DETAILED ACTION
Election to Restriction Requirement
In response to the Restriction Requirement through phone conversation between Applicant’s representative Sheree Rowe (Reg. Num. 59068) on June 5, 2025, Applicant elects with traverse Group I (claims 1-20) for examination.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over MIHALEF (WO 2020064090 A1), in view of ITU (US 20210219935 A1).
Re Claim 1, MIHALEF discloses a neural network apparatus for providing outcome predictions for intrasaccular implant devices based on digital imaging and clinical information (see MIHALEF: e.g., --[0046] The decision support uses the anatomical and/or physiological knowledge using any of the different machine learning models. For training, many samples with known ground truth (e.g., selections) are used. For the samples, the machine learning model inputs may be real (from patients, or bench-scale measurements), virtual (synthetic or not representative of a given patient or physical bench arrangement), or a combination of the two. For patient data, the input samples may contain the patient images, blood biomarkers, demographics, measurements and/or genetic data. For the ground truth, the input data includes the therapy that was chosen for the patient. Other ground truths may alternatively or additionally be used, such as the recorded outcome at different temporal points (e.g., 30 days, 90 days, etc.) and/or the occurrence or lack thereof of adverse events. …[0048] The machine-learnt predictor, with or without deep learning, is trained to associate the categorical labels (output clinical decision of the selections) to the extracted values of one or more features. The machine- learning uses training data with ground truth to learn to select based on the input vector. The resulting machine-learnt network is a matrix for inputs, weighting, convolution kernels, and/or combinations to output a clinical decision. Using the learned network, the processor inputs the extracted values for features and outputs the selection. ….[0047] Any machine learning or training may be used. A probabilistic boosting tree, support vector machine, neural network, sparse auto-encoding classifier, Bayesian network, or other now known or later developed machine learning may be used. Any semi-supervised, supervised, or unsupervised learning may be used. Hierarchal or other approaches may be used. In one embodiment, the classification is by a machine-learnt classifier learnt with deep learning. As part of identifying features that distinguish between different outcomes, the classifier is also machine learnt. Any deep learning approach or architecture may be used. For example, a convolutional neural network is used….
[0049] The machine-learned network is trained to output a selection, outcome, risk, deployment, or other clinical support information. The information extracted from the model may include the predicted outcomes and risks under different therapy choices. The information may select the therapy configuration, such as (i) start and end locations of the flow diverter, (ii) the number of flow diverters deployed, (iii) the configuration (overlapping or deployment into different branches), (iv) the reference radius of the flow diverter to use, (v) mean and maximum porosity in the neck/non-vessel area, etc. The information output by the machine-learned network may include identification of similar patients and the therapy choices made for the similar patients identified from a patient database and their outcomes.--, in [0044]-[0049]), the apparatus comprising:
memory; and at least one processor coupled to the memory, and based at least in part on information stored in the memory, configured to: receive, as input from a user device, digital imaging information and the clinical information for an aneurysm patient (see MIHALEF: e.g., --the user may indicate the starting and ending points on an image of the vessel or vessel model (e.g., 3D mesh rendered to 2D display image). Any locations of packing to increase porosity may be indicated. Other user inputs of the device and/or placement may be used, such as configuration for multiple devices…[0044]… the decision support processor selects. A selection function may be used, such as relating one or more different patient- specific characteristics to characteristics of or specific ones of the available devices. In one embodiment, a machine-learned network selects the vascular implant and/or placement of the vascular implant….the machine learning model inputs may be real (from patients, or bench-scale measurements), virtual (synthetic or not representative of a given patient or physical bench arrangement), or a combination of the two. For patient data, the input samples may contain the patient images, blood biomarkers, demographics, measurements and/or genetic data. For the ground truth, the input data includes the therapy that was chosen for the patient. Other ground truths may alternatively or additionally be used, such as the recorded outcome at different temporal points (e.g., 30 days, 90 days, etc.) and/or the occurrence or lack thereof of adverse events.