Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-4, 6-9, 12-13, 16-18, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Naghavi (US 20230352181 A1) in view of Slomka et al. (US 20250111516 A1).
Regarding claim 1, Naghavi teaches a method, comprising: receiving a computed tomography (CT) calcium score image of a chest (see para [0079]; “a set of CT scan images is received”, see also para [0080]; “non-contrast CT scans, which can be cardiac scans and/or full chest scans. In one embodiment, non-contrast, gated and non-gated, chest CT scans can be used” and para [0086]; “system 200 using non-contrast CAC scans” and para [0054]; “methods disclosed here using cardiac CT scans (such as coronary artery calcium (CAC) scan”); identifying tissue of interest in the CT calcium score image (see para [0050]; “automated measurement of cardiac chamber volume and left ventricular wall mass…the systems and methods can facilitate identifying asymptomatic patients at risk of developing atrial fibrillation (AF) and heart failure (HF) based on enlarged left atrium (LA), for example. In one embodiment, the inventive systems and methods can facilitate estimating the volumes of left atrium (LA), left ventricle (LV), right atrium (RA), right ventricle (RV), and left ventricle wall (LVW)” Note; segment and identify cardiac chambers implies the "tissue of interest); analyzing the CT calcium score image to determine features of the identified tissue of interest (see also para [0021]; “provide the CAC score and/or the cardiovascular volumetry index. In certain embodiments, using AI includes using non-contrast enhanced CT scan images as input to the AI. In one embodiment, using AI to provide the cardiovascular volumetry index includes estimating a volume of a left ventricle”, see also para [0079; “segment cardiovascular structures and to estimate cardiovascular structure volumes” Note; calculating the volumes of cardiac chambers implies features of the identified tissue of interest). However, Naghavi does not disclose and determining a risk prediction of major adverse cardiovascular event (MACE) based on the features.
In the same field of endeavor, Slomka et al. teaches and determining a risk prediction of major adverse cardiovascular event (MACE) based on the features (see para [0070]; “Automated and rapid CAC quantification from CTAC scans using a novel deep learning approach that integrates the data from adjacent CT slices is disclosed. The prognostic value of CAC scores obtained from deep learning (DL) segmentations of CTAC scans (DL-CTAC scores) in the prediction of major adverse cardiac events (MACE)”, Note; CTAC scans are used, a deep learning model automatically identifies and quantifies calcium-related features (CAC scores), and these quantified features are used to predict the risk of Major Adverse Cardiac Events (MACE). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify risk assessment systems and methods for patients with chest pain of Naghavi in view of a use of convolutional long short-term memory (LSTM) networks are leveraged to segment and/or quantify medical imaging data of Slomka et al. in order to segment smaller and more difficult to classify targets (see para [0070]).
Regarding claim 2, the rejection of claim 1 is incorporated herein.
Naghavi in the combination further teach wherein the features include a feature other than coronary artery calcification (see para [0088]; “CT coronary calcium imaging gives direct evidence of coronary artery disease in patents”).
Regarding claim 3, the rejection of claim 1 is incorporated herein.
Naghavi in the combination further teach wherein the features pertain to at least one of: coronary calcifications; aortic calcifications; aortic valve calcifications; liver; liver fat; pericardial fat depots; epicardial fat depots; pericoronary fat; heart morphometrics; bone density; or muscle (see para [0094]; “system 1400 can be configured to generate one or more additional reports from CT scan results covering one or more subject matters from the following group: lung nodules, emphysema score, cardiac and aortic sizes, pericardial fat, fatty liver, and bone density” Note; claim uses or option and one listed features required).
Regarding claim 4, the rejection of claim 1 is incorporated herein.
Naghavi in the combination further teach wherein the determining comprises: providing a machine learning model trained to relate the features to a risk; and predicting the risk using the machine learning model (see para [0076]; “Various machine learning and deep learning and image processing tools can be used to maximize the performance of the system”), using calcium-related, fat- related, texture-related, intensity-related, or morphometrics-related features (see para [0093]-[0094]; “the artificial intelligence used to calculate a CAC score can include: detecting features from at least one or more portions of CT scan images.. ..CT scan results covering .. lung nodules, emphysema score, cardiac and aortic sizes, pericardial fat, fatty liver, and bone density”).
