Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models.

BACKGROUND: Statistically derived cardiovascular risk calculators (CVRC) that use conventional risk factors, generally underestimate or overestimate the risk of cardiovascular disease (CVD) or stroke events primarily due to lack of integration of plaque burden. This study investigates the role of machine learning (ML)-based CVD/stroke risk calculators (CVRCML) and compares against statistically derived CVRC (CVRCStat) based on (I) conventional factors or (II) combined conventional with plaque burden (integrated factors). METHODS: The proposed study is divided into 3 parts: (I) statistical calculator: initially, the 10-year CVD/stroke risk was computed using 13 types of CVRCStat (without and with plaque burden) and binary risk stratification of the patients was performed using the predefined thresholds and risk classes; (II) ML calculator: using the same risk factors (without and with plaque burden), as adopted in 13 different CVRCStat, the patients were again risk-stratified using CVRCML based on support vector machine (SVM) and finally; (III) both types of calculators were evaluated using AUC based on ROC analysis, which was computed using combination of predicted class and endpoint equivalent to CVD/stroke events. RESULTS: An Institutional Review Board approved 202 patients (156 males and 46 females) of Japanese ethnicity were recruited for this study with a mean age of 69±11 years. The AUC for 13 different types of CVRCStat calculators were: AECRS2.0 (AUC 0.83, P<0.001), QRISK3 (AUC 0.72, P<0.001), WHO (AUC 0.70, P<0.001), ASCVD (AUC 0.67, P<0.001), FRScardio (AUC 0.67, P<0.01), FRSstroke (AUC 0.64, P<0.001), MSRC (AUC 0.63, P=0.03), UKPDS56 (AUC 0.63, P<0.001), NIPPON (AUC 0.63, P<0.001), PROCAM (AUC 0.59, P<0.001), RRS (AUC 0.57, P<0.001), UKPDS60 (AUC 0.53, P<0.001), and SCORE (AUC 0.45, P<0.001), while the AUC for the CVRCML with integrated risk factors (AUC 0.88, P<0.001), a 42% increase in performance. The overall risk-stratification accuracy for the CVRCML with integrated risk factors was 92.52% which was higher compared all the other CVRCStat. CONCLUSIONS: ML-based CVD/stroke risk calculator provided a higher predictive ability of 10-year CVD/stroke compared to the 13 different types of statistically derived risk calculators including integrated model AECRS 2.0.

A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes.

BACKGROUND: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system. METHODS: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set. RESULTS: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC =0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of ~18% against AtheroRisk-Conventional ML (AUC =0.68, P<0.0001, 95% CI: 0.64 to 0.72). CONCLUSIONS: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment.

A Special Report on Changing Trends in Preventive Stroke/Cardiovascular Risk Assessment Via B-Mode Ultrasonography.

PURPOSE OF REVIEW: Cardiovascular disease (CVD) and stroke risk assessment have been largely based on the success of traditional statistically derived risk calculators such as Pooled Cohort Risk Score or Framingham Risk Score. However, over the last decade, automated computational paradigms such as machine learning (ML) and deep learning (DL) techniques have penetrated into a variety of medical domains including CVD/stroke risk assessment. This review is mainly focused on the changing trends in CVD/stroke risk assessment and its stratification from statistical-based models to ML-based paradigms using non-invasive carotid ultrasonography. RECENT FINDINGS: In this review, ML-based strategies are categorized into two types: non-image (or conventional ML-based) and image-based (or integrated ML-based). The success of conventional (non-image-based) ML-based algorithms lies in the different data-driven patterns or features which are used to train the ML systems. Typically these features are the patients' demographics, serum biomarkers, and multiple clinical parameters. The integrated (image-based) ML-based algorithms integrate the features derived from the ultrasound scans of the arterial walls (such as morphological measurements) with conventional risk factors in ML frameworks. Even though the review covers ML-based system designs for carotid and coronary ultrasonography, the main focus of the review is on CVD/stroke risk scores based on carotid ultrasound. There are two key conclusions from this review: (i) fusion of image-based features with conventional cardiovascular risk factors can lead to more accurate CVD/stroke risk stratification; (ii) the ability to handle multiple sources of information in big data framework using artificial intelligence-based paradigms (such as ML and DL) is likely to be the future in preventive CVD/stroke risk assessment.