--, in [0043]-[0046]; and, --the implant model is bent to follow the curvature of the vessel centerline. In a last stage, the release of the crimping is simulated so the implant model expands and fills the lumen. Other non- clinical-based simulation may be used, such as outputting a deployment and corresponding deformation from a machine-learned network based on inputs of the scan data, vessel model, patient-specific information, and/or device model. …[0062] In act 23, the decision support processor calculates a porosity of the endovascular device from the deployment. For the flow diverter or other implant, the porosity, in part, controls the flow. In-situ implantation may produce variations in the metal coverage ratio (MCR) and porosity of the device, especially in the aneurysm and adjacent regions. Hemodynamics is consequently affected by such regions, so porosity is a consideration when performing the implantation. By simulating deployment, the porosity that results in general or by location may be determined for estimating outcome.--, in [0060]-[0063]; also see: -- for patients with aneurysms, the choices could be coiling the aneurysm or placement of any one of different models of flow diverters with multiple possible implantation configurations. The clinician decides if implantation of endovascular devices like flow diverters or stents would be beneficial to the patient, and if so, make an optimal choice of the device type and its parameters (e.g. diameter, length, porosity, metal coverage area, material mechanical properties, etc.). In the case of patients with aneurysms--, in [0001], and, -- The decision support for the treatment planning process is based on a systemic analysis of the above-mentioned factors along with simulated treatment of the patient’s aneurysm using a virtual implantation of a flow-diverting stent. Only the data acquired prior to the treatment planning (medical images, blood biomarkers, patient medical history and demographics) is used for further processing. The model may also utilize simulated outcomes using machine learning or physics-based simulators, such as computational fluid dynamics models. For such simulations, an anatomical model may be extracted using either manual, semi-automatic or automatic segmentation algorithms. The virtual deployment system is integrated into the decision support. [0022] Figure 1 shows a flow chart or outline of one embodiment of the decision support for endovascular procedure planning. The outline illustrates a workflow, including device modeling (fitting the device characteristics to a physics model), deployment of the device model in a representation of the vessel of the patient, and outcome prediction based on the deployment.--, in [0021]-[0022]);
generate, using a neural network trained for aneurysm outcome prediction, the digital imaging information, and the clinical information, an outcome prediction for at least one intrasaccular implant device for implant in an aneurysm sac identified in the digital imaging information from a set of potential treatment devices (see MIHALEF: e.g., --[0021] Aneurysm treatment planning in clinical practice depends on acquiring suitable medical images describing the pathologic vascular region. Based on the imaging data in combination with patient demographics (age, gender, weight, body mass index, etc.), past medical history (diabetes, current list of medications, previous stroke, etc.) and/or blood biomarkers (e.g., blood counts, clotting factors, hematocrit, blood viscosity, glucose level, etc.), a patient-individualized treatment strategy is set up and performed. The decision support for the treatment planning process is based on a systemic analysis of the above-mentioned factors along with simulated treatment of the patient’s aneurysm using a virtual implantation of a flow-diverting stent. Only the data acquired prior to the treatment planning (medical images, blood biomarkers, patient medical history and demographics) is used for further processing. The model may also utilize simulated outcomes using machine learning or physics-based simulators, such as computational fluid dynamics models. For such simulations, an anatomical model may be extracted using either manual, semi-automatic or automatic segmentation algorithms. The virtual deployment system is integrated into the decision support. [0022] Figure 1 shows a flow chart or outline of one embodiment of the decision support for endovascular procedure planning. The outline illustrates a workflow, including device modeling (fitting the device characteristics to a physics model), deployment of the device model in a representation of the vessel of the patient, and outcome prediction based on the deployment.--, in [0021]-[0022], and, --[0026] In stage 16, a machine-learned network predicts outcome based on the simulated deployment, imaging data (e.g., segmented vessel), and/or other patient-specific information. The machine-learned network may be used to output implant information (e.g., type, size, placement, number of devices, configuration of device, etc.) used for the simulation in stage 14. Alternatively or additionally, the machine-learned network may be used to predict outcome or prognosis for a simulated deployment. The decision support system may be trained on outcome data, if available, to predict not only short-term physiological response, but also long-term outcomes and/or costs. The outcome or prognosis may be used to compare or select implant information, or the network directly outputs the selection.