Regarding claim 6, the rejection of claim 1 is incorporated herein.
Naghavi in the combination further teach further comprising: preprocessing the CT calcium score image to enhance the CT calcium score image, wherein the identifying is performed on the CT calcium score image as enhanced (see para [0073]; “a calibration factor can be used to reduce noise effects and CT based LV mass measurements versus MRI. Outputs can be inspected and case failures, and pursue iterative enhancement”).
Regarding claim 7, the rejection of claim 6 is incorporated herein.
Naghavi in the combination further teach wherein the preprocessing comprises at least one of motion artifact suppression, noise reduction, image volume normalization, automated beam hardening correction, or deconvolution (see para [0073]; “a calibration factor can be used to reduce noise effects and CT based LV mass measurements versus MRI. Outputs can be inspected and case failures, and pursue iterative enhancement”).
Regarding claim 8, the rejection of claim 1 is incorporated herein.
Naghavi in the combination further teach wherein the identified tissue of interest comprises at least one of a liver, cardiac chambers, calcifications in coronary arteries, calcifications in an aorta, calcifications in an aortic valve, calcifications in a mitral annulus, fat depots in epicardium regions, fat depots in pericardium regions, fat depots in periaortic regions, or fat depots in pericoronary regions (see para [0068]; “Accurate assessment of cardiac chamber sizes, LV mass, aortic size and calcifications, can improve cardiac risk prediction over standard risk equations”, see also para [0094]; “CT scan results covering one or more subject matters from the following group: lung nodules, emphysema score, cardiac and aortic sizes, pericardial fat, fatty liver, and bone density”).
Regarding claim 9, the rejection of claim 1 is incorporated herein.
Naghavi in the combination further teach wherein the analyzing comprises at least one of: analysis of coronary calcifications; analysis of aortic calcifications; analysis of aortic valve calcifications; analysis of liver; analysis of liver fat; analysis of pericardial fat depots; analysis of epicardial fat depots; analysis of pericoronary fat; analysis of heart morphometrics, analysis of bone density, or analysis of muscle (see para [0068]; “Accurate assessment of cardiac chamber sizes, LV mass, aortic size and calcifications, can improve cardiac risk prediction over standard risk equations”, see also para [0094]; “CT scan results covering one or more subject matters from the following group: lung nodules, emphysema score, cardiac and aortic sizes, pericardial fat, fatty liver, and bone density”).
Regarding claim 12, the rejection of claim 1 is incorporated herein.
Naghavi in the combination further teach and epicardial fat detected in the CT calcium score image (see para [0094]; “more subject matters from the following group: lung nodules, emphysema score, cardiac and aortic sizes, pericardial fat, fatty liver, and bone density”).
Slomka et al. in the combination further teach further comprising determining the risk prediction based on coronary calcifications (see para [0067]; “This quantitative analysis can be used to track the progression of coronary artery calcification”)
Regarding claim 13, Naghavi teaches an analysis apparatus, comprising a processor (see Abstract; “A computer enabled risk calculator”); that when executed by the processor, cause the processor to perform operations comprising receiving a CT calcium score image associated with a patient (see para [0079]; “a set of CT scan images is received”, see also para [0080]; “non-contrast CT scans, which can be cardiac scans and/or full chest scans. In one embodiment, non-contrast, gated and non-gated, chest CT scans can be used” and para [0086]; “system 200 using non-contrast CAC scans” and para [0054]; “methods disclosed here using cardiac CT scans (such as coronary artery calcium (CAC) scan”); processing the CT calcium score image to identify at least one calcium- related feature of interest or at least one fat-related feature of interest (see para [0050]; “automated measurement of cardiac chamber volume and left ventricular wall mass…the systems and methods can facilitate identifying asymptomatic patients at risk of developing atrial fibrillation (AF) and heart failure (HF) based on enlarged left atrium (LA), for example. In one embodiment, the inventive systems and methods can facilitate estimating the volumes of left atrium (LA), left ventricle (LV), right atrium (RA), right ventricle (RV), and left ventricle wall (LVW)” Note; segment and identify cardiac chambers implies the "tissue of interest). However, Naghavi does not teach and memory storing a trained machine learning model that relates features of interest to a risk prediction for MACE and instructions, and providing data related to the at least one calcium-related feature of interest or the at least one fat-related feature of interest to the trained machine learning model to generate a risk prediction for MACE for the patient and generating a report indicating the risk prediction for MACE for a non- clinician.