Effect of carotid image-based phenotypes on cardiovascular risk calculator: AECRS1.0.

Today, the 10-year cardiovascular risk largely relies on conventional cardiovascular risk factors (CCVRFs) and suffers from the effect of atherosclerotic wall changes. In this study, we present a novel risk calculator AtheroEdge Composite Risk Score (AECRS1.0), designed by fusing CCVRF with ultrasound image-based phenotypes. Ten-year risk was computed using the Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study 56 (UKPDS56), UKPDS60, Reynolds Risk Score (RRS), and pooled composite risk (PCR) score. AECRS1.0 was computed by measuring the 10-year five carotid phenotypes such as IMT (ave., max., min.), IMT variability, and total plaque area (TPA) by fusing eight CCVRFs and then compositing them. AECRS1.0 was then benchmarked against the five conventional cardiovascular risk calculators by computing the receiver operating characteristics (ROC) and area under curve (AUC) values with a 95% CI. Two hundred four IRB-approved Japanese patients' left/right common carotid arteries (407 ultrasound scans) were collected with a mean age of 69 ± 11 years. The calculators gave the following AUC: FRS, 0.615; UKPDS56, 0.576; UKPDS60, 0.580; RRS, 0.590; PCRS, 0.613; and AECRS1.0, 0.990. When fusing CCVRF, TPA reported the highest AUC of 0.81. The patients were risk-stratified into low, moderate, and high risk using the standardized thresholds. The AECRS1.0 demonstrated the best performance on a Japanese diabetes cohort when compared with five conventional calculators. Graphical abstract AECRS1.0: Carotid ultrasound image phenotype-based 10-year cardiovascular risk calculator. The figure provides brief overview of the proposed carotid image phenotype-based 10-year cardiovascular risk calculator called AECRS1.0. AECRS1.0 was also benchmarked against five conventional cardiovascular risk calculators (Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study 56 (UKPDS56), UKPDS60, Reynolds Risk Score (RRS), and pooled composite risk (PCR) score).

Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: Logistic regression approach.

PURPOSE: Conventional cardiovascular risk factors (CCVRFs) and carotid ultrasound image-based phenotypes (CUSIP) are independently associated with long-term risk of cardiovascular (CV) disease. In this study, 26 cardiovascular risk (CVR) factors which consisted of a combination of CCVRFs and CUSIP together were ranked. Further, an optimal risk calculator using AtheroEdge composite risk score (AECRS1.0) was designed and benchmarked against seven conventional CV risk (CVR) calculators. METHODS: Two types of ranking were performed: (i) ranking of 26 CVR factors and (ii) ranking of eight types of 10-year risk calculators. In the first case, multivariate logistic regression was used to compute the odds ratio (OR) and in the second, receiver operating characteristic curves were used to evaluate the performance of eight types of CVR calculators using SPSS23.0 and MEDCALC12.0 with validation against STATA15.0. RESULTS: The left and right common carotid arteries (CCA) of 202 Japanese patients were examined to obtain 404 ultrasound scans. CUSIP ranked in the top 50% of the 26 covariates. Intima-media thickness variability (IMTV) and IMTV10yr were the most influential carotid phenotypes for left CCA (OR = 250, P < 0.0001 and OR = 207, P < 0.0001 respectively) and right CCA (OR = 1614, P < 0.0001 and OR = 626, P < 0.0001 respectively). However, for the mean CCA, AECRS1.0 and AECRS1.010yr reported the most highly significant OR among all the CVR factors (OR = 1.073, P < 0.0001 and OR = 1.104, P < 0.0001). AECRS1.010yr also reported highest area-under-the-curve (AUC = 0.904, P < 0.0001) compared to seven types of conventional calculators. Age and glycated haemoglobin reported highest OR (1.96, P < 0.0001 and 1.05, P = 0.012) among all other CCVRFs. CONCLUSION: AECRS1.010yr demonstrated the best performance due to presence of CUSIP and ranked at the first place with highest AUC.

Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study.

MOTIVATION: AtheroEdge Composite Risk Score (AECRS1.010yr) is an integrated stroke/cardiovascular risk calculator that was recently developed and computes the 10-year risk of carotid image phenotypes by integrating conventional cardiovascular risk factors (CCVRFs). It is therefore important to understand how closely AECRS1.010yr is associated with the ten other currently available conventional cardiovascular risk calculators (CCVRCs). METHODS: The Institutional Review Board of Toho University approved the examination of the left/right common carotid arteries of 202 Japanese patients. Step 1 consists of measurement of AECRS1.010yr, given current image phenotypes and CCVRFs. Step 2 consists of computing the risk score using ten different CCVRCs given CCVR factors: QRISK3, Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study (UKPDS) 56, UKPDS60, Reynolds Risk Score (RRS), Pooled cohort Risk Score (PCRS or ASCVD), Systematic Coronary Risk Evaluation (SCORE), Prospective Cardiovascular Munster Study (PROCAM) calculator, NIPPON, and World Health Organization (WHO) risk. Step 3 consists of computing the closeness factor between AECRS1.010yr and ten CCVRCs using cumulative ranking index derived using eight different statistically derived metrics. RESULTS: AECRS1.010yr reported the highest area-under-the-curve (0.927;P < 0.001) among all the risk calculators. The top three CCVRCs closest to AECRS1.010yr were QRISK3, FRS, and UKPDS60 with cumulative ranking scores of 2.1, 3.0, and 3.8, respectively. CONCLUSION: AECRS1.010yr produced the largest AUC due to the integration of image-based phenotypes with CCVR factors, and ranked at first place with the highest AUC. Cumulative ranking of ten CCVRCs demonstrated that QRISK3 was the closest calculator to AECRS1.010yr, which is also consistent with the industry trend.

Nonlinear model for the carotid artery disease 10-year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study.

MOTIVATION: This study presents a novel nonlinear model which can predict 10-year carotid ultrasound image-based phenotypes by fusing nine traditional cardiovascular risk factors (ethnicity, gender, age, artery type, body mass index, hemoglobin A1c, hypertension, low-density lipoprotein, and smoking) with five types of carotid automated image phenotypes (three types of carotid intima-media thickness (IMT), wall variability, and total plaque area). METHODOLOGY: Two-step process was adapted: First, five baseline carotid image-based phenotypes were automatically measured using AtheroEdge™ (AtheroPoint™ , CA, USA) system by two operators (novice and experienced) and an expert. Second, based on the annual progression rates of cIMT due to nine traditional cardiovascular risk factors, a novel nonlinear model was adapted for 10-year predictions of carotid phenotypes. RESULTS: Institute review board (IRB) approved 204 Japanese patients' left/right common carotid artery (407 ultrasound scans) was collected with a mean age of 69 ± 11 years. Age and hemoglobin were reported to have a high influence on the 10-year carotid phenotypes. Mean correlation coefficient (CC) between 10-year carotid image-based phenotype and age was improved by 39.35% in males and 25.38% in females. The area under the curves for the 10-year measurements of five phenotypes IMTave10yr , IMTmax10yr , IMTmin10yr , IMTV10yr , and TPA10yr were 0.96, 0.94, 0.90, 1.0, and 1.0. Inter-operator variability between two operators showed significant CC (P < 0.0001). CONCLUSIONS: A nonlinear model was developed and validated by fusing nine conventional CV risk factors with current carotid image-based phenotypes for predicting the 10-year carotid ultrasound image-based phenotypes which may be used risk assessment.