[0027] In stage 18, the simulated deployment, predicted outcome, and/or a calculated hemodynamic characteristic based on the deployment are provided to the clinician. The information may be used to support decisions for planning an endovascular implantation.--, in [0026]-[0027]);
MIHALEF however does not explicitly disclose {outcome prediction for} intrasaccular implant device having a highest predicted likelihood of complete occlusion of the aneurysm sac from a set of potential treatment devices;
ITU discloses outcome prediction for an intrasaccular implant device having a highest predicted likelihood of complete occlusion of the aneurysm sac from a set of potential treatment devices (see ITU: e.g., -- FIG. 8 shows an example of the normal distribution of the root radius of a coronary left arterial tree. Known, estimated, or standard normal distributions may be used. The synthetic examples generated are assigned the value for the root radius of the coronary left arterial tree based on the distribution (e.g., probability of a given value per example assigned using the distribution).--, in [0084], and,
-- [0102] Other features extracted include parameters for one or more abnormalities of the vessel structure. Abnormal morphology may be characterized by characteristics of calcification, characteristics of the plaque (e.g., fibrous tissue, lipid tissue, necrotic tissue, calcified tissue), characteristics of thrombus, characteristics of diffuse disease, presence of total or sub-total occlusion, presence of myocardial bridging (superficial and/or deep), congenital anomalies of coronary arteries (e.g., anomalous origin of a coronary artery from an abnormal sinus of Valsalva with an inter-arterial course between the great arteries, anomalous origin of one coronary artery from the pulmonary trunk, or others), aneurysmal dilatation and superimposed atherosclerosis, “high take off” coronary artery (e.g., the ostium is several millimeters above the sino-tubular junction (the artery may have a sharp downward angle and runs partially through the aortic wall)), myocardial bridging: superficial and deep, coronary fistula, coronary artery dissection, coronary vasculitis (e.g., rheumatoid arthritis, systemic lupus erythematosus (SLE), or Behçet's disease, Kawasaki disease, polyarteritis nodosa, and/or persisting (post) inflammatory aneurysms), fibromuscular dysplasia, coronary micro embolization, and/or left or right dominance. Additional, different, or fewer abnormality features may be used.--, in [0102], and, -- A local ischemia weight value is estimated independently for each root/branch/leaf segment using geometric features of the segment, such as the reference radius, length, tapering rate and other features…. An average value of healthy radiuses of the entire branch or a part of the branch, an average value of healthy radiuses obtained when excluding the largest x % and the smallest y % of the radius values of the entire branch or a part of the branch, or maximum or minimum value of healthy radii of the entire branch or part of the branch are computed.--, in [0110]-[0112]; and,
-- [0146] The in vitro model 23 and the flow conditions may be modified in numerous ways to generate a large number of setups. For example, the number, position and shape of the occluders is altered. As another example, the resistance at one or more locations is altered. In yet another example, the operation of the pump is altered. The number of side branches and any occlusions may be altered. Other alterations of combinations of different alterations are used to create different models with corresponding features and resulting flow characteristics.--, in [0145]-[0146], and, -- [0152] More than one classifier may be created. Since different types of branches and regions are present in a vessel tree, different classifiers may be machine trained for the different branches and/or regions. For example, different classifiers are trained for main and side branches, bifurcation regions and single branch regions, different types of pathologic regions such as different types of single branch stenotic regions (e.g., focal, long, diffuse, restenosis, or other), different types of bifurcation stenoses (e.g. a separate model for each bifurcation stenosis type in the medina classification), different types of aneurysms, different types of plaque, different types of total and/or sub-total occlusions, stenotic and regurgitant valves, various pathologies of the heart (e.g., past infarct or myopathies), or different types of branches (e.g. in case of coronary arterial trees: LM, LAD, LCx, RCA, Diagonal, OM, or other). Since the training is based on synthetic geometries, a large enough number of training instances may be generated for each of these different classifiers. Another possibility is to divide the geometry into separate segments (e.g. for coronary geometries: proximal LAD, mid LAD, and distal LAD) and to extract the features discussed in the previous sections separately for each segment. Afterwards these features may either be combined into cumulative features or used separately for a single or multiple machine learning algorithms for predicting a hemodynamic metric of interest.--, in [0152], and, -- In act 60, uncertainty is assigned to one or more features. The uncertainty is a distribution of possible values for the feature. For example, the radius may be measured as 0.25 cm, but the accuracy or tolerance in the measurement provides that the radius is between 0.20 cm and 0.30 cm with greater probability for the values closer to 0.25 cm. Any distribution of possible or probable values may be used, such as a normal distribution, a distribution from a study, or from another source…. Using the machine-learnt algorithm, the confidence of the estimated hemodynamic metric is provided. A confidence or probability is provided for one value of the metric. Alternatively, the predictions from the learnt model may also be ranges or confidence intervals within which the predicted quantity is expected. The predicted confidence interval for the patient could be either directly predicted from the model or estimated from a set of similar anatomies from a saved database of synthetic models. --, in [0178]-[0180]);
MIHALEF and ITU are combinable as they are in the same field of endeavor: machine learning and neural network in vascular implant treatment outcome prediction. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify MIHALEF’s apparatus using ITU’s teachings by including outcome prediction for an intrasaccular implant device having a highest predicted likelihood of complete occlusion of the aneurysm sac from a set of potential treatment devices to MIHALEF’s outcome prediction in order to provide confidence or probability for the features associated with the outcome prediction for an intrasaccular implant (see ITU: e.g. in [0084], [0102], [0110]-[0112], [0145]-[0146], [0152], and [0178]-[0180]);
MIHALEF as modified by ITU further disclose output, for display on a device, an identification of the at least one intrasaccular implant device and the outcome prediction for each of the at least one intrasaccular implant device (see MIHALEF: e.g., ….[0047] Any machine learning or training may be used. A probabilistic boosting tree, support vector machine, neural network, sparse auto-encoding classifier, Bayesian network, or other now known or later developed machine learning may be used. Any semi-supervised, supervised, or unsupervised learning may be used. Hierarchal or other approaches may be used. In one embodiment, the classification is by a machine-learnt classifier learnt with deep learning. As part of identifying features that distinguish between different outcomes, the classifier is also machine learnt. Any deep learning approach or architecture may be used. For example, a convolutional neural network is used….