In the same field of endeavor, Slomka et al. teaches and memory storing a trained machine learning model that relates features of interest to a risk prediction for MACE (see para [0070]; “Automated and rapid CAC quantification from CTAC scans using a novel deep learning approach that integrates the data from adjacent CT slices is disclosed. The prognostic value of CAC scores obtained from deep learning (DL) segmentations of CTAC scans (DL-CTAC scores) in the prediction of major adverse cardiac events (MACE), see also para [0105]; “The processing module 106 can process the imaging data 104 by applying it to the multi-branch model 112 to generate output data 108. The multi-branch model 112 can be stored in any suitable location, such as local or remote memory accessible to processing module 106”), and instructions, and providing data related to the at least one calcium-related feature of interest or the at least one fat-related feature of interest to the trained machine learning model to generate a risk prediction for MACE for the patient (see para [0070]; “Automated and rapid CAC quantification from CTAC scans using a novel deep learning approach that integrates the data from adjacent CT slices is disclosed. The prognostic value of CAC scores obtained from deep learning (DL) segmentations of CTAC scans (DL-CTAC scores) in the prediction of major adverse cardiac events (MACE)”, Note; CTAC scans are used, a deep learning model automatically identifies and quantifies calcium-related features (CAC scores), and these quantified features are used to predict the risk of Major Adverse Cardiac Events (MACE)); and generating a report indicating the risk prediction for MACE for a non- clinician (see para [0106]; “Consistent data has shown the strong prognostic value of CAC assessment in asymptomatic individuals. Assessment of CAC during SPECT MPI scans has been shown to add to perfusion in risk assessment and improve assessment of pretest likelihood of CAD, thereby contributing to diagnostic accuracy and leading to changes in preventive medications and beneficial changes in patient's adherence to medication recommendations” Note; CAC (measured via scans) provides strong, personalized risk data for individuals (asymptomatic patients). Giving this data to a patient (non-clinician) or a clinician implies the same: understanding the individual's specific risk level). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify risk assessment systems and methods for patients with chest pain of Naghavi in view of a use of convolutional long short-term memory (LSTM) networks are leveraged to segment and/or quantify medical imaging data of Slomka et al. in order to segment smaller and more difficult to classify targets (see para [0070]).
Regarding claim 16, the rejection of claim 13 is incorporated herein.
Slomka et al. in the combination further teach wherein the at least one calcium-related feature of interest includes a whole heart calcification mass or an aortic calcification mass (see para [0088]; “the model disclosed herein can successfully distinguish CAC from non-coronary opacities like aortic or mitral calcifications”).
Regarding claim 17, the rejection of claim 13 is incorporated herein.
Naghavi in the combination further teach wherein the at least one fat-related feature of interest includes liver fat, pericardial fat, epicardial fat, periaortic fat, or pericoronary fat (see para [0094]; “CT scan results covering one or more subject matters from the following group: lung nodules, emphysema score, cardiac and aortic sizes, pericardial fat, fatty liver, and bone density”).
Regarding claim 18, the scope of claim 18 is fully applicable inhere, the rejection analysis of claim 13 is equally applicable here.
Regarding claim 21, the rejection of claim 18 is incorporated herein.
Slomka et al. in the combination further teach wherein the at least one calcium-related feature of interest includes a whole heart calcification mass or an aortic calcification mass (see para [0088]; “the model disclosed herein can successfully distinguish CAC from non-coronary opacities like aortic or mitral calcifications”).
Regarding claim 22, the rejection of claim 18 is incorporated herein.
