natural aortic valve / aortic annulus. Furthermore, this ensures that the coronary orifice, which supply oxygen-rich blood to the myocardium, is not obstructed and remains accessible for anticipated intensive coronary interventions.

[0083] Navigation pathway information, including instructions on how to translate the valve relative to the natural aortic valve / annulus, is crucial to prevent plaque / calcification dislodgement while traversing the natural valve. Navigation pathway information can also include information on how to adjust the position and / or orientation of medical devices, such as guidewires, to optimize traversal of the natural valve and deployment of the prosthetic valve, particularly how to deploy the medical device into the left ventricle. Furthermore, the information can include information on paravalvular leakage or conduction issues, particularly related to implantation depth and / or specific valve types. This may reduce the incidence of permanent pacemaker implantation.

[0084] Information regarding the access site may include the location of the access site and specifications for the access site for each device, such as pacing access, pigtail access, access for stent pre-implantation, or auxiliary secondary access.

[0085] Typically, the location of the access site is determined and output for each device. For example, a pigtail is inserted from a pacing device via a radial access site, a primary femoral access site, or a secondary femoral access site.

[0086] Depending on the type of device, a specific location for access may be required. For example, a temporary pacemaker may be needed via venous access.

[0087] The access site may be linked to a parameter that indicates the access site on the patient and / or on the image.

[0088] Information regarding the placement of a temporary pacemaker and information regarding stent pre-implantation may also include the presence of a stent and / or pacemaker, and, if applicable, the type, location, and / or access site of the stent and / or pacemaker.

[0089] The puncture route may take into account the presence of stents and / or calcifications. The puncture route plan should ensure a route free from anticipated collisions with already implanted devices, such as stents. Needle orientation may need to be adapted. Therefore, information regarding the puncture route may include information regarding needle orientation.

[0090] Information regarding the puncture route may include parameters for controlling the puncture device. Information regarding the dilation process during the access phase may be optimized by taking into account (i) the depth of the artery, (ii) the presence of calcification, (iii) the mechanical properties of the vessel, and / or (iv) the shape of the surgical instrument and / or at least one of the mechanical properties of the surgical instrument.

[0091] Information regarding the expansion process may include parameters for controlling the expansion device during the access phase, such as selecting or setting the size of the introducer.

[0092] The computing unit can be adapted to compute a 3D geometric model of the calcification distribution and / or the location and / or access areas of the calcifications.

[0093] The computing unit may be adapted to combine at least one of the following: a biomedical model for predicting deformation and associated risks, a calculated calcification distribution, a calculated calcification location, and a 3D geometric model of the patient, particularly the access area.

[0094] Information regarding the navigation route can be optimized considering the selected surgical instruments and the patient's characteristics, particularly their anatomical structure.

[0095] The navigation route may be optimized by taking into account at least one of the following: (i) calcification, (ii) abnormal geometric shape of the blood vessel due to, for example, an aneurysm or thrombosis, (iii) shape and mechanical properties of the blood vessel, and (iv) shape and mechanical properties of the selected surgical instrument.

[0096] Preferably, the computing unit is adapted to use at least one of a parametric shape model and a biomedical model to predict vascular deformation and / or indicate the location of calcifications and plaques extracted from images.

[0097] Such models can be used to determine the navigation route, the puncture route, and to plan the dilation and prepare the innate valve for implantation.

[0098] Before a new valve can be placed, it may be necessary to break up and open the calcified original valve using an expansion device.

[0099] Information regarding the navigation path can include parameters for controlling navigation devices such as robot guide wires.

[0100] Information regarding the expansion parameters may include details about the type of valve expansion, such as active or passive valve expansion, details about the degree of valve expansion, and details about the need for pre-expansion and / or post-expansion.

[0101] Information regarding the deployment parameters may include balloon pressure for valve implant expansion, pre-expansion, and / or post-expansion, if applicable.

[0102] Information regarding deployment parameters can include parameters for controlling the deployment device.

[0103] Imaging instructions may include optimized imaging time and / or radiation dose according to the patient's anatomical structure.

[0104] For example, fluoroscopy may be omitted for segments of anatomical structures that are easy to navigate.

[0105] The imaging instructions may include instructions regarding the injection of contrast agent, particularly the timing of injection, the target area, and / or the amount of contrast agent, in order to create complex visible segments.

[0106] Information regarding the final position of the implanted valve, its navigation route, and the orientation and / or shape of the optimized implant may also be related to commissure alignment, which is useful for facilitating coronary artery access after transcatheter heart valve implantation.

[0107] Information regarding the navigation route may include information regarding the angle for inserting the delivery device into the blood vessel and / or implant location in order to orient the implant valve in an appropriate manner.

[0108] Optimal implant placement and orientation enable access to the coronary arteries.

[0109] The output data regarding surgical method characteristics may further include risk parameters. Risk parameters specify factors that could potentially cause the anticipated risk.

[0110] Risk parameters may indicate the risk of undesirable events during clinical intervention, such as vascular perforation, vascular dissection, calcium deposition displacement, valve embolism, leakage, conduction problems, coronary artery occlusion, particularly those associated with suboptimal valve positioning, annular ring rupture, particularly those associated with pre- and post-diastolic and diastolic movements, and cardiac tamponade, particularly those associated with the manipulation of rigid guidewires.

[0111] Risk parameters may include a combination of morphological and mechanical properties of the patient's anatomical structure related to the selected surgical instrument and / or method.

[0112] Risk parameters may include areas of the patient population that require particular attention. In each region, there is a risk of plaque rupture, calcification displacement, vascular dissection, and / or vascular perforation, particularly during navigation or access or retraction.

[0113] Each region may be automatically recognized on the image through image analysis. Image analysis may include region detection in fluorescence fluoroscopy images or region detection in CT scans, as well as image fusion of CT and fluorescence fluoroscopy images.

[0114] The risk of plaque rupture can be quantified by location, surface stiffness, and / or contact stress. The risk of calcification dislocation can be quantified by the location and amount of calcification. The risk of vascular dissection can be quantified by wall stiffness, the presence of calcification, and / or contact stress between the instrument and the vascular wall.

[0115] Strain / stress values ​​at specific locations, such as wall stiffness, as well as the thickness and location of calcification, can be calculated based on biomechanical models.

[0116] Risk parameters may include stress and / or strain specifications. These quantities correspond to the risk of rupture or dissociation.

[0117] The computing unit may be adapted to determine stress and / or strain based on biomechanical modeling and imaging such as ultrasound (US) imaging and / or computed tomography (CT) scans.

[0118] Risk parameters may include local and / or regional arterial morphology, including tortuosity. The local and / or regional shapes of each artery can be automatically recognized on the image by image analysis performed using a computing unit.

[0119] The computing unit may be adapted to determine the local and / or regional shape of arteries based on at least one of the following: (i) centerline curvature measurements calculated from, for example, preoperative CT scans or contrast-enhanced fluoroscopy; (ii) surface mesh; (iii) shape parameters particularly related to local arterial tortuosity; (iv) determining intra-patient versus patient variability; and (v) identifying areas / regions of high risk.

[0120] Risk parameters can be quantified by the number of bends relative to a standard "straight" anatomical structure and the angle of each of those bends.

