AtheroPoint TM LLC

Roseville, CA, United States

AtheroPoint TM LLC

Roseville, CA, United States
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Ikeda N.,Toho University | Dey N.,Global Biomedical Technologies Inc. | Sharma A.,University of Virginia | Gupta A.,Cornell University | And 11 more authors.
Computer Methods and Programs in Biomedicine | Year: 2017

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 10 mm 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 ± 0.01224 mm, mean media-adventitia error: 0.020933 ± 0.01539 mm and mean IMT error: 0.01063 ± 0.0031 mm. 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. © 2017 Elsevier Ireland Ltd


Than J.C.M.,University of Technology Malaysia | Saba L.,University of Cagliari | Noor N.M.,University of Technology Malaysia | Rijal O.M.,University of Malaya | And 7 more authors.
Computers in Biology and Medicine | Year: 2017

Lung disease risk stratification is important for both diagnosis and treatment planning, particularly in biopsies and radiation therapy. Manual lung disease risk stratification is challenging because of: (a) large lung data sizes, (b) inter- and intra-observer variability of the lung delineation and (c) lack of feature amalgamation during machine learning paradigm. This paper presents a two stage CADx cascaded system consisting of: (a) semi-automated lung delineation subsystem (LDS) for lung region extraction in CT slices followed by (b) morphology-based lung tissue characterization, thereby addressing the above shortcomings. LDS primarily uses entropy-based region extraction while ML-based lung characterization is mainly based on an amalgamation of directional transforms such as Riesz and Gabor along with texture-based features comprising of 100 greyscale features using the K-fold cross-validation protocol (K = 2, 3, 5 and 10). The lung database consisted of 96 patients: 15 normal and 81 diseased. We use five high resolution Computed Tomography (HRCT) levels representing different anatomy landmarks where disease is commonly seen. We demonstrate the amalgamated ML stratification accuracy of 99.53%, an increase of 2% against the conventional non-amalgamation ML system that uses alone Riesz-based feature embedded with feature selection based on feature strength. The robustness of the system was determined based on the reliability and stability that showed a reliability index of 0.99 and the deviation in risk stratification accuracies less than 5%. Our CADx system shows 10% better performance when compared against the mean of five other prominent studies available in the current literature covering over one decade. © 2017


Rajendra Acharya U.,Ngee Ann Polytechnic | Vinitha Sree S.,CA Technologies | Kulshreshtha S.,CA Technologies | Molinari F.,Polytechnic University of Turin | And 6 more authors.
Technology in Cancer Research and Treatment | Year: 2014

Ovarian cancer is the fifth highest cause of cancer in women and the leading cause of death from gynecological cancers. Accurate diagnosis of ovarian cancer from acquired images is dependent on the expertise and experience of ultrasonographers or physicians, and is therefore, associated with inter observer variabilities. Computer Aided Diagnostic (CAD) techniques use a number of different data mining techniques to automatically predict the presence or absence of cancer, and therefore, are more reliable and accurate. A review of published literature in the field of CAD based ovarian cancer detection indicates that many studies use ultrasound images as the base for analysis. The key objective of this work is to propose an effective adjunct CAD technique called GyneScan for ovarian tumor detection in ultrasound images. In our proposed data mining framework, we extract several texture features based on first order statistics, Gray Level Co-occurrence Matrix and run length matrix. The significant features selected using t-test are then used to train and test several supervised learning based classifiers such as Probabilistic Neural Networks (PNN), Support Vector Machine (SVM), Decision Tree (DT), κ-Nearest Neighbor (KNN), and Naïve Bayes (NB). We evaluated the developed framework using 1300 benign and 1300 malignant images. Using 11 significant features in KNN/PNN classifiers, we were able to achieve 100% classification accuracy, sensitivity, specificity, and positive predictive value in detecting ovarian tumor. Even though more validation using larger databases would better establish the robustness of our technique, the preliminary results are promising. This technique could be used as a reliable adjunct method to existing imaging modalities to provide a more confident second opinion on the presence/absence of ovarian tumor. © Adenine Press (2014).