Naghavi et al. in the combination further teach wherein the at least one fat-related feature of in interest includes liver fat, pericardial fat, epicardial fat, periaortic fat, or pericoronary fat (see para [0094]; “CT scan results covering one or more subject matters from the following group: lung nodules, emphysema score, cardiac and aortic sizes, pericardial fat, fatty liver, and bone density”).
Claims 5, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Naghavi in view of Slomka et al. as applied in claim 1 above, and further in view of Washko et al. (US 20230027734 A1).
Regarding claim 5, the rejection of claim 1 is incorporated herein. The combination of Naghavi and Slomka et al. as a whole does not teach further comprising: determining a relative contribution of the determined features to the risk prediction; and generating a report summarizing the risk prediction, suggested risk-reduction actions prioritized based on the relative contribution of the determined features, percentile of similar group risk among population, or pictures of specific regions showing risk histograms.
Washko et al. teach further comprising: determining a relative contribution of the determined features to the risk prediction; and generating a report summarizing the risk prediction, suggested risk-reduction actions prioritized based on the relative contribution of the determined features, percentile of similar group risk among population, or pictures of specific regions showing risk histograms (see para [0164]; “the risk predictions 270 for the subject can be displayed to a user e.g., a clinician user. Thus, the clinician user can inform the subject of the risk of cancer that is predicted for the subject. ..For example, if a risk of cancer prediction for a subject indicates that the subject is likely to develop cancer within a time period, information such as the features that most heavily contributed to the risk of cancer prediction can be displayed to the user e.g., clinician user. For example, a subject predicted to have a risk of cancer can be largely due to a percentage of the subject's lung occupied by centrilobular emphysema. Thus, the identification of the feature and/or the value of the feature (e.g., percentage of the subject's lung occupied by centrilobular emphysema) can be displayed to a user e.g., clinician user”, Note; claim uses or option and only one required). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify risk assessment systems and methods for patients with chest pain of Naghavi in view of a use of convolutional long short-term memory (LSTM) networks are leveraged to segment and/or quantify medical imaging data of Slomka et al. and risk prediction models trained to analyze computed tomography images, for predicting risk of lung cancer of Washko et al. in order to develop preventive therapies for lung cancer by enabling clinical trial enrichment (see para [0164]).
Regarding claim 15, the rejection of claim 13 is incorporated herein.
Washko et al. in the combination further teach wherein the instructions further comprise instructions, that when executed by the processor, cause the processor to perform operations comprising determining a relative contribution of the at least one calcium-related feature of interest or the at least one fat-related feature of interest to the risk prediction; and including suggested risk-reduction actions in the report, wherein the risk-reduction actions are prioritized based on the relative contribution of the at least one calcium-related feature of interest or the at least one fat-related feature of interest (see para [0164]; “the risk predictions 270 for the subject can be displayed to a user e.g., a clinician user. Thus, the clinician user can inform the subject of the risk of cancer that is predicted for the subject. ..For example, if a risk of cancer prediction for a subject indicates that the subject is likely to develop cancer within a time period, information such as the features that most heavily contributed to the risk of cancer prediction can be displayed to the user e.g., clinician user. For example, a subject predicted to have a risk of cancer can be largely due to a percentage of the subject's lung occupied by centrilobular emphysema. Thus, the identification of the feature and/or the value of the feature (e.g., percentage of the subject's lung occupied by centrilobular emphysema) can be displayed to a user e.g., clinician user”, Note; claim uses or option and only one required). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify risk assessment systems and methods for patients with chest pain of Naghavi in view of a use of convolutional long short-term memory (LSTM) networks are leveraged to segment and/or quantify medical imaging data of Slomka et al. and risk prediction models trained to analyze computed tomography images, for predicting risk of lung cancer of Washko et al. in order to develop preventive therapies for lung cancer by enabling clinical trial enrichment (see para [0164]).
Regarding claim 20, the rejection of claim 18 is incorporated herein.