[0121] Certain combinations of bends and / or specific curvature patterns can be associated with a high risk and should be avoided during navigation.

[0122] Shape parameters can be stored as a statistical atlas in memory that can be accessed by the computing unit.

[0123] Risk parameters may include morphological or anatomical abnormalities, particularly morphological or anatomical abnormalities of blood vessels and / or congenital valves.

[0124] Shape or anatomical abnormalities can be identified using an atlas. Shape parameters describing the size of an abdominal aneurysm can be determined, for example, based on the patient's 3D shape. These shape parameters can be determined using Principal Component Analysis (PCA) on the 3D shape. Partial Least Squares (PLS) regression can be used to estimate the aneurysm size and correlate it to each PCA model.

[0125] This model can be directly applied to the 3D geometry of a new patient to identify and extract the size of an aneurysm. The same procedure can be used for other geometric anomalies.

[0126] Each shape or anatomical abnormality can be automatically recognized on the patient's image through image analysis performed using a computing unit.

[0127] The computing unit may be adapted to determine shape or anatomical abnormalities based on at least one of the following: (i) using a standard deviation from the mean population shape or anatomical structure or a standard deviation from a subgroup population; (ii) extracting vessel diameters, preferably based on preoperative CT and US imaging; (iii) online estimation by contact with a guidewire; and (iv) blood flow analysis, particularly by detecting deviations from normal flow.

[0128] Risk parameters may include the distribution and / or amount of calcification. The output data may also be relevant to risk values ​​and / or risk predictions resulting from the risk analysis, particularly based on risk parameters.

[0129] The computing unit may be adapted to determine risk parameters based on at least one of the following: (i) preoperative CT (computed tomography) or US (ultrasound) imaging segmentation to extract pixel-level positions, and (ii) inline stiffness estimation from preferably highly sensitive impedance / capacitance of the arterial wall.

[0130] The computing unit may be adapted to analyze preoperative CT or US images using AI (Artificial Intelligence) software and / or conventional methods.

[0131] The computing unit can be further adapted to determine and output an overall risk rate, risk probability, and / or risk map based on risk parameters.

[0132] Risk probability is a measure of the likelihood of a risk occurring. In the case of identified risks, the computing unit may be adapted to provide improved or alternative output data, particularly regarding surgical methods, including alternative routes and / or alternative devices and / or manual or surgical placements.

[0133] The computing unit may be adapted to provide at least one parameter for controlling the robot guidewire along an alternative path.

[0134] The computing unit may be adapted to automatically select or recommend surgical instruments and / or surgical method characteristics by considering an optimized combination of one or more instruments and / or one or more method characteristics that minimize the risks of the procedure and / or maximize the expected results.

[0135] The maximized results may relate to the maximum pressure gradient caused by the implanted valve, minimum radiation exposure, minimum treatment time, maximum flow rate of the replaced valve, and optimal orientation of the replaced valve.

[0136] Additionally or alternatively, the computing unit is adapted to automatically select surgical instrument and surgical method characteristics by considering the presence of stents and / or calcified areas at high risk of rupture or migration.

[0137] Calcified areas can pose a risk of rupture, and therefore a risk to the entire procedure. The computing unit may be adapted to receive user feedback via an input interface. This feedback may be validation feedback or modification feedback.

[0138] The computing unit may be adapted to use user feedback for planning. In response to the feedback, the computing unit may be adapted to iterate on selections, confirm selections, and / or modify selections. The feedback may trigger changes to selected routes and / or selected combinations of equipment.

[0139] The user can change the automatically selected surgical instruments and surgical method characteristics. For example, if areas to be avoided are suggested due to risk parameters such as the rate of calcification, and stress and strain occur in the interaction with surgical instruments, the user can provide feedback by manually correcting the path to be taken.

[0140] The user may choose to avoid the area that should be avoided, or they may choose to pass through this area anyway.

[0141] Users can also force the use of a pacemaker. The computing unit may be adapted to calculate the risk of a procedure using a given surgical instrument and surgical method characteristics. In particular, the computing unit may be adapted to calculate the risk of a procedure using surgical instrument and surgical method characteristics suggested by user feedback.

[0142] The computing unit may be adapted to create virtual models representing the body parts involved in the procedure. These virtual models may be created based on patient characteristics. The computing unit may also be adapted to automatically select surgical instruments and surgical procedure characteristics based on these virtual models.

[0143] The creation of a virtual model may include methods for morphing an anatomical template into a set of points representing the body parts involved in the procedure, particularly landmark fitting and parametric surface fitting, at least one of these.

[0144] The computing unit can be adapted to annotate CT (computed tomography) images, fluoroscopy images, and / or US (ultrasound) images on a pixel-by-pixel basis, for example, in a partially or fully automated manner.

[0145] The computing unit may be adapted to merge multiple images to provide a whole image. The multiple images may be images of the same region provided by different imaging sources.

[0146] Multiple images may also be provided by the same imaging device, but using different techniques or different projection planes or imaging angles.

[0147] The computing unit may be adapted to automatically detect or define high-risk areas as regions with constricted blood vessels and / or regions with high concentrations of calcification having a given shape of calcification component. The computing unit may be adapted to determine risk factors using statistical methods by correlationing anatomical characteristics with known clinical outcomes.

[0148] The mechanical properties of blood vessels, such as rigidity, can be considered in combination with their respective geometries. For example, the distribution of calcification within the annular ring defines the overall annular ring rigidity, which can ultimately influence valve selection.

[0149] The computing unit may be adapted to automatically select or recommend surgical instrument and surgical method characteristics based on at least one database describing anatomical features, including geometric features, particularly the shape of blood vessels, the length of blood vessels, the diameter of blood vessels, the thickness of blood vessel walls, and / or other non-geometric characteristics such as missing or additional typical blood vessels.

[0150] Patient information can be stored in a specific database structure that enables rapid analysis, quick patient comparison, and optimal information retention.

[0151] The data structure can include multiple templates that take into account geometric shapes reflecting the patient's topology.

[0152] In the case of geometric shapes that reflect the patient's topology a) Branch defects, for example, branch defects of the deep femoral artery, b) Additional arteries, e.g., some patients have two or more renal arteries, and / or c) Brachiocephalic artery topology This can be taken into consideration.

[0153] The data structure may include a shape atlas for each template. The shape atlas can capture changes in the anatomical shape of a population related to a given topology, for example, diameter, length, and meandering.

[0154] The database can be obtained by differential homeomorphic fitting of a template to annotations extracted from the patient's medical images, according to the patient's topology.

[0155] The computing unit may be adapted to automatically select or recommend surgical instruments and surgical method characteristics based on at least one of the following: a database describing changes in anatomical 3D (three-dimensional) shape, and a database describing anatomical changes other than shape, such as defects or additional arterial branching.

[0156] A database describing changes in anatomical 3D shape may be created based on topology, or may be based on or include a statistical atlas describing changes in shape of a given population.

[0157] The database may include multiple templates related to patient topology, along with a statistical atlas for each template.

[0158] The database may include at least one representation method selected from parametric mesh surfaces, landmarks, and 3D representations of bodies, particularly 1D and / or 2D surface representations.

[0159] A database describing anatomical changes excluding shape can include multiple templates that describe changes in anatomical features excluding shape for a given population.