Intra- and inter-operator reproducibility of automated cloud-based carotid lumen diameter ultrasound measurement.

BACKGROUND: Common carotid artery lumen diameter (LD) ultrasound measurement systems are either manual or semi-automated and lack reproducibility and variability studies. This pilot study presents an automated and cloud-based LD measurements software system (AtheroCloud) and evaluates its: (i) intra/inter-operator reproducibility and (ii) intra/inter-observer variability. METHODS: 100 patients (83M, mean age: 68±11years), IRB approved, consisted of L/R CCA artery (200 ultrasound images), acquired using a 7.5-MHz linear transducer. The intra/inter-operator reproducibility was verified using three operator's readings. Near-wall and far carotid wall borders were manually traced by two observers for intra/inter-observer variability analysis. RESULTS: The mean coefficient of correlation (CC) for intra- and inter-operator reproducibility between all the three automated reading pairs were: 0.99 (P<0.0001) and 0.97 (P<0.0001), respectively. The mean CC for intra- and inter-observer variability between both the manual reading pairs were 0.98 (P<0.0001) and 0.98 (P<0.0001), respectively. The Figure-of-Merit between the mean of the three automated readings against the four manuals were 98.32%, 99.50%, 98.94% and 98.49%, respectively. CONCLUSIONS: The AtheroCloud LD measurement system showed high intra/inter-operator reproducibility hence can be adapted for vascular screening mode or pharmaceutical clinical trial mode.

State-of-the-art review on automated lumen and adventitial border delineation and its measurements in carotid ultrasound.

BACKGROUND AND OBJECTIVE: Accurate, reliable, efficient, and precise measurements of the lumen geometry of the common carotid artery (CCA) are important for (a) managing the progression/regression of atherosclerotic build-up and (b) the risk of stroke. The image-based degree of stenosis in the carotid artery and the plaque burden can be predicted using the automated carotid lumen diameter (LD)/inter-adventitial diameter (IAD) measurements from B-mode ultrasound images. The objective of this review is to present the state-of-the-art methods and systems for the measurement of LD/IAD in CCA based on automated or semi-automated strategies. Further, the performance of these systems is compared based on various metrics for its measurements. METHODS: The automated algorithms proposed for the segmentation of carotid lumen are broadly classified into two different categories as: region-based and boundary-based. These techniques are discussed in detail specifying their pros and cons. Further, we discuss the challenges encountered in the segmentation process along with its quantitative assessment. Lastly, we present stenosis quantification and risk stratification strategies. RESULTS: Even though, we have found more boundary-based approaches compared to region-based approaches in the literature, however, the region-based strategy yield more satisfactory performance. Novel risk stratification strategies are presented. On a patient database containing 203 patients, 9 patients are identified as high risk patients, whereas 27 patients are identified as medium risk patients. CONCLUSIONS: We have presented different techniques for the lumen segmentation of the common carotid artery from B-mode ultrasound images and measurement of lumen diameter and inter-adventitial diameter. We believe that the issue regarding boundary-based techniques can be compensated by taking regional statistics embedded with boundary-based information.

Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in diabetes cohort.