Acharya U.R.,Ngee Ann Polytechnic | Acharya U.R.,University of Malaya | Mookiah M.R.K.,Ngee Ann Polytechnic | Vinitha Sree S.,Global Biomedical Technologies Inc. | And 10 more authors.
Medical and Biological Engineering and Computing | Year: 2013

In the case of carotid atherosclerosis, to avoid unnecessary surgeries in asymptomatic patients, it is necessary to develop a technique to effectively differentiate symptomatic and asymptomatic plaques. In this paper, we have presented a data mining framework that characterizes the textural differences in these two classes using several grayscale features based on a novel combination of trace transform and fuzzy texture. The features extracted from the delineated plaque regions in B-mode ultrasound images were used to train several classifiers in order to prepare them for classification of new test plaques. Our CAD system was evaluated using two different databases consisting of 146 (44 symptomatic to 102 asymptomatic) and 346 (196 symptomatic and 150 asymptomatic) images. Both these databases differ in the way the ground truth was determined. We obtained classification accuracies of 93.1 and 85.3 %, respectively. The techniques are low cost, easily implementable, objective, and non-invasive. For more objective analysis, we have also developed novel integrated indices using a combination of significant features. © 2013 International Federation for Medical and Biological Engineering.


Saba L.,University of Cagliari | Than J.C.M.,University of Technology Malaysia | Noor N.M.,University of Technology Malaysia | Rijal O.M.,University of Malaya | And 6 more authors.
Journal of Medical Systems | Year: 2016

Human interaction has become almost mandatory for an automated medical system wishing to be accepted by clinical regulatory agencies such as Food and Drug Administration. Since this interaction causes variability in the gathered data, the inter-observer and intra-observer variability must be analyzed in order to validate the accuracy of the system. This study focuses on the variability from different observers that interact with an automated lung delineation system that relies on human interaction in the form of delineation of the lung borders. The database consists of High Resolution Computed Tomography (HRCT): 15 normal and 81 diseased patients’ images taken retrospectively at five levels per patient. Three observers manually delineated the lungs borders independently and using software called ImgTracer™ (AtheroPoint™, Roseville, CA, USA) to delineate the lung boundaries in all five levels of 3-D lung volume. The three observers consisted of Observer-1: lesser experienced novice tracer who is a resident in radiology under the guidance of radiologist, whereas Observer-2 and Observer-3 are lung image scientists trained by lung radiologist and biomedical imaging scientist and experts. The inter-observer variability can be shown by comparing each observer’s tracings to the automated delineation and also by comparing each manual tracing of the observers with one another. The normality of the tracings was tested using D’Agostino-Pearson test and all observers tracings showed a normal P-value higher than 0.05. The analysis of variance (ANOVA) test between three observers and automated showed a P-value higher than 0.89 and 0.81 for the right lung (RL) and left lung (LL), respectively. The performance of the automated system was evaluated using Dice Similarity Coefficient (DSC), Jaccard Index (JI) and Hausdorff (HD) Distance measures. Although, Observer-1 has lesser experience compared to Obsever-2 and Obsever-3, the Observer Deterioration Factor (ODF) shows that Observer-1 has less than 10 % difference compared to the other two, which is under acceptable range as per our analysis. To compare between observers, this study used regression plots, Bland-Altman plots, two tailed T-test, Mann-Whiney, Chi-Squared tests which showed the following P-values for RL and LL: (i) Observer-1 and Observer-3 were: 0.55, 0.48, 0.29 for RL and 0.55, 0.59, 0.29 for LL; (ii) Observer-1 and Observer-2 were: 0.57, 0.50, 0.29 for RL and 0.54, 0.59, 0.29 for LL; (iii) Observer-2 and Observer-3 were: 0.98, 0.99, 0.29 for RL and 0.99, 0.99, 0.29 for LL. Further, CC and R-squared coefficients were computed between observers which came out to be 0.9 for RL and LL. All three observers however manage to show the feature that diseased lungs are smaller than normal lungs in terms of area. © 2016, Springer Science+Business Media New York.