[0160] The computing unit may be adapted to automatically select or recommend surgical instrument and surgical method characteristics based on anatomical features within each set of templates, particularly at least one, preferably multiple, statistical atlases describing morphological changes in at least a portion, preferably the entire aorta.

[0161] A statistical atlas describing changes in the shape of anatomical features in a given population may include tortuosity and diameter, as well as shape modes associated with difficult anatomical structures or anticipated complications.

[0162] Such shape modes may include specific diameter variations in certain regions that are exceptional compared to statistical models. Shape modes may also include difficult anatomical structures defined as deviating by a predetermined range from the population mean shape, or they may include specific combinations of meanders compared to statistical models.

[0163] The computing unit can be adapted to automatically select or recommend surgical instrument and surgical method characteristics based on multiple templates describing topological variations of the aortic anatomical structure.

[0164] Surgical instruments or methods may be selected, for example, by pure geometric analysis by valve selection, and / or by statistical analysis including comparison of new shapes with shape atlases, and / or by using biomechanical models to predict anticipated outcomes and / or complications such as vascular dissection, improperly fitted implants, conduction abnormalities, moderate / severe paravalvular leakage, injury, particularly acute kidney injury, and / or stroke.

[0165] In the context of statistical analysis, the probability that a particular instrument is unsuitable for a patient can be based on knowledge of how that instrument functions in a population or subgroup of patients with similar shapes.

[0166] The computing unit may be adapted to automatically select surgical instrument and / or surgical method characteristics based on multiple templates and / or multiple statistical atlases along with an integrated 3D representation. The computing unit may be adapted to determine a template / statistical atlas from among multiple templates / statistical atlases based on the greatest similarity with the integrated 3D representation.

[0167] Using a PCA model, the number of patient characteristics required to quantify the differences between templates / statistical atlases can be reduced. The computing unit can determine similarity by projecting the integrated 3D representation onto the principal component space of the PCA model, and can be adapted to quantify the similarity between multiple templates / statistical atlases and the integrated 3D representation using k-means algorithms and, for example, Mahalanobis distance. This allows for the easy and reliable identification of the template / statistical atlas closest to the integrated 3D representation.

[0168] The computing unit can be adapted to automatically incorporate new patient templates / statistical atlases into the database to provide more accurate data for future patients. In particular, the statistical atlas can include and provide the geometric shape of the valve used during the patient's valve implantation and associated complications.

[0169] The template includes, in particular, at least one representation method selected from parametric mesh surfaces, landmarks, and 3D (three-dimensional) representations of bodies, and in particular includes 1D (one-dimensional) landmark and 2D (two-dimensional) surface representations.

[0170] The computing unit can be further adapted to classify patients by comparing their characteristics with multiple average templates.

[0171] Patients can be compared to existing patients considered in the database using a template.

[0172] The nearest category is used to classify new patients. This category can be used to define the type of surgical method or surgical instrument used.

[0173] The database may contain averaged data on characteristic data of specific areas related to clinical interventions. The database may also include the patient's 3D anatomical structure, as well as the instruments used during the procedure and the outcomes obtained.

[0174] This information can be used to create statistical models with good predictive power for new patients.

[0175] The computing unit can be adapted to query the database. Using the appropriate template, you can create, store, and query databases.

[0176] The computing unit may be adapted to determine local deformations on the patient's body resulting from forces applied to areas of the patient's body by external devices, particularly by selected instruments.

[0177] Biomechanical modeling can be used to determine local deformation. The computing unit may be adapted to determine local deformations based on at least one of a statistical atlas of deformation, a biomechanical model that takes tissue properties and anatomical constraints, and local non-rigid registrations between a template and collected data, preferably online collected data.

[0178] To use a biomechanical model, geometric shapes, tissue properties, and applied forces can be utilized.

[0179] Alternatively, non-rigid registration may be driven by observation on real-time images. Biomechanical properties of tissue, such as arterial calcification or stiffness due to thickness, may limit movement.

[0180] Determining or predicting local deformation can be used as a criterion for selecting appropriate instruments and appropriate surgical method characteristics, for example, for expansion during access or inflation during deployment, or for advancing the delivery instrument during navigation.

[0181] Local non-rigid registration can be based on at least one of the following: (i) fitting a parametric template to a 3D representation containing landmarks using corresponding points such that the measured landmarks in the 3D representation are fitted to the corresponding points in the parametric template; and (ii) determining a parametric model that describes the correspondence between deformations displayed in two dimensions and deformations in a three-dimensional representation, using at least one of a 3D shape model, tissue properties, and anatomical constraints for transferring features from a 2D image to a 3D representation.

[0182] Two-dimensional presentation can correspond to projections or sliced ​​presentations of the patient region. For example, the patient's shape, biomechanical properties, and boundary conditions related to surrounding organs are relevant to the conversion from 2D images to 3D images.

[0183] Assuming that the patient's 3D shape and the "camera position" of the 2D images are known, it is possible to extract local deformations from each 2D image.

[0184] Furthermore, based on biomechanical models, deformations extracted from 2D images can be locally transformed into 3D deformations.

[0185] The computing unit can be further adapted to relate patient characteristics, particularly patient-specific anatomical structures or pathologies, to the geometric and / or mechanical properties of surgical instruments.

[0186] The computing unit can be adapted to correlate patient characteristics with surgical instrument characteristics based on statistical shape analysis combined with partial least squares regression (PLSR) on patient profiles related to the specific design of instruments such as delivery devices, catheters, or valves.

[0187] Patient characteristics can be defined by shape, patient history, and clinical parameters. PCA can be used to reduce the number of characteristics.

[0188] The database could include information on which instruments were used for which "patient types," based on the experience of professionals who made the best choices for a given patient.

[0189] PLSR can be used to define a patient's phenotype by matching patient characteristics against a database. New patient data is compared to the database. Instruments corresponding to determined patients already existing in the database may be recommended, and determined patients are selected from the database such that the distance between the new patient's characteristics and the determined patient's characteristics is minimized.

[0190] The computing unit can be further adapted to associate patient characteristics with surgical method characteristics, such as maneuvering commands for navigation and commands regarding pressure applied during expansion.

[0191] The computing unit may be adapted to associate patient characteristics with surgical instrument characteristics, for example, based on geometric metrics that characterize meandering.

[0192] The computing unit can be adapted to sequentially provide output data for each stage of a clinical intervention, according to a sequence corresponding to the sequence of the clinical intervention.

[0193] A clinical intervention may involve multiple steps. A stage of a clinical intervention may include one or more subsequent steps of the clinical intervention.

[0194] The stages may, for example, relate to the use of specific equipment. The first stage may relate to preparation for clinical intervention, and the appropriate valve and deployment device should be selected. Subsequent stages may relate to access, and the access site and access device should be selected. Subsequent stages may relate to navigation, and the characteristics of the navigation device and / or navigation route and their respective control parameters should be selected. Subsequent stages may relate to the positioning, deployment, and testing of the valve implant, and their respective control parameters should be selected for each. Subsequent stages may relate to the retraction of the device, and the final stage may relate to vascular closure, for which the respective devices and / or their respective control parameters should also be selected.