BACKGROUND: This study examines the association between six types of carotid artery disease image-based phenotypes and HbA1c in diabetes patients. Six phenotypes (intima-media thickness measurements (cIMT (ave.), cIMT (max.), cIMT (min.)), bidirectional wall variability (cIMTV), morphology-based total plaque area (mTPA), and composite risk score (CRS)) were measured in an automated setting using AtheroEdge™ (AtheroPoint, CA, USA). METHOD: Consecutive 199 patients (157 M, age: 68.96 ±â€¯10.98 years), L/R common carotid artery (CCA; 398 US scans) who underwent a carotid ultrasound (L/R) were retrospectively analyzed using AtheroEdge™ system. Two operators (novice and experienced) manually calibrated all the US scans using AtheroEdge™. Logistic regression (LR) and Odds ratio (OR) was computed and phenotypes were ranked. RESULTS: The baseline results showed 150 low-risk patients (HbA1c < 6.50 mg/dl) and 49 high-risk patients (HbA1c ≥ 6.50 mg/dl). The fasting blood sugar (FBS) was highly associated with HbA1c (P < 0.001). Except for cIMTV, all phenotypes showed an OR > 1.0 (P < 0.001) for left common carotid artery (LCCA), right carotid artery (RCCA), and mean of left and right common carotid artery (MCCA). After adjusting the FBS, the OR for mTPA showed a higher risk for LCCA, RCCA, and MCCA. The coefficient of correlation (CC) between phenotypes and HbA1c were strong and inter-CC between cIMT and mTPA/CRS was above 0.9 (P < 0.001). The statistical tests showed that phenotypes were significantly associated with diabetes (P-value<0.0001). CONCLUSIONS: All phenotypes using AtheroEdge™, except cIMTV, showed a strong association with HbA1c. mTPA and CRS were equally strong phenotypes as cIMT. The CRS phenotype showed the strongest relationship to HbA1c.

Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review.

PURPOSE OF REVIEW: Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple components of plaque buildup in the arterial walls. The American College of Cardiology/American Heart Association guidelines point to the importance of calcium in the coronary and carotid arteries and further recommend its quantification for the prevention of heart disease. It is therefore essential to stratify the CVD risk of the patient into low- and high-risk bins. RECENT FINDING: Calcium formation in the artery walls is multifocal in nature with sizes at the micrometer level. Thus, its detection requires high-resolution imaging. Clinical experience has shown that even though optical coherence tomography offers better resolution, intravascular ultrasound still remains an important imaging modality for coronary wall imaging. For a computer-based analysis system to be complete, it must be scientifically and clinically validated. This study presents a state-of-the-art review (condensation of 152 publications after examining 200 articles) covering the methods for calcium detection and its quantification for coronary and carotid arteries, the pros and cons of these methods, and the risk stratification strategies. The review also presents different kinds of statistical models and gold standard solutions for the evaluation of software systems useful for calcium detection and quantification. Finally, the review concludes with a possible vision for designing the next-generation system for better clinical outcomes.

Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients.

AIM: The study investigated the association of carotid ultrasound echolucent plaque-based biomarker with HbA1c, measured as age-adjusted grayscale median (AAGSM) as a function of chronological age, total plaque area, and conventional grayscale median (GSMconv). METHODS: Two stages were developed: (a) automated measurement of carotid parameters such as total plaque area (TPA); (b) computing the AAGSM as a function of GSMconv, age, and TPA. Intra-operator (novice and experienced) analysis was conducted. RESULTS: IRB approved, 204 patients' left/right CCA (408 images) ultrasound scans were collected: mean age: 69 ±â€¯11 years; mean HbA1c: 6.12 ±â€¯1.47%. A moderate inverse correlation was observed between AAGSM and HbA1c (CC of -0.13, P = 0.01), compared to GSM (CC of -0.06, P = 0.24). The RCCA and LCCA showed CC of -0.18, P < 0.01 and -0.08; P < 0.24. Female and males showed CC of -0.29, P < 0.01 and -0.10, P = 0.09. Using the threshold for AAGSM and HbA1c as: low-risk (AAGSM > 100; HbA1c < 5.7%), moderate-risk (40 < AAGSM < 100; 5.7% < HbA1c < 6.5%) and high-risk (AAGSM < 40; HbA1c > 6.5%), the area under the curve showed a better performance of AAGSM over GSMconv. A paired t-test between operators and expert (P < 0.0001); inter-operator CC of 0.85 (P < 0.0001). CONCLUSIONS: Echolucent plaque in patients with diabetes can be more accurately characterized for risk stratification using AAGSM compared to GSMconv.

Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort.