[0049] The machine-learned network is trained to output a selection, outcome, risk, deployment, or other clinical support information. The information extracted from the model may include the predicted outcomes and risks under different therapy choices. The information may select the therapy configuration, such as (i) start and end locations of the flow diverter, (ii) the number of flow diverters deployed, (iii) the configuration (overlapping or deployment into different branches), (iv) the reference radius of the flow diverter to use, (v) mean and maximum porosity in the neck/non-vessel area, etc. The information output by the machine-learned network may include identification of similar patients and the therapy choices made for the similar patients identified from a patient database and their outcomes.--, in [0044]-[0049], and, --[0077] In act 26, the decision support processor generates an image, and a display screen displays the image. The output (e.g., outcome, hemodynamic quantity, porosity, visual depiction of the deployment or end result of deployment, or combinations thereof) is transmitted to a display…..[0079] In one embodiment, the clinical decisions are visualized, either as text or in a graphical way (e.g. overlaid on the medical images) and presented to the clinician. Decision support information, such as treatments, risks, guidelines, or other information, may be output. Diagnostic rules for treatment, such as based on guidelines or studies, may be output as decision support.
[0080] Any display of the decision or decisions may be used. In one embodiment, a decision tree shows the clinical decision, other possible decisions, and further treatment options resulting from the clinical decision. Besides a basic text-based display, another option is to display in a hierarchy not only the currently selected clinical decision but also possible subsequent clinical decisions.
[0081] The displayed image from the deployment simulation may be used to visually guide the placement of the device or devices. For instance, the imaging during the procedure may utilize the output to overlay planes for the start and end locations of the flow diverter to aid in device placement.—[0077]-[0081]).
Re Claim 2, MIHALEF as modified by ITU further disclose wherein the neural network is configured with a classification algorithm based on at least one of a random forest algorithm, a multilayer perceptron (MLP) neural network algorithm, a logistic regression algorithm, a naive Bayes machine learning algorithm, or a support vector machine (SVM) algorithm (see MIHALEF: e.g., --[0021] Aneurysm treatment planning in clinical practice depends on acquiring suitable medical images describing the pathologic vascular region. Based on the imaging data in combination with patient demographics (age, gender, weight, body mass index, etc.), past medical history (diabetes, current list of medications, previous stroke, etc.) and/or blood biomarkers (e.g., blood counts, clotting factors, hematocrit, blood viscosity, glucose level, etc.), a patient-individualized treatment strategy is set up and performed. The decision support for the treatment planning process is based on a systemic analysis of the above-mentioned factors along with simulated treatment of the patient’s aneurysm using a virtual implantation of a flow-diverting stent. Only the data acquired prior to the treatment planning (medical images, blood biomarkers, patient medical history and demographics) is used for further processing. The model may also utilize simulated outcomes using machine learning or physics-based simulators, such as computational fluid dynamics models. For such simulations, an anatomical model may be extracted using either manual, semi-automatic or automatic segmentation algorithms. The virtual deployment system is integrated into the decision support.--, in [0021], and, -- [0047] Any machine learning or training may be used. A probabilistic boosting tree, support vector machine, neural network, sparse auto-encoding classifier, Bayesian network, or other now known or later developed machine learning may be used. Any semi-supervised, supervised, or unsupervised learning may be used. Hierarchal or other approaches may be used. In one embodiment, the classification is by a machine-learnt classifier learnt with deep learning. As part of identifying features that distinguish between different outcomes, the classifier is also machine learnt. Any deep learning approach or architecture may be used. For example, a convolutional neural network is used. The network may include convolutional, sub-sampling (e.g., max pooling), fully connected layers, and/or other types of layers. By using convolution, the number of possible features to be tested is limited. The fully connected layers operate to fully connect the features as limited by the convolution layer after maximum pooling. Other features may be added to the fully connected layers, such as non-imaging or clinical information. Any combination of layers may be provided. Hierarchical structures are employed, either for learning features or representation or for classification or regression. The computer-based decision support system employs a machine learning algorithm for automated decision making.--, in [0046]-[0047]).