[0195] The computing unit may be adapted to sequentially provide output data for each stage during the clinical intervention, either before executing each stage or during the planning stage prior to the clinical intervention. The output data for each stage may also be based on information collected during the previous stage.

[0196] The computing unit can be adapted to automatically provide checklists.

[0197] In particular, checklists may be provided after the completion of selections at each stage and / or before the commencement of each stage of clinical intervention.

[0198] According to another aspect of the present invention, a method for computer-based automated planning of a clinical intervention for implanting a heart valve in a patient includes (i) receiving input data relating to the patient's characteristics via an input interface, (ii) automatically selecting or recommending suitable surgical instruments and surgical method characteristics for implanting a heart valve in the patient, and (iii) providing output data relating to at least the selected or recommended surgical instruments and surgical method characteristics. The selection is made by a computing unit based on the patient's characteristics.

[0199] Clinical intervention may involve transcatheter aortic valve implantation. Additionally or alternatively, input data regarding patient characteristics, as well as input data regarding surgical instrument and / or surgical method characteristics, may be received via the input interface. Risk values ​​may be determined based on the characteristics of the patient and at least one of the surgical instrument and / or surgical method characteristics. Output data regarding risk values ​​may be provided.

[0200] Preferably, the system described above is used. The input data can be selected from the group consisting of imaging data, particularly 2D and / or 3D imaging data, characteristics related to access and closure, characteristics related to pacing, characteristics related to imaging, characteristics related to navigation, characteristics related to valve positioning and placement, characteristics related to commissure alignment, and characteristics related to surgical instruments and / or surgical methods. Details of the input data are the same as above.

[0201] The method may include a control unit receiving pre-processed input data relating to patient characteristics via an input interface. The pre-processed input data may be selected from the group consisting of (i) 3D representations, particularly of the aortic root, left ventricle, sinus junction ring, aortic ring, and / or calcification; (ii) characteristics indicating recommended surgical instruments, recommended surgical method characteristics, and recommended risk values ​​based on particularly recommended surgical instruments and recommended surgical method characteristics; (iii) characteristics relating to the location and / or orientation of the coronary orifice; (iv) characteristics relating to pacing; (v) characteristics relating to imaging; (vi) characteristics relating to navigation; (vii) characteristics relating to valve selection; (viii) characteristics relating to valve positioning and placement; and (ix) characteristics relating to commissure alignment.

[0202] The method may include the input interface communicating bidirectionally with an external processing system by sending input data to the processing system and receiving pre-processed input data from the external processing system via the input interface.

[0203] The input data may be image data, in particular image data that has been processed, for example, through segmentation or preprocessing, especially image data that has been processed before being transmitted to the processing system. The transmitted image data may be subsections / subvolumes of larger image data, for example, only image data of the cardiac-aortic junction region of image data of the aortic-cardiac complex region.

[0204] The method may include a computing unit aligning, integrating, and merging 3D imaging data of the input data and 3D representations of the preprocessed input data to fuse them into a unified 3D representation of the patient's characteristics.

[0205] The method may include a computing unit that provides displayable output data of an integrated 3D representation to a 3D graphical user interface on a 3D display device.

[0206] The surgical instruments may be selected from the group consisting of indwelling devices and valves. The details of the selected instruments are the same as those described above.

[0207] Output data for surgical instruments may include instrument characteristics selected from a group of delivery devices, such as stiffness, length, diameter, brand, model and / or type, shape, diameter and / or deployment workflow, growth robots, valve implant type, shape, orientation, brand, model and / or size, and puncture needle type, size, brand, model and / or shape.

[0208] Details of the output data regarding surgical instruments are the same as described above. The output data regarding surgical method characteristics is: - Access site, - Information regarding the placement of temporary pacemakers.

[0209] - Information regarding stent pre-placement, - Information regarding the puncture route, - Information regarding the expansion process during the access phase, - Information regarding navigation routes, - Information regarding the final position of valve implants, - Information regarding the optimized orientation and / or shape of valve implants, - Expansion parameters, - Includes method characteristics selected from a group of imaging commands.

[0210] Details of the output data regarding surgical method characteristics are the same as described above. Preferably, the characteristics of each method are provided in the order of the clinical intervention procedure.

[0211] The output data regarding surgical method characteristics can further include risk parameters, as described above.

[0212] The output data regarding surgical method characteristics (AS, VP, PS) is further divided into risk parameters selected from a group of areas requiring specific attention, This may include at least one of strain and stress, or anatomical or pathological changes.

[0213] Risk parameters can be determined based on biomechanical modeling and at least one of imaging techniques such as US (ultrasound) imaging and / or CT (computed tomography) scans.

[0214] Risk parameters may include local and / or regional morphology of the arteries, including tortuosity, and may be determined as described above.

[0215] Risk parameters may include morphological or anatomical abnormalities and can be determined as described above.

[0216] Risk parameters may include the distribution and / or amount of calcification and may be determined as described above.

[0217] The method may include a further step of determining and outputting an overall risk rate based on the risk parameters described above.

[0218] In the case of identified risks, output data regarding surgical methods may further include alternative output data, particularly alternative routes. Output data regarding routes may include parameters controlled by the robotic guidewire.

[0219] Automatic selection or recommendation of surgical instruments and surgical method characteristics may be performed considering optimized combinations of one or more instruments and / or method characteristics that minimize the risks of the procedure.

[0220] Automatic selection of surgical instruments and surgical method characteristics may be performed considering the presence of stents and / or calcified areas with a high risk of rupture or migration. Preferably, the automatic selection provides to minimize overall and / or specific risks.

[0221] The method may further include receiving feedback for the plan via an input user interface. The details of the feedback are the same as above.

[0222] The method may include a further step of creating a virtual model representing the body part involved in the treatment, the virtual model being created based on patient characteristics, and automatic selection or recommendation being performed based on the virtual model. Details regarding the virtual model are the same as above.

[0223] Automatic selection or recommendation may be based on a database describing changes in anatomical 3D shape. Automatic selection may also be based on multiple templates describing anatomical or pathological changes other than shape. Details regarding automatic selection are the same as above.

[0224] The method may further include classifying patients by comparison with multiple average templates. Details regarding patient classification are the same as above.

[0225] The method may further include determining local deformities on the patient's body resulting from the applied force. Details regarding the determination of local deformities are the same as described above.

[0226] The method may further include relating patient characteristics, particularly patient-specific anatomical structures or pathologies, to the geometric and / or mechanical properties of surgical instruments. Details regarding the relating are the same as described above.

[0227] The output data may be provided sequentially for each stage of the clinical intervention, according to a sequence corresponding to the sequence of the clinical intervention. Details regarding sequential provision are the same as above.

[0228] The method may include a further step of automatically providing checklists, particularly after the completion of each stage of the clinical intervention. Details regarding the provision of checklists are the same as above.

[0229] The invention also provides a computer program that includes program code for performing the steps of the above method when the program is executed on a computer.

[0230] The system, method, computer program, and computer program product can be applied in a similar manner to the automated planning of clinical interventions for the placement of obesity implants, such as gastrointestinal implants like gastric bypass or gastric band, in patients.

[0231] The invention also provides a computer program product that includes a software code portion which can be directly loaded into the internal memory of a digital computer and which performs the above method steps when the program is running on the computer.