MOTIVATION: The carotid intima-media thickness (cIMT) is an important biomarker for cardiovascular diseases and stroke monitoring. This study presents an intelligence-based, novel, robust, and clinically-strong strategy that uses a combination of deep-learning (DL) and machine-learning (ML) paradigms. METHODOLOGY: A two-stage DL-based system (a class of AtheroEdge™ systems) was proposed for cIMT measurements. Stage I consisted of a convolution layer-based encoder for feature extraction and a fully convolutional network-based decoder for image segmentation. This stage generated the raw inner lumen borders and raw outer interadventitial borders. To smooth these borders, the DL system used a cascaded stage II that consisted of ML-based regression. The final outputs were the far wall lumen-intima (LI) and media-adventitia (MA) borders which were used for cIMT measurements. There were two sets of gold standards during the DL design, therefore two sets of DL systems (DL1 and DL2) were derived. RESULTS: A total of 396 B-mode ultrasound images of the right and left common carotid artery were used from 203 patients (Institutional Review Board approved, Toho University, Japan). For the test set, the cIMT error for the DL1 and DL2 systems with respect to the gold standard was 0.126 ±â€¯0.134 and 0.124 ±â€¯0.100 mm, respectively. The corresponding LI error for the DL1 and DL2 systems was 0.077 ±â€¯0.057 and 0.077 ±â€¯0.049 mm, respectively, while the corresponding MA error for DL1 and DL2 was 0.113 ±â€¯0.105 and 0.109 ±â€¯0.088 mm, respectively. The results showed up to 20% improvement in cIMT readings for the DL system compared to the sonographer's readings. Four statistical tests were conducted to evaluate reliability, stability, and statistical significance. CONCLUSION: The results showed that the performance of the DL-based approach was superior to the nonintelligence-based conventional methods that use spatial intensities alone. The DL system can be used for stroke risk assessment during routine or clinical trial modes.

Web-based accurate measurements of carotid lumen diameter and stenosis severity: An ultrasound-based clinical tool for stroke risk assessment during multicenter clinical trials.

BACKGROUND: This pilot study presents a completely automated, novel, smart, cloud-based, point-of-care system for (a) carotid lumen diameter (LD); (b) stenosis severity index (SSI) and (c) total lumen area (TLA) measurement using B-mode ultrasound. The proposed system was (i) validated against manual reading taken by the Neurologist and (ii) benchmarked against the commercially available system. METHOD: One hundred patients (73 M/27 F, mean age: 68 ± 11 years), institutional review board approved, written informed consent, consisted of left/right common carotid artery (200 ultrasound scans) were acquired using a 7.5-MHz linear transducer. RESULTS: The measured mean LD for left and right carotids were (in mm): (i) for proposed system (6.49 ± 1.77, 6.66 ± 1.70); and (ii) for manual (6.29 ± 1.79, 6.45 ± 1.63), respectively and coefficient of correlation between cloud-based automated against manual were 0.98 (P < 0.0001) and 0.99 (P < 0.0001), respectively. The corresponding TLA error, Precision-of-Merit, and Figure-of-Merit when measured against the manual were: 4.56 ± 3.54%, 96.18 ± 3.21%, and 96.85%, respectively. The AUC for the receiving operating characteristics for the cloud-based system was: 1.0. Four statistical tests such as: Two-tailed z-test, Mann-Whitney test, Kolmogorov-Smirnov (KS) and one-way ANOVA were performed to demonstrate consistency and reliability. CONCLUSIONS: The proposed system is reliable, accurate, fast, completely automated, anytime-anywhere solution for multi-center clinical trials and routine vascular screening.

Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm.