Re Claim 3, MIHALEF as modified by ITU further disclose wherein, based at least in part on the information stored in the memory, the at least one processor is further configured to perform at least one of: semi-automatic segmentation of the digital imaging information to obtain one or more measurements of the aneurysm sac by passing the digital imaging information through an encoder to obtain code and through a decoder to output the one or more measurements of the aneurysm sac based on the code, or automatic segmentation of raw imaging information to identify the aneurysm sac and to obtain the one or more measurements of the aneurysm sac by passing the digital imaging information through the encoder to obtain the code and through the decoder to output the one or more measurements of the aneurysm sac based on the code (see MIHALEF: e.g., -- The decision support for the treatment planning process is based on a systemic analysis of the above-mentioned factors along with simulated treatment of the patient’s aneurysm using a virtual implantation of a flow-diverting stent. Only the data acquired prior to the treatment planning (medical images, blood biomarkers, patient medical history and demographics) is used for further processing. The model may also utilize simulated outcomes using machine learning or physics-based simulators, such as computational fluid dynamics models. For such simulations, an anatomical model may be extracted using either manual, semi-automatic or automatic segmentation algorithms. The virtual deployment system is integrated into the decision support. [0022] Figure 1 shows a flow chart or outline of one embodiment of the decision support for endovascular procedure planning. The outline illustrates a workflow, including device modeling (fitting the device characteristics to a physics model), deployment of the device model in a representation of the vessel of the patient, and outcome prediction based on the deployment.--, in [0021]-[0022], and, ….[0047] Any machine learning or training may be used. A probabilistic boosting tree, support vector machine, neural network, sparse auto-encoding classifier, Bayesian network, or other now known or later developed machine learning may be used. Any semi-supervised, supervised, or unsupervised learning may be used. Hierarchal or other approaches may be used. In one embodiment, the classification is by a machine-learnt classifier learnt with deep learning. As part of identifying features that distinguish between different outcomes, the classifier is also machine learnt. Any deep learning approach or architecture may be used. For example, a convolutional neural network is used--, in [0047]),
wherein the outcome prediction for each of the at least one intrasaccular implant device is based on the one or more measurements obtained for the aneurysm sac, dimensions of the at least one intrasaccular implant device, and the clinical information for the aneurysm patient (see MIHALEF: e.g., ….[0047] Any machine learning or training may be used. A probabilistic boosting tree, support vector machine, neural network, sparse auto-encoding classifier, Bayesian network, or other now known or later developed machine learning may be used. Any semi-supervised, supervised, or unsupervised learning may be used. Hierarchal or other approaches may be used. In one embodiment, the classification is by a machine-learnt classifier learnt with deep learning. As part of identifying features that distinguish between different outcomes, the classifier is also machine learnt. Any deep learning approach or architecture may be used. For example, a convolutional neural network is used….
[0049] The machine-learned network is trained to output a selection, outcome, risk, deployment, or other clinical support information. The information extracted from the model may include the predicted outcomes and risks under different therapy choices. The information may select the therapy configuration, such as (i) start and end locations of the flow diverter, (ii) the number of flow diverters deployed, (iii) the configuration (overlapping or deployment into different branches), (iv) the reference radius of the flow diverter to use, (v) mean and maximum porosity in the neck/non-vessel area, etc. The information output by the machine-learned network may include identification of similar patients and the therapy choices made for the similar patients identified from a patient database and their outcomes.--, in [0044]-[0049], and, --[0077] In act 26, the decision support processor generates an image, and a display screen displays the image. The output (e.g., outcome, hemodynamic quantity, porosity, visual depiction of the deployment or end result of deployment, or combinations thereof) is transmitted to a display…..[0079] In one embodiment, the clinical decisions are visualized, either as text or in a graphical way (e.g. overlaid on the medical images) and presented to the clinician. Decision support information, such as treatments, risks, guidelines, or other information, may be output. Diagnostic rules for treatment, such as based on guidelines or studies, may be output as decision support.
[0080] Any display of the decision or decisions may be used. In one embodiment, a decision tree shows the clinical decision, other possible decisions, and further treatment options resulting from the clinical decision. Besides a basic text-based display, another option is to display in a hierarchy not only the currently selected clinical decision but also possible subsequent clinical decisions.
[0081] The displayed image from the deployment simulation may be used to visually guide the placement of the device or devices. For instance, the imaging during the procedure may utilize the output to overlay planes for the start and end locations of the flow diverter to aid in device placement.—[0077]-[0081]).