[0232] The invention is described below with reference to exemplary embodiments and accompanying drawings. [Brief explanation of the drawing]

[0233] [Figure 1] This is a schematic diagram of the system according to the present invention. [Figure 2] This is a schematic diagram of the selection criteria for transcatheter aortic valve implantation. [Figure 3] The diagram shows an annular plan of the aortic root with (i) a congenital valve and (ii) a implanted valve. [Figure 4] This shows a graphical user interface that displays an integrated 3D representation on a display device. [Figure 5] A 2D view of the 3D representation of the pre-processed input data of the aortic root and left ventricle morphology is shown, along with the recommended surgical instruments for the valve morphology. [Figure 6](i) A 2D view of the integrated 3D representation of the aortic root, including the aorta and valves. (ii) A 2D fluoroscopic image aligned to the integrated 3D representation. [Modes for carrying out the invention]

[0234] Figure 1 is a schematic diagram of a system 100 according to the present invention, which includes an input interface 101, a computing unit 102, and an output interface 103.

[0235] Figure 2 is a schematic diagram of the selection criteria for transcatheter aortic valve implantation. To plan a transcatheter aortic valve implantation, it is necessary to select surgical instruments, at least specific valve implants (VT) and delivery devices (DT), as well as several surgical method characteristics (AS, VP, PS).

[0236] The selection can be described in relation to the relevant anatomical regions 1, 2, 3, 4, 5, 6, such as the iliofemoral vascular system 1, the aorta 2, the ascending aorta 3, the aortic root 4, the left ventricular outflow tract (LVOT) 5, and the membranous septum 6.

[0237] Each anatomical region 1, 2, 3, 4, 5, and 6 can be characterized by quantitative characteristics 11, 21, 31, 41, 51, and 61 and qualitative characteristics 12, 22, 32, and 42.

[0238] Characteristics are considered when selecting instruments such as valve type VT and delivery device DT, as well as surgical method characteristics such as access site AS, valve position VP, and other procedure PS.

[0239] The iliofemoral vascular system 1 can be quantitatively described by its diameter 11. Diameter 11 affects the size of the delivery device DT, e.g., a 14F or 16F catheter. Diameter 11 also affects the valve type VT, e.g., the type and size of the valve implant.

[0240] Qualitative characteristics describing the iliofemoral vascular system 1 may include tortuosity, the presence of calcification, aneurysms, and thrombotic attachments. The location of tortuosity and calcification may influence the selection of access sites AS.

[0241] The aorta 2 can be quantitatively described by its diameter 21. Diameter 21 can influence the selection of the access site AS. If the diameter is too small relative to the diameter of the delivery device, a secondary access site may be preferred.

[0242] Qualitative characteristics describing the aorta 2 may include the presence of calcification and plaque. These may influence the selection of the access site AS.

[0243] Excessive calcification of the femoral artery increases the risk of vascular dissection. Increased calcification can also increase the risk of complications during closure. Therefore, clinicians may select less calcified areas from the left and right femoral arteries. If both femoral arteries are severely calcified, carotid artery access may be preferable.

[0244] The ascending aorta 3 can be quantitatively described by its diameter 21. Its diameter 31 may influence valve selection.

[0245] Qualitative measures describing the ascending aorta 3 may include curvature and the presence of calcification. The valve type VT, particularly the shape of the prosthetic valve, can be selected based on curvature. Depending on the presence of calcification, a self-expanding valve implant or a balloon-expandable valve implant should be selected.

[0246] The quantitative characteristics 41 representing the aortic root 4 may include the area around the aortic valve annulus, which can influence the selection of valve type VT, particularly the diameter of the valve implant.

[0247] The quantitative characteristics 41 representing the aortic root 4 may further include the annular angle, which can influence the selection of valve type VT. Depending on the annular angle, a self-expanding valve implant or a balloon-expandable valve implant can be selected. The annular angle may also influence the selection of the valve implant position VP.

[0248] Quantitative characteristics 41 representing the aortic root 4 may relate to the height relative to the coronary arteries, such as the aortic annulus and SoV (Valsalva sinus, abnormal dilation of the aortic root located between the aortic annulus and sinus duct junction) and the diameter of the SoV. These quantitative characteristics 41 may influence the selection of valve type VT, particularly the selection of the shape of the prosthetic valve.

[0249] The qualitative characteristics 42 representing the aortic root 4 may further include the presence of calcification, which can determine the selection of parameters for the treatment process PS, such as balloon dilation and carotid artery protection.

[0250] The left ventricular outflow tract 5 may be quantitatively characterized by the LVOT periphery 51, which can influence the selection of parameters of the procedure process PS, such as pressure for delivery. The left ventricular outflow tract 5 may have a trapezoidal or parallelogram shape, which affects the implantation depth.

[0251] The membranous septum 6 can be quantitatively characterized by its height 61, which influences the selection of the valve position VP.

[0252] Figure 3 shows an annular view of the aortic root heart in (i) the case with a congenital valve and (ii) the case with a implanted valve.

[0253] The commissure of the implanted valve may have an orientation that is shifted by an angle α from the orientation of the original valve commissure.

[0254] If the misalignment angle α is between 0° and 15°, "commissural alignment" is expected. The pathways to the right coronary artery (RCA) and left coronary artery (LCA) remain undisturbed.

[0255] Mild commistrial misalignment is expected in the 15° to 30° angle range, moderate commistrial misalignment in the 30° to 45° angle range, and severe commistrial misalignment in the 45° to 60° angle range.

[0256] Commissural misalignment can restrict coronary artery access, potentially disrupting coronary blood flow.

[0257] Stress and strain can develop on the valve leaflets, and in the worst case, central leakage may occur.

[0258] The effectiveness of the external seal skirt may be affected by misalignment, and it may be necessary to reposition the valve.

[0259] The grade of commissure alignment or misalignment can be detected by fluorescence fluoroscopy.

[0260] In diagrams where adjacent cusps overlap (see dashed arrows), one of the TAVI-valve commissure posts should be positioned to the right of the fluoroscopic image.

[0261] Alternatively, a field of view that overlaps the left and right coronary artery orifices may be used. Fluoroscopy can be used to determine the degree of coronary artery eccentricity relative to the congenital valve commissure.

[0262] To automatically position the implant valve and calculate the implant location that facilitates coronary artery access after transcatheter heart valve implantation, the valve compression position and delivery device orientation should be determined before implantation.

[0263] A 3D geometric model, particularly a rigid model, of the anatomical structure of the aorta, preferably patient-specific, from the femoral access to the annular ring, can be used as input data.

[0264] Optionally, a 3D biomechanical model of the patient's arteries can be considered as further input data, which may provide, for example, individual eccentricity of the coronary arteries relative to the congenital valves, and may be provided by fluorescence fluoroscopy imaging methods.

[0265] A simplified model of the delivery device can be used as input data. Models simulating the interaction between the delivery device and static 3D anatomical structures, or / or models simulating the interaction between the delivery device and dynamic 3D anatomical structures, may be provided by the computing unit (see Figure 1).

[0266] Depending on the intended final position within the computing unit, it can provide output regarding how the valve should be crimped onto the delivery device and at what angle the delivery device should be inserted into the artery and the natural valve.