Washko et al. in the combination further teach comprising: determining a relative contribution of the at least one calcium-related feature of interest or the at least one fat-related feature of interest to the risk prediction; and including suggested risk-reduction actions in the report, wherein the risk-reduction actions are prioritized based on the relative contribution of the at least one calcium-related feature of interest or the at least one fat-related feature of interest (see para [0164]; “the risk predictions 270 for the subject can be displayed to a user e.g., a clinician user. Thus, the clinician user can inform the subject of the risk of cancer that is predicted for the subject. ..For example, if a risk of cancer prediction for a subject indicates that the subject is likely to develop cancer within a time period, information such as the features that most heavily contributed to the risk of cancer prediction can be displayed to the user e.g., clinician user. For example, a subject predicted to have a risk of cancer can be largely due to a percentage of the subject's lung occupied by centrilobular emphysema. Thus, the identification of the feature and/or the value of the feature (e.g., percentage of the subject's lung occupied by centrilobular emphysema) can be displayed to a user e.g., clinician user”, Note; claim uses or option and only one required). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify risk assessment systems and methods for patients with chest pain of Naghavi in view of a use of convolutional long short-term memory (LSTM) networks are leveraged to segment and/or quantify medical imaging data of Slomka et al. and risk prediction models trained to analyze computed tomography images, for predicting risk of lung cancer of Washko et al. in order to develop preventive therapies for lung cancer by enabling clinical trial enrichment (see para [0164]).
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Naghavi in view of Slomka et al. as applied in claim 1 above, and further in view of Morteza et al. (US 20240120095 A1).
Regarding claim 10, the rejection of claim 1 is incorporated herein. The combination of Naghavi and Slomka et al. as a whole does not teach further comprising assessing bone mineral density from CT intensity values in spine vertebrae in the CT calcium score image; and determining a risk prediction of fracture based on the assessments.
Morteza et al. teach further comprising assessing bone mineral density from CT intensity values in spine vertebrae in the CT calcium score image; and determining a risk prediction of fracture based on the assessments (see claim 18; “an AI-based module configured to extract from the CT scans a CAC scans cardiac score, cardiac chambers volumetry data, and thoracic vertebral bone mineral density data a computerized CVD risk calculator configured to provide a minimum Net Reclassification Index of 0.1 for risk assessment of individuals at risk of future heart failure, atrial fibrillation, stroke, LVH, ALVD, CVD related death and all-cause mortality”, see also para [0062]; “Each DL model has two steps to automatically detect individual vertebrae and disks. In the first step, the models were trained to focus on the whole spine area and trained for 100 epochs”, and para [0004]; “CAC scoring has been used to prove the presence of CAD and to facilitate predicting the risk of serious coronary events”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify risk assessment systems and methods for patients with chest pain of Naghavi in view of a use of convolutional long short-term memory (LSTM) networks are leveraged to segment and/or quantify medical imaging data of Slomka et al. and method for determining an individual's risk of an adverse health outcome particularly a near-term cardiovascular even of Morteza et al. in order to detect active and inflamed plaques from passive and stable ones (see claim 18).
Regarding claim 11, the rejection of claim 1 is incorporated herein.
Morteza et al. in the combination further teach further comprising assessing skeletal muscle intensity values in the CT calcium score image; and determining a risk prediction of sarcopenia based on the assessments (see para [0057]; “detect osteoporosis and osteopenia by measuring thoracic vertebral bone density, cardiometabolic associated excess intrathoracic visceral fat, fatty liver disease, muscle loss or sarcopenia, and other abnormalities in other organs such as thyroid nodules and esophageal masses. All these findings can be actionable and can be automatically detected by a suitably trained artificial intelligence system in both non-contrast and contrast enhanced chest CT scans”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify risk assessment systems and methods for patients with chest pain of Naghavi in view of a use of convolutional long short-term memory (LSTM) networks are leveraged to segment and/or quantify medical imaging data of Slomka et al. and method for determining an individual's risk of an adverse health outcome particularly a near-term cardiovascular even of Morteza et al. in order to rapidly extract actionable information for evaluating the risk of adverse health events (see para [0057]).
Claims 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Naghavi in view of Slomka et al. as applied in claim 1 above, and further in view of Min (US 20240312018 A1).