BACKGROUND: Planning of percutaneous interventional procedures involves a pre-screening and risk stratification of the coronary artery disease. Current screening tools use stand-alone plaque texture-based features and therefore lack the ability to stratify the risk. METHOD: This IRB approved study presents a novel strategy for coronary artery disease risk stratification using an amalgamation of IVUS plaque texture-based and wall-based measurement features. Due to common genetic plaque makeup, carotid plaque burden was chosen as a gold standard for risk labels during training-phase of machine learning (ML) paradigm. Cross-validation protocol was adopted to compute the accuracy of the ML framework. A set of 59 plaque texture-based features was padded with six wall-based measurement features to show the improvement in stratification accuracy. The ML system was executed using principle component analysis-based framework for dimensionality reduction and uses support vector machine classifier for training and testing-phases. RESULTS: The ML system produced a stratification accuracy of 91.28%, demonstrating an improvement of 5.69% when wall-based measurement features were combined with plaque texture-based features. The fused system showed an improvement in mean sensitivity, specificity, positive predictive value, and area under the curve by: 6.39%, 4.59%, 3.31% and 5.48%, respectively when compared to the stand-alone system. While meeting the stability criteria of 5%, the ML system also showed a high average feature retaining power and mean reliability of 89.32% and 98.24%, respectively. CONCLUSIONS: The ML system showed an improvement in risk stratification accuracy when the wall-based measurement features were fused with the plaque texture-based features.

Paget-Schroetter Syndrome Resulting from Thoracic Outlet Syndrome and KAATSU Training.

A 29-year-old woman who worked as a KAATSU (a type of body exercise that involves blood flow restriction) instructor visited our emergency room with a chief complaint of swelling and left upper limb pain. Chest computed tomography (CT) showed non-uniform contrast images corresponding to the site from the left axillary vein to the left subclavian vein; vascular ultrasonography of the upper limb revealed a thrombotic obstruction at the same site, leading to a diagnosis of Paget-Schroetter syndrome (PSS). We herein report our experience with a case of PSS derived from thoracic outlet syndrome (TOS), in a patient who was a KAATSU instructor.

Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm.

Severe atherosclerosis disease in carotid arteries causes stenosis which in turn leads to stroke. Machine learning systems have been previously developed for plaque wall risk assessment using morphology-based characterization. The fundamental assumption in such systems is the extraction of the grayscale features of the plaque region. Even though these systems have the ability to perform risk stratification, they lack the ability to achieve higher performance due their inability to select and retain dominant features. This paper introduces a polling-based principal component analysis (PCA) strategy embedded in the machine learning framework to select and retain dominant features, resulting in superior performance. This leads to more stability and reliability. The automated system uses offline image data along with the ground truth labels to generate the parameters, which are then used to transform the online grayscale features to predict the risk of stroke. A set of sixteen grayscale plaque features is computed. Utilizing the cross-validation protocol (K = 10), and the PCA cutoff of 0.995, the machine learning system is able to achieve an accuracy of 98.55 and 98.83%corresponding to the carotidfar wall and near wall plaques, respectively. The corresponding reliability of the system was 94.56 and 95.63%, respectively. The automated system was validated against the manual risk assessment system and the precision of merit for same cross-validation settings and PCA cutoffs are 98.28 and 93.92%for the far and the near wall, respectively.PCA-embedded morphology-based plaque characterization shows a powerful strategy for risk assessment and can be adapted in clinical settings.

Well-balanced system for coronary calcium detection and volume measurement in a low resolution intravascular ultrasound videos.

BACKGROUND: Accurate and fast quantitative assessment of calcium volume is required during the planning of percutaneous coronary interventions procedures. Low resolution in intravascular ultrasound (IVUS) coronary videos poses a threat to calcium detection causing over-estimation in volume measurement. We introduce a correction block that counter-balances the bias introduced during the calcium detection process. METHOD: Nineteen patients image dataset (around 40,090 frames), IRB approved, were collected using 40MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5mm/sec). A new set of 20 generalized and well-balanced systems each consisting of three stages: (i) calcium detection, (ii) calibration and (iii) measurement, while ensuring accuracy of four soft classifiers (Threshold, FCM, K-means and HMRF) and workflow speed using five multiresolution techniques (bilinear, bicubic, wavelet, Lanczos, Gaussian Pyramid) were designed. Results of the three calcium detection methods were benchmarked against the Threshold-based method. RESULTS: All 20 well-balanced systems with calibration block show superior performance. Using calibration block, FCM versus Threshold-based method shows the highest cross-correlation 0.99 (P<0.0001), Jaccard index 0.984±0.013 (P<0.0001), and Dice similarity 0.992±0.007 (P<0.0001). The corresponding area under the curve for four calcium detection techniques is: 1.0, 1.0, 0.97 and 0.93, respectively. The mean overall performance improvement is 38.54% and when adapting calibration block. The mean workflow speed improvement is 62.14% when adapting multiresolution paradigm. Three clinical tests shows consistency, reliability, and stability of our well-balanced system. CONCLUSIONS: A well-balanced system with a combination of Threshold embedded with Lanczos multiresolution was optimal and can be useable in clinical settings.