[0267] This allows for optimal implant placement relative to the coronary artery and / or commissure locations in the original valve, resulting in optimal access to the coronary arteries after transcatheter heart valve implantation.

[0268] Figure 4 shows a graphical user interface for displaying an integrated 3D representation on a 3D display device. The 3D display device may be an automated stereoscopic or 3D glasses-based display device or a virtual reality headset. The graphical user interface displays actual 3D rendering data, external data based on the 3D representation of pre-processed input data, and internally generated data segmented based on 3D cardiac CT imaging data of the input data. The graphical user interface can further display patient metadata received, for example, from an electronic patient file via the input interface and displayed on the 3D display device.

[0269] Preprocessed input data was processed by an external computing system based on cloud and AI-based analytics for cardiac surgery planning and received by an automated planning system connected to a graphical user interface.

[0270] External data includes representations of the aortic root, valves, and annular ring, with additionally internally generated data representing the aorta. The 3D representations and 3D image data were aligned and integrated by a computing unit using fusion algorithms known to those skilled in the art. This assists clinicians in real-time navigation of medical instruments.

[0271] The graphical user interface is further adapted to provide output data regarding surgical instruments in the form of proposed valves that can be used based on an integrated 3D representation. Valve selection is preferably based on automatically selecting the valve and corresponding surgical method based on determining the template / statistical atlas that most closely resembles the integrated 3D representation. The template / statistical atlas shows the shape or shape changes of the anatomical features of patients who have previously undergone valve replacement. Furthermore, the statistical atlas can provide information on the implanted valve and the expected complications for each patient.

[0272] This enables clinicians to make informed decisions regarding the appropriate valve, the characteristics of the surgical method for valve placement, and the risk values ​​indicating anticipated complications. A virtual representation of the deployed selected valve or its deployment location can be displayed in a graphical user interface (see Figure 5). The graphical user interface can be further adapted to provide a virtual simulation of the planned placement route of the medical device corresponding to the selected valve.

[0273] The graphical user interface enables a clinician to select an appropriate valve from the proposed valves, whereby the integrated 3D representation is automatically adjusted to include the selected valve and related potential complications such as paravalvular leakage. The graphical user interface of FIG. 4 can be manipulated by the clinician to adjust the display position and display orientation accordingly in three dimensions.

[0274] Alternatively or additionally, the graphical user interface can provide valve options based on recommended surgical instruments such as valves, recommended surgical method characteristics, and related risks provided by preprocessed input data.

[0275] The graphical user interface can be adapted to display a graphic statistical analysis, particularly a graphic statistical analysis considering the contact between the valve and the native anatomical structure, to simplify the comparison between different proposed valves, particularly based on a statistical atlas or preprocessed data input.

[0276] FIG. 5 shows a 2D view of a 3D representation of preprocessed input data of the aortic root and left ventricular morphology, together with the recommended surgical instrument for the valve morphology. The 3D representation was generated by an external processing system based on input data transmitted in the form of a CT scan of the patient from the computing unit of the system. The preprocessed input data received by the computing unit can further provide, for example, predictions for contacting the valve with the local native anatomical structure and additional information on recommended valve selection such as risks associated with valve selection.

[0277] FIG. 6(i) shows a 2D view of an integrated 3D representation of the aortic root including the aorta and the valve. As described in FIG. 4, the input data in the form of 3D image data of the aorta was merged with the preprocessed input data in the form of a 3D representation of the aortic root. The generation of the integrated 3D representation was performed preoperatively. The valve selection was determined based on the integrated 3D representation. The 3D representation preferably includes the location of calcification included in the integrated 3D representation. <00009

[0278] FIG. 6(ii) shows a 2D fluoroscopic image that is aligned with the integrated 3D representation of FIG. 6(i) during the procedure. This enables the provision of additional real-time information of a medical instrument for delivering a valve for navigation simulation or intraoperative guidance. A virtual representation of the valve corresponding to the valve selected for implantation can be provided to provide a virtual implantation path of the medical instrument to the target position.

Claims

1. A system for automated planning of clinical interventions for implanting heart valves in patients, wherein the system (100) - An input interface (101) for inputting data, - Computing unit (102), The computing unit (102) is -Receive input data regarding the patient's characteristics (1, 2, 3, 4, 5, 6) via the input interface (101), - The patient automatically selects surgical instruments (VT, DT) and surgical method characteristics (AS, VP, PS) based on the aforementioned characteristics, and the surgical instruments (VT, DT) and surgical method characteristics (AS, VP, PS) are suitable for implanting the heart valve in the patient. - Provides output data relating to at least the selected surgical instruments (VT, DT) and the selected surgical method characteristics (AS, VP, PS). and / or The computing unit (102) is -Receive input data regarding the patient's characteristics (1, 2, 3, 4, 5, 6), and receive input data regarding surgical instruments (VT, DT) and / or surgical method characteristics (AS, VP, PS) via the input interface (101). - Based on the patient's characteristics and at least one of the surgical instruments (VT, DT) and the surgical method, the risk value is automatically determined. - A system adapted to provide output data relating to the aforementioned risk values.

2. The system according to claim 1, wherein the system includes an output interface (103) for receiving output data from the computing unit (102) and providing the output data to a user.

3. The aforementioned input data is - In particular, 3D imaging data of the aorta, - Characteristics related to access and closure, - Characteristics related to pacing, - Characteristics related to imaging, - Characteristics related to navigation, - Characteristics related to valve positioning and placement, - Characteristics related to commissure alignment, - The system according to claim 1 or 2, selected from the group consisting of properties related to surgical instruments (VT, DT) and / or surgical methods.

4. The control unit (102) is adapted to receive pre-processed input data relating to the patient's characteristics (1, 2, 3, 4, 5, 6) via the input interface (101), wherein the pre-processed input data is preferably - 3D representation, especially of the aortic root, left ventricle, sinusoidal junction ring, aortic ring, and / or calcification. - Recommended surgical instruments (VT, DT), recommended surgical method characteristics (AS, VP, PS), and in particular, characteristics indicating recommended risk values ​​based on the recommended surgical instruments (VT, DT) and recommended surgical method characteristics (AS, VP, PS), - Characteristics related to the location and / or orientation of the coronary artery orifice, - Characteristics related to pacing, - Characteristics related to imaging, - Characteristics related to navigation, - Characteristics related to valve selection, - Characteristics related to valve positioning and placement, and - A system selected from the group consisting of characteristics related to commissure alignment, particularly according to any one of the preceding claims.

5. The system according to claim 4, wherein the input interface is adapted for bidirectional communication with a processing system by transmitting image data, particularly processed image data, and receiving the preprocessed input data.

6. The system according to claim 3 and 4 or 5, wherein the computing unit (102) is adapted to use a fusion algorithm to align, integrate, and merge the 3D imaging data of the input data and the 3D representation of the preprocessed input data to form a unified 3D representation of the patient's characteristics.

7. The system according to claim 6, wherein the computing unit (102) is adapted to provide displayable output data of the integrated 3D representation to a 3D graphical user interface on a 3D display device.