Re Claims 4-8, claims 4-8 are corresponding system claim to claims 1-3, respectively. Claims 4-8 thus are rejected for the similar reasons for claims 1-3. See above discussions with regard to claims 1-3 respectively. MIHALEF as modified by ITU further disclose further disclose system for providing outcome predictions for intrasaccular implant devices based on imaging and clinical information (see MIHALEF: e.g., --[0046] The decision support uses the anatomical and/or physiological knowledge using any of the different machine learning models. For training, many samples with known ground truth (e.g., selections) are used. For the samples, the machine learning model inputs may be real (from patients, or bench-scale measurements), virtual (synthetic or not representative of a given patient or physical bench arrangement), or a combination of the two. For patient data, the input samples may contain the patient images, blood biomarkers, demographics, measurements and/or genetic data. For the ground truth, the input data includes the therapy that was chosen for the patient. Other ground truths may alternatively or additionally be used, such as the recorded outcome at different temporal points (e.g., 30 days, 90 days, etc.) and/or the occurrence or lack thereof of adverse events. …[0048] The machine-learnt predictor, with or without deep learning, is trained to associate the categorical labels (output clinical decision of the selections) to the extracted values of one or more features. The machine- learning uses training data with ground truth to learn to select based on the input vector. The resulting machine-learnt network is a matrix for inputs, weighting, convolution kernels, and/or combinations to output a clinical decision. Using the learned network, the processor inputs the extracted values for features and outputs the selection. ….[0047] Any machine learning or training may be used. A probabilistic boosting tree, support vector machine, neural network, sparse auto-encoding classifier, Bayesian network, or other now known or later developed machine learning may be used. Any semi-supervised, supervised, or unsupervised learning may be used. Hierarchal or other approaches may be used. In one embodiment, the classification is by a machine-learnt classifier learnt with deep learning. As part of identifying features that distinguish between different outcomes, the classifier is also machine learnt. Any deep learning approach or architecture may be used. For example, a convolutional neural network is used….
[0049] The machine-learned network is trained to output a selection, outcome, risk, deployment, or other clinical support information. The information extracted from the model may include the predicted outcomes and risks under different therapy choices. The information may select the therapy configuration, such as (i) start and end locations of the flow diverter, (ii) the number of flow diverters deployed, (iii) the configuration (overlapping or deployment into different branches), (iv) the reference radius of the flow diverter to use, (v) mean and maximum porosity in the neck/non-vessel area, etc. The information output by the machine-learned network may include identification of similar patients and the therapy choices made for the similar patients identified from a patient database and their outcomes.--, in [0044]-[0049]).
Re Claim 9, MIHALEF as modified by ITU further disclose wherein the clinical information includes at least one of: demographic information for the aneurysm patient,
aneurysm information associated with the imaging information, dimension information for an aneurysm imaged in the imaging information, allergies of the aneurysm patient, medication information for the aneurysm patient, or pre-existing condition information for the aneurysm patient (see MIHALEF: e.g., --[0046] The decision support uses the anatomical and/or physiological knowledge using any of the different machine learning models. For training, many samples with known ground truth (e.g., selections) are used. For the samples, the machine learning model inputs may be real (from patients, or bench-scale measurements), virtual (synthetic or not representative of a given patient or physical bench arrangement), or a combination of the two. For patient data, the input samples may contain the patient images, blood biomarkers, demographics, measurements and/or genetic data. For the ground truth, the input data includes the therapy that was chosen for the patient. Other ground truths may alternatively or additionally be used, such as the recorded outcome at different temporal points (e.g., 30 days, 90 days, etc.) and/or the occurrence or lack thereof of adverse events. …[0048] The machine-learnt predictor, with or without deep learning, is trained to associate the categorical labels (output clinical decision of the selections) to the extracted values of one or more features. The machine- learning uses training data with ground truth to learn to select based on the input vector. The resulting machine-learnt network is a matrix for inputs, weighting, convolution kernels, and/or combinations to output a clinical decision. Using the learned network, the processor inputs the extracted values for features and outputs the selection. ….[0047] Any machine learning or training may be used. A probabilistic boosting tree, support vector machine, neural network, sparse auto-encoding classifier, Bayesian network, or other now known or later developed machine learning may be used. Any semi-supervised, supervised, or unsupervised learning may be used. Hierarchal or other approaches may be used. In one embodiment, the classification is by a machine-learnt classifier learnt with deep learning. As part of identifying features that distinguish between different outcomes, the classifier is also machine learnt. Any deep learning approach or architecture may be used. For example, a convolutional neural network is used….