Automated segmental-IMT measurement in thin/thick plaque with bulb presence in carotid ultrasound from multiple scanners: Stroke risk assessment.

BACKGROUND AND OBJECTIVES: Standardization of the carotid IMT requires a reference marker in ultrasound scans. It has been shown previously that manual reference marker and manually created carotid segments are used for measuring IMT in these segments. Manual methods are tedious, time consuming, subjective, and prone to errors. Bulb edge can be considered as a reference marker for measurements of the cIMT. However, bulb edge can be difficult to locate in ultrasound scans due to: (a) low signal to noise ratio in the bulb region as compared to common carotid artery region; (b) uncertainty of bulb location in craniocaudal direction; and (c) variability in carotid bulb shape and size. This paper presents an automated system (a class of AtheroEdge™ system from AtheroPoint™, Roseville, CA, USA) for locating the bulb edge as a reference marker and further develop segmental-IMT (sIMT) which measures IMT in 10mm segments (namely: s1, s2 and s3) proximal to the bulb edge. METHODS: The patented methodology uses an integrated approach which combines carotid geometry and pixel-classification paradigms. The system first finds the bulb edge and then measures the sIMT proximal to the bulb edge. The system also estimates IMT in bulb region (bIMT). The 649 image database consists of varying plaque (light, moderate to heavy), image resolutions, shapes, sizes and ethnicity. RESULTS: Our results show that the IMT contributions in different carotid segments are as follows: bulb-IMT 34%, s1-IMT 29.46%, s2-IMT 11.48%, and s3-IMT 12.75%, respectively. We compare our automated results against reader's tracings demonstrating the following performance: mean lumen-intima error: 0.01235 ± 0.01224mm, mean media-adventitia error: 0.020933 ± 0.01539mm and mean IMT error: 0.01063 ± 0.0031mm. Our system's Precision of Merit is: 98.23%, coefficient of correlation between automated and Reader's IMT is: 0.998 (p-value < 0.0001). These numbers are improved compared to previous publications by Suri's group which is automated multi-resolution conventional cIMT. CONCLUSIONS: Our fully automated bulb detection system reports 92.67% precision against ideal bulb edge locations as marked by the reader in the bulb transition zone.

A case of transient apical hypertrophy associated with coronary vasospasm.

A 49-year-old woman was admitted to our hospital because of repeated loss of consciousness. On arrival, she was in cardiopulmonary arrest associated with arrhythmia of Torsades de pointes, and recovered from it after cardiopulmonary resuscitation and defibrillation. The administration of a ß-blocker and amiodarone was initiated to prevent ventricular tachycardia. On day 2, coronary angiography revealed non-obstructive coronary artery, and left ventriculography (LVG) exhibited hypokinesis in the anterior and apical wall. On day 20, an acetylcholine provocation test revealed a multivessel vasospasm, and LVG showed "spade-shaped left ventricle" in end-diastole because of apical hypertrophy. Transthoracic echocardiography (TTE) also showed apical wall thickness. Subsequent apical wall thickness gradually decreased and returned to normal on day 51 as observed on the TTE. Thus, we report a case of transient apical hypertrophy associated with coronary vasospasm, which was demonstrated by both the TTE and LVG. .