8. The aforementioned surgical instruments (VT, DT) are - Implantation devices, especially guidewires, growth robots and / or delivery devices, - A system according to any one of claims 1 to 7, selected from the group consisting of valves.

9. The output data relating to the surgical instrument is, - The rigidity, length, diameter, brand, model and / or type of the delivery device, - Shape, diameter, and / or deployment workflow of a growing robot, including, for example, process, process timing, pressure setting, - Shape, size, orientation, brand, model and / or type of valve implant, - The system according to claim 8, comprising instrument characteristics selected from the group of size, shape, brand, model and / or type of puncture needles.

10. The output data relating to the surgical method characteristics (AS, VP, PS) is - Access site, - Information regarding the placement of temporary pacemakers, - Information regarding stent pre-placement, - Information regarding the puncture route, - Information regarding the expansion process during the access phase, - Information regarding the final position of the valve to be implanted, - Information regarding navigation routes, - Optimized implant orientation and / or shape, - Expansion parameters, - The system according to any one of claims 1 to 9, comprising a method characteristic selected from a group of imaging commands.

11. The system according to any one of claims 1 to 10, wherein the output data relating to the surgical method characteristics (AS, VP, PS) includes, in particular, risk parameters for specific areas of the patient requiring special attention.

12. The system according to claim 11, wherein the computing unit (102) is adapted to determine the risk parameters based on at least one of biomechanical modeling and imaging such as US imaging of a CT scan.

13. The aforementioned risk parameters are the local and / or regional shape of the artery, including tortuosity. The computing unit (102) is (i) Centerline curvature measurement calculated from preoperative CT scan or contrast-enhanced fluoroscopy, (ii) Surface mesh, (iii) Geometric parameters related to regional arterial tortuosity, (iv) Determination of intra-patient variability versus inter-patient variability, (v) The system according to claims 11 and 12, which is adapted to determine the risk parameter based on at least one of identifying a high-risk area / region.

14. The aforementioned risk parameters are morphological or anatomical abnormalities. The computing unit (102) is (i) the standard deviation from the mean population shape or anatomical structure, or the standard deviation from the subgroup population shape or anatomical structure, (ii) Preferably extract the vessel diameter based on preoperative CT or US imaging. (iii) Online estimation by contact with the guidewire, (iv) The system according to claims 11 to 13, which is adapted to determine shape or anatomical abnormalities based on at least one of blood flow analyses, particularly deviations from normal flow detected.

15. The risk parameters are the distribution and / or amount of calcification, and the computing unit (102) (i) Preoperative CT or US imaging segmentation to extract pixel-level positions, and (ii) The system according to any one of claims 11 to 14, preferably adapted to determine the distribution and / or amount of calcification based on at least one of in-line stiffness estimates from the highly sensitive impedance / capacitance of the arterial wall.

16. The system according to any one of claims 11 to 15, wherein the computing unit (102) is further adapted to determine and output an overall risk rate, risk probability, and / or risk map based on the risk parameters.

17. In the event of identified risks, the computing unit (102) is adapted to provide alternative output data, in particular output data relating to the surgical method characteristics (AS, VP, PS), further including alternative routes and / or alternative devices and / or manual or surgical placements, according to any one of claims 11 to 16.

18. The computing unit (102) is - An optimized combination of one or more instrument and / or method characteristics that minimizes the risks of the procedure and / or maximizes the expected results. - The system according to any one of claims 1 to 17, which is adapted to automatically select or recommend the surgical instrument or method characteristics by taking into account at least one of the presence of a stent and / or calcified area that is at high risk of rupture or migration.

19. The system according to any one of claims 1 to 18, wherein the computing unit (102) is adapted to receive user feedback for planning via the input interface.

20. The computing unit (102) is adapted to create a virtual model representing the body parts involved in the procedure, the virtual model is created based on the patient characteristics, and the computing unit is adapted to automatically select or recommend the surgical instruments and surgical method characteristics based on the virtual model. The creation of the virtual model preferably involves a method of morphing an anatomical template into a set of points representing the body parts involved in the procedure, particularly including at least one of landmark fitting and parametric surface fitting, according to any one of claims 1 to 19.

21. The computing unit (102) processes the surgical instruments (VT, DT) and the characteristics of the surgical method (AS, VP, PS), - In particular, a database describing changes in anatomical 3D shape, including at least one representation method selected from parametric mesh surfaces, landmarks, and volumes. - The system according to any one of claims 1 to 19, which is adapted to automatically select or recommend based on a plurality of templates describing anatomical or pathological changes excluding shape, and at least one, preferably multiple, statistical atlases that optionally describe anatomical features in populations or subgroups of populations for each template, particularly shape changes of at least a portion, preferably the entire, of the aorta.

22. The system according to any one of claims 1 to 21, wherein the computing unit (102) is further adapted to classify the patient by comparison with a plurality of average templates.

23. The computing unit (102) determines local deformations on the patient's body resulting from forces applied to a region of the patient's body by an external device, in particular - Statistical Atlas of Deformations - A biomechanical model that takes into account tissue characteristics and anatomical constraints. - Preferably, the system according to any one of claims 1 to 22, further adapted to determine based on at least one of local non-rigid registrations between a template and online acquired data, based on at least one of (i) fitting a parametric template to 3D landmarks using corresponding points, and (ii) determining a parametric model that describes the correspondence between 2D deformation and 3D deformation, particularly using at least one of a 3D shape model, tissue properties, and anatomical constraints.

24. The computing unit (102) preferably uses the patient's characteristics, particularly the patient's unique anatomical structure or pathology, - Statistical shape analysis combined with partial least squares regression (PLSR) on patient profiles related to specific designs of delivery devices, catheters, or valves. - The system according to any one of claims 1 to 23, further adapted to relate the geometric and / or mechanical properties of the surgical instrument based on at least one of the geometric metrics.

25. The system according to any one of claims 1 to 24, wherein the computing unit (102) is further adapted to sequentially provide output data for each stage of the clinical intervention in accordance with a sequence corresponding to the sequence of the clinical intervention.

26. The system according to any one of claims 1 to 25, wherein the computing unit (102) is further adapted to automatically provide a checklist, particularly after the completion of each stage of the clinical intervention.

27. Preferably, a method for computer-based automated planning of a clinical intervention for implanting a heart valve in a patient, using the system described in any one of claims 1 to 26, - Receiving input data regarding the patient's characteristics via the input interface (101), - To automatically select or recommend suitable surgical instruments (VT, DT) and surgical method characteristics (AS, VP, PS) for implanting the heart valve of the patient, wherein the selection is made by a computing unit based on the patient's characteristics. - To provide output data relating at least to the selected or recommended surgical instruments (VT, DT) and surgical method characteristics (AS, VP, PS), and / or - Receiving input data regarding the patient's characteristics (1, 2, 3, 4, 5, 6), and receiving input data regarding surgical instruments (VT, DT) and / or surgical method characteristics (AS, VP, PS) via the input interface (101), - Automatically determine the risk value based on the patient's characteristics and at least one of the surgical instruments (VT, DT) and the surgical method. A method comprising providing output data relating to the aforementioned risk value.