[0049] The machine-learned network is trained to output a selection, outcome, risk, deployment, or other clinical support information. The information extracted from the model may include the predicted outcomes and risks under different therapy choices. The information may select the therapy configuration, such as (i) start and end locations of the flow diverter, (ii) the number of flow diverters deployed, (iii) the configuration (overlapping or deployment into different branches), (iv) the reference radius of the flow diverter to use, (v) mean and maximum porosity in the neck/non-vessel area, etc. The information output by the machine-learned network may include identification of similar patients and the therapy choices made for the similar patients identified from a patient database and their outcomes.--, in [0044]-[0049]; and, …[0062] In act 23, the decision support processor calculates a porosity of the endovascular device from the deployment. For the flow diverter or other implant, the porosity, in part, controls the flow. In-situ implantation may produce variations in the metal coverage ratio (MCR) and porosity of the device, especially in the aneurysm and adjacent regions. Hemodynamics is consequently affected by such regions, so porosity is a consideration when performing the implantation. By simulating deployment, the porosity that results in general or by location may be determined for estimating outcome.--, in [0060]-[0063];also see ITU: e.g., -- [0102] Other features extracted include parameters for one or more abnormalities of the vessel structure. Abnormal morphology may be characterized by characteristics of calcification, characteristics of the plaque (e.g., fibrous tissue, lipid tissue, necrotic tissue, calcified tissue), characteristics of thrombus, characteristics of diffuse disease, presence of total or sub-total occlusion, presence of myocardial bridging (superficial and/or deep), congenital anomalies of coronary arteries (e.g., anomalous origin of a coronary artery from an abnormal sinus of Valsalva with an inter-arterial course between the great arteries, anomalous origin of one coronary artery from the pulmonary trunk, or others), aneurysmal dilatation and superimposed atherosclerosis, “high take off” coronary artery (e.g., the ostium is several millimeters above the sino-tubular junction (the artery may have a sharp downward angle and runs partially through the aortic wall)), myocardial bridging: superficial and deep, coronary fistula, coronary artery dissection, coronary vasculitis (e.g., rheumatoid arthritis, systemic lupus erythematosus (SLE), or Behçet's disease, Kawasaki disease, polyarteritis nodosa, and/or persisting (post) inflammatory aneurysms), fibromuscular dysplasia, coronary micro embolization, and/or left or right dominance. Additional, different, or fewer abnormality features may be used.--, in [0102], and, -- A local ischemia weight value is estimated independently for each root/branch/leaf segment using geometric features of the segment, such as the reference radius, length, tapering rate and other features…. An average value of healthy radiuses of the entire branch or a part of the branch, an average value of healthy radiuses obtained when excluding the largest x % and the smallest y % of the radius values of the entire branch or a part of the branch, or maximum or minimum value of healthy radii of the entire branch or part of the branch are computed.--, in [0110]-[0112]; and,
-- [0146] The in vitro model 23 and the flow conditions may be modified in numerous ways to generate a large number of setups. For example, the number, position and shape of the occluders is altered. As another example, the resistance at one or more locations is altered. In yet another example, the operation of the pump is altered. The number of side branches and any occlusions may be altered. Other alterations of combinations of different alterations are used to create different models with corresponding features and resulting flow characteristics.--, in [0145]-[0146], and, -- [0152] More than one classifier may be created. Since different types of branches and regions are present in a vessel tree, different classifiers may be machine trained for the different branches and/or regions. For example, different classifiers are trained for main and side branches, bifurcation regions and single branch regions, different types of pathologic regions such as different types of single branch stenotic regions (e.g., focal, long, diffuse, restenosis, or other), different types of bifurcation stenoses (e.g. a separate model for each bifurcation stenosis type in the medina classification), different types of aneurysms, different types of plaque, different types of total and/or sub-total occlusions, stenotic and regurgitant valves, various pathologies of the heart (e.g., past infarct or myopathies), or different types of branches (e.g. in case of coronary arterial trees: LM, LAD, LCx, RCA, Diagonal, OM, or other). Since the training is based on synthetic geometries, a large enough number of training instances may be generated for each of these different classifiers. Another possibility is to divide the geometry into separate segments (e.g. for coronary geometries: proximal LAD, mid LAD, and distal LAD) and to extract the features discussed in the previous sections separately for each segment. Afterwards these features may either be combined into cumulative features or used separately for a single or multiple machine learning algorithms for predicting a hemodynamic metric of interest.--, in [0152]).
Re Claim 10, MIHALEF as modified by ITU further disclose wherein the outcome prediction comprises at least one of: one or more measurements for an aneurysm imaged in the imaging information, or a best predicted size of an intrasaccular device for the aneurysm imaged in the imaging information (see MIHALEF: e.g., --[0026] In stage 16, a machine-learned network predicts outcome based on the simulated deployment, imaging data (e.g., segmented vessel), and/or other patient-specific information. The machine-learned network may be used to output implant information (e.g., type, size, placement, number of devices, configuration of device, etc.) used for the simulation in stage 14. Alternatively or additionally, the machine-learned network may be used to predict outcome or prognosis for a simulated deployment. The decision support system may be trained on outcome data, if available, to predict not only short-term physiological response, but also long-term outcomes and/or costs. The outcome or prognosis may be used to compare or select implant information, or the network directly outputs the selection.