Regarding claim 14, the rejection of claim 13 is incorporated herein. The combination of Naghavi and Slomka et al. as a whole does not teach wherein the memory stores demographic information mapped to risk prediction for MACE; and the instructions further comprise instructions, that when executed by the processor, cause the processor to perform operations comprising identifying demographic information for the patient; accessing the stored demographic information to determine a representative risk prediction for MACE for training patients in a similar demographic to the patient; and including an indication of the representative risk prediction for MACE in the report.
Min teach wherein the memory stores demographic information mapped to risk prediction for MACE (see para [0081]; “Also, the patient information 140 may include a variety of patient information which is available from a patient portal, which may be accessed by one of the devices 130”); and the instructions further comprise instructions, that when executed by the processor, cause the processor to perform operations comprising identifying demographic information for the patient (see para [0081]; “mage information may include patient information including (one or more) characteristics of a patient, for example, age, gender, body mass index (BMI), medication, blood pressure, heart rate, height, weight, race, whether the patient is a smoker or non-smoker, body habitus (for example, the “physique” or “body type” which may be based on a wide range of factors), medical history, diabetes, hypertension, prior coronary artery disease (CAD)”); accessing the stored demographic information to determine a representative risk prediction for MACE for training patients in a similar demographic to the patient (see para [0081]; “Also, the patient information 140 may include a variety of patient information which is available from a patient portal, which may be accessed by one of the devices 130”, see also para [0173]; “generate a CAD risk assessment or stratification based on an ordinal scale or continuous scale. In some embodiments, the systems, methods, and devices described herein can be configured to generate a CAD risk assessment on an absolute scale or relative scale, for example compared to normal risk levels in a reference population, such as one determined based at least in part on similar demographics, such as gender or age, of the subject… a CAD risk assessment or stratification that is tied to a specific time horizon for MACE. For example, the CAD risk assessment can be based on the likelihood of a MACE occurring within a certain time frame”); and including an indication of the representative risk prediction for MACE in the report (see para [0056]; “the system can be further configured to utilize the identified, quantified, and/or classified one or more coronary arteries and/or plaque to generate a treatment plan, track disease progression, and/or a patient-specific medical report, for example using one or more artificial intelligence and/or machine learning algorithms”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify risk assessment systems and methods for patients with chest pain of Naghavi in view of a use of convolutional long short-term memory (LSTM) networks are leveraged to segment and/or quantify medical imaging data of Slomka et al. and devices for image based analysis of plaque Min in order to develop both local and systemic treatment plans (see para [0081]).
Regarding claim 19, the rejection of claim 18 is incorporated herein.
Min in the combination further teach comprising: identifying demographic information for the patient (see para [0081]; “mage information may include patient information including (one or more) characteristics of a patient, for example, age, gender, body mass index (BMI), medication, blood pressure, heart rate, height, weight, race, whether the patient is a smoker or non-smoker, body habitus (for example, the “physique” or “body type” which may be based on a wide range of factors), medical history, diabetes, hypertension, prior coronary artery disease (CAD)”); determining a representative risk prediction for MACE for training patients having similar demographic information to the patient; and including an indication of the representative risk prediction for MACE in the report (see para [0173]; “generate a CAD risk assessment or stratification based on an ordinal scale or continuous scale. In some embodiments, the systems, methods, and devices described herein can be configured to generate a CAD risk assessment on an absolute scale or relative scale, for example compared to normal risk levels in a reference population, such as one determined based at least in part on similar demographics, such as gender or age, of the subject… a CAD risk assessment or stratification that is tied to a specific time horizon for MACE. For example, the CAD risk assessment can be based on the likelihood of a MACE occurring within a certain time frame”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify risk assessment systems and methods for patients with chest pain of Naghavi in view of a use of convolutional long short-term memory (LSTM) networks are leveraged to segment and/or quantify medical imaging data of Slomka et al. and devices for image based analysis of plaque Min in order to develop both local and systemic treatment plans (see para [0081]).
Conclusion
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/WINTA GEBRESLASSIE/ Examiner, Art Unit 2677