28. The aforementioned input data is - 2D and / or 3D imaging data, - Characteristics related to access and closure, - Characteristics related to pacing, - Characteristics related to imaging, - Characteristics related to navigation, - Characteristics related to valve positioning and placement, - Characteristics related to commissure alignment, - The method according to claim 27, selected from the group consisting of properties related to surgical instruments (VT, DT) and / or surgical methods.

29. The control unit (102) receives pre-processed input data relating to the patient's characteristics (1, 2, 3, 4, 5, 6) via the input interface (101), and the pre-processed input data is - 3D representation, especially of the aortic root, left ventricle, sinusoidal junction ring, aortic ring, and / or calcification. - Recommended surgical instruments (VT, DT), recommended surgical method characteristics (AS, VP, PS), and in particular, characteristics indicating recommended risk values ​​based on the recommended surgical instruments (VT, DT) and recommended surgical method characteristics (AS, VP, PS), - Characteristics related to the location and / or orientation of the coronary artery orifice, - Characteristics related to pacing, - Characteristics related to imaging, - Characteristics related to navigation, - Characteristics related to valve selection, - Characteristics related to valve positioning and placement, and - The method according to any one of claims 27 or 28, selected from the group consisting of characteristics related to commissure alignment.

30. The method according to claim 29, wherein the input interface communicates bidirectionally with the processing system by transmitting the input data and receiving the pre-processed input data.

31. The computing unit (102) aligns, integrates, and merges the 3D imaging data of the input data and the 3D representation of the preprocessed input data to fuse them into an integrated 3D representation of the patient's characteristics, as described in claims 28 and 29 or the method according to claims 28, 29, and 30.

32. The method according to claim 31, wherein the computing unit (102) provides output data for displaying the integrated 3D representation on a 3D graphical user interface on a 3D display device.

33. The aforementioned surgical instruments (VT, DT) are - Implantation devices, especially guidewires, growth robots and / or delivery devices, - The method according to any one of claims 27 to 32, selected from the group consisting of valves.

34. The output data relating to the surgical instrument is, - Rigidity, length, and diameter of the delivery device, - Shape, diameter, and / or deployment workflow of a growing robot, including, for example, process, process timing, pressure setting, - Shape, size, orientation, brand, model and / or type of valve implant, - The method according to claim 28, comprising instrument characteristics selected from the group of size, shape, brand, model and / or type of puncture needle.

35. The output data relating to the surgical method characteristics (AS, VP, PS) is - Access site, - Information regarding the placement of temporary pacemakers, - Information regarding stent pre-placement, - Information regarding the puncture route, - Information regarding the expansion process during the access phase, - Information regarding the final position of the valve to be implanted, - Information regarding navigation routes, - Optimized implant orientation and / or shape, - Expansion parameters, - The method according to any one of claims 27 to 34, comprising method characteristics selected from a group of imaging commands.

36. The output data relating to the surgical method characteristics (AS, VP, PS) is particularly, - Areas requiring special attention, - The method according to any one of claims 27 to 35, further comprising a risk parameter selected from at least one of strain and stress and anatomical and pathological variations.

37. The method according to claim 36, wherein the risk parameter is determined based on at least one of biomechanical modeling and US imaging.

38. The aforementioned risk parameters are the local and / or regional shape of the artery, including tortuosity. (i) Use of centerline curvature measurements calculated from preoperative CT scans or contrast-enhanced fluoroscopy, (ii) Using a surface mesh, (iii) Using shape parameters related to regional arterial tortuosity, (iv) Determining intra-patient variability versus inter-patient variability, (v) Identifying areas / regions at high risk, and determined based on at least one of the following: The method according to claims 36 and 37.

39. The aforementioned risk parameters are morphological or anatomical abnormalities. (i) Using the standard deviation from the mean population shape or anatomical structure or subgroup, (ii) Preferably extracting the vessel diameter based on preoperative CT or US imaging, (iii) Online estimation by contact with the guidewire, (iv) In particular, blood flow analysis by detecting deviations from normal flow, and determined based on at least one of the following: The method according to any one of claims 36 to 38.

40. The aforementioned risk parameters are the distribution and / or amount of calcification, (i) Preoperative CT or US imaging segmentation to extract pixel-level positions, and (ii) Preferably determined based on at least one of in-line stiffness estimations from the highly sensitive impedance / capacitance of the arterial wall, The method according to any one of claims 36 to 39.

41. The method according to any one of claims 36 to 40, further comprising the step of determining and outputting an overall risk rate based on the aforementioned risk parameters.

42. In the case of identified risks, the output data relating to the surgical method characteristics (AS, VP, PS) further includes alternative output data, in particular alternative routes, according to any one of claims 36 to 41.

43. The aforementioned automatic selection is - An optimized combination of one or more instrument and / or method characteristics that minimizes the risks of the aforementioned procedure, - The method according to any one of claims 27 to 42, which is carried out by taking into consideration at least one of the presence of a stent and / or calcified area that is at high risk of rupture or migration.

44. The method according to any one of claims 27 to 43, further comprising receiving user feedback for planning via the input interface.

45. The method according to any one of claims 27 to 44, further comprising the step of creating a virtual model representing the body part involved in the treatment, wherein the virtual model is created based on the patient characteristics, and the automatic selection or recommendation is performed based on the model.

46. The aforementioned automatic selection or recommendation is - In particular, a database describing changes in anatomical or pathological 3D shape, including at least one representation method selected from parametric mesh surfaces, landmarks, and volumes. The method according to any one of claims 27 to 45, wherein the method is performed based on a plurality of templates describing the anatomical changes excluding shape, and at least one, preferably a plurality of statistical atlases, which optionally describe the anatomical features in the population or population subgroup for each template, particularly the shape changes of at least a portion, preferably the entire, of the aorta.

47. The method according to any one of claims 27 to 46, further comprising classifying patients by comparison with a plurality of average templates.

48. Local deformation of the patient's body resulting from a force applied to a region of the patient's body by an external device, in particular, - Statistical Atlas of Deformations - A biomechanical model that takes into account tissue characteristics and anatomical constraints. The method according to any one of claims 27 to 47, further comprising: (i) fitting a parametric template to 3D landmarks using corresponding points; and (ii) determining a parametric model that describes the correspondence between 2D deformation and 3D deformation, in particular using at least one of a 3D shape model, tissue properties, and anatomical constraints, based on at least one of local non-rigid registrations between a template and online acquired data.

49. The aforementioned characteristics of the patient, in particular the patient's unique anatomical structure or pathology, preferably - Statistical shape analysis combined with partial least squares regression (PLSR) on patient profiles related to specific designs of delivery devices, catheters, or valves. - The method according to any one of claims 27 to 48, further comprising relating the geometric and / or mechanical properties of the surgical instrument based on at least one of the geometric metrics.

50. The method according to any one of claims 27 to 49, wherein output data for each stage of the clinical intervention is provided sequentially according to a sequence corresponding to the sequence of the clinical intervention.

51. The method according to any one of claims 27 to 50, further comprising the step of automatically providing a checklist after the completion of each stage of the clinical intervention.

52. A computer program that includes program code for performing the steps of the method according to any one of claims 27 to 51 when the program is executed on a computer.

53. A computer program product comprising a software code portion that can be directly loaded into the internal memory of a digital computer and performs a step according to at least one of claims 27 to 51 when the program is running on the digital computer.

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