[0027] In stage 18, the simulated deployment, predicted outcome, and/or a calculated hemodynamic characteristic based on the deployment are provided to the clinician. The information may be used to support decisions for planning an endovascular implantation.--, in [0026]-[0027]; and, --[0046] The decision support uses the anatomical and/or physiological knowledge using any of the different machine learning models. For training, many samples with known ground truth (e.g., selections) are used. For the samples, the machine learning model inputs may be real (from patients, or bench-scale measurements), virtual (synthetic or not representative of a given patient or physical bench arrangement), or a combination of the two. For patient data, the input samples may contain the patient images, blood biomarkers, demographics, measurements and/or genetic data. For the ground truth, the input data includes the therapy that was chosen for the patient. Other ground truths may alternatively or additionally be used, such as the recorded outcome at different temporal points (e.g., 30 days, 90 days, etc.) and/or the occurrence or lack thereof of adverse events. …[0048] The machine-learnt predictor, with or without deep learning, is trained to associate the categorical labels (output clinical decision of the selections) to the extracted values of one or more features. The machine- learning uses training data with ground truth to learn to select based on the input vector. The resulting machine-learnt network is a matrix for inputs, weighting, convolution kernels, and/or combinations to output a clinical decision. Using the learned network, the processor inputs the extracted values for features and outputs the selection. ….[0047] Any machine learning or training may be used. A probabilistic boosting tree, support vector machine, neural network, sparse auto-encoding classifier, Bayesian network, or other now known or later developed machine learning may be used. Any semi-supervised, supervised, or unsupervised learning may be used. Hierarchal or other approaches may be used. In one embodiment, the classification is by a machine-learnt classifier learnt with deep learning. As part of identifying features that distinguish between different outcomes, the classifier is also machine learnt. Any deep learning approach or architecture may be used. For example, a convolutional neural network is used….
[0049] The machine-learned network is trained to output a selection, outcome, risk, deployment, or other clinical support information. The information extracted from the model may include the predicted outcomes and risks under different therapy choices. The information may select the therapy configuration, such as (i) start and end locations of the flow diverter, (ii) the number of flow diverters deployed, (iii) the configuration (overlapping or deployment into different branches), (iv) the reference radius of the flow diverter to use, (v) mean and maximum porosity in the neck/non-vessel area, etc. The information output by the machine-learned network may include identification of similar patients and the therapy choices made for the similar patients identified from a patient database and their outcomes.--, in [0044]-[0049]; and, …[0062] In act 23, the decision support processor calculates a porosity of the endovascular device from the deployment. For the flow diverter or other implant, the porosity, in part, controls the flow. In-situ implantation may produce variations in the metal coverage ratio (MCR) and porosity of the device, especially in the aneurysm and adjacent regions. Hemodynamics is consequently affected by such regions, so porosity is a consideration when performing the implantation. By simulating deployment, the porosity that results in general or by location may be determined for estimating outcome.--, in [0060]-[0063];also see ITU: e.g., -- [0102] Other features extracted include parameters for one or more abnormalities of the vessel structure. Abnormal morphology may be characterized by characteristics of calcification, characteristics of the plaque (e.g., fibrous tissue, lipid tissue, necrotic tissue, calcified tissue), characteristics of thrombus, characteristics of diffuse disease, presence of total or sub-total occlusion, presence of myocardial bridging (superficial and/or deep), congenital anomalies of coronary arteries (e.g., anomalous origin of a coronary artery from an abnormal sinus of Valsalva with an inter-arterial course between the great arteries, anomalous origin of one coronary artery from the pulmonary trunk, or others), aneurysmal dilatation and superimposed atherosclerosis, “high take off” coronary artery (e.g., the ostium is several millimeters above the sino-tubular junction (the artery may have a sharp downward angle and runs partially through the aortic wall)), myocardial bridging: superficial and deep, coronary fistula, coronary artery dissection, coronary vasculitis (e.g., rheumatoid arthritis, systemic lupus erythematosus (SLE), or Behçet's disease, Kawasaki disease, polyarteritis nodosa, and/or persisting (post) inflammatory aneurysms), fibromuscular dysplasia, coronary micro embolization, and/or left or right dominance. Additional, different, or fewer abnormality features may be used.--, in [0102], and, -- A local ischemia weight value is estimated independently for each root/branch/leaf segment using geometric features of the segment, such as the reference radius, length, tapering rate and other features…. An average value of healthy radiuses of the entire branch or a part of the branch, an average value of healthy radiuses obtained when excluding the largest x % and the smallest y % of the radius values of the entire branch or a part of the branch, or maximum or minimum value of healthy radii of the entire branch or part of the branch are computed.--, in [0110]-[0112]; and,
-- [0146] The in vitro model 23 and the flow conditions may be modified in numerous ways to generate a large number of setups. For example, the number, position and shape of the occluders is altered. As another example, the resistance at one or more locations is altered. In yet another example, the operation of the pump is altered. The number of side branches and any occlusions may be altered. Other alterations of combinations of different alterations are used to create different models with corresponding features and resulting flow characteristics.--, in [0145]-[0146], and, -- [0152] More than one classifier may be created. Since different types of branches and regions are present in a vessel tree, different classifiers may be