Rajendra Acharya U.,Ngee Ann Polytechnic | Rajendra Acharya U.,University of Malaya | Swapna G.,Government Engineering College | Vinitha Sree S.,Global Biomedical Technologies Inc. | And 7 more authors.
Technology in Cancer Research and Treatment | Year: 2014

In this paper, we review the different studies that developed Computer Aided Diagnostic (CAD) for automated classification of thyroid cancer into benign and malignant types. Specifically, we discuss the different types of features that are used to study and analyze the differences between benign and malignant thyroid nodules. These features can be broadly categorized into (a) the sonographic features from the ultrasound images, and (b) the nonclinical features extracted from the ultrasound images using statistical and data mining techniques. We also present a brief description of the commonly used classifiers in ultrasound based CAD systems. We then review the studies that used features based on the ultrasound images for thyroid nodule classification and highlight the limitations of such studies. We also discuss and review the techniques used in studies that used the non-clinical features for thyroid nodule classification and report the classification accuracies obtained in these studies. © Adenine Press (2014).


Lucatelli P.,University of Rome La Sapienza | Raz E.,New York University | Saba L.,Azienda Ospedaliero Universitaria | Argiolas G.M.,Azienda Ospedaliera Brotzu | And 8 more authors.
European Radiology | Year: 2016

Objective: To assess the relationship between the degree of leukoaraiosis (LA), carotid intima-media thickness (IMT) and intima-media thickness variability (IMTV). Materials and methods: Sixty-one consecutive patients, who underwent a brain MRI examination and a carotid artery ultrasound, were included in this retrospective study, which conformed with the Declaration of Helsinki. Written informed consent was waived. In each patient, right/left carotid arteries and brain hemispheres were assessed using automated software for IMT, IMTV and LA volume. Results: The mean hemispheric LA volume was 2,224 mm3 (SD 2,702 mm3) and there was no statistically significant difference in LA volume between the right and left hemispheres (p value = 0.628). The mean IMT and IMTV values were 0.866 mm (SD 0.170) and 0.143 mm (SD 0.100), respectively, without significant differences between the right and left sides (p values 0.733 and 0.098, respectively). The correlation coefficient between IMTV and LA volume was 0.41 (p value = 0.0001), and 0.246 (p value = 0.074) between IMT and LA volume. Conclusions: IMTV significantly correlates with LA volume. Further studies are warranted to verify whether this parameter can be used clinically as a marker of cerebrovascular risk. Key Points: • Intima-media thickness variability (IMTV) significantly correlates with leukoaraiosis volume.• IMTV could be used as a marker for cerebrovascular risk.• IMTV seems to be a better predictor of weighted mean difference than IMT. © 2016 European Society of Radiology


Saba L.,University of Cagliari | Ikeda N.,Toho University | Deidda M.,University of Cagliari | Araki T.,Toho University | And 10 more authors.
Diabetes Research and Clinical Practice | Year: 2013

Aims: The purpose of this study was to evaluate whether carotid IMT (cIMT) identified using automated software is associated with HbA1c in Japanese patients with coronary artery disease. Methods: 370 consecutive patients (males 218; median age 69 years. ±. 11) who underwent carotid-US and first coronary angiography were prospectively analyzed. After ultrasonographic examinations were performed, the plaque score (PS) was calculated and automated IMT analysis was obtained with a dedicated algorithm. Pearson correlation analysis was performed to calculate the association between automated IMT, PS and HbA1c. Results: The mean value of cIMT was 1.00. ±. 0.47. mm for the right carotid and 1.04. ±. 0.49. mm for the left carotid; the average bilateral value was 1.02. ±. 0.43. mm. No significant difference of cIMT was detected between men and women. We found a direct correlation between cIMT values and HbA1c (p= 0.0007) whereas the plaque score did not correlate with the HbA1c values (p>. 0.05). Conclusion: The results of our study confirm that automated cIMT values and levels of HbA1c in Japanese patients with coronary artery disease are correlated whereas the plaque score does not show a statistically significant correlation. © 2013 Elsevier Ireland Ltd.


Saba L.,Azienda Ospedaliero Universitaria | Argiolas G.M.,Azienda Ospedaliero Brotzu A.O.B. | Raz E.,New York University | Raz E.,University of Rome La Sapienza | And 8 more authors.
European Journal of Radiology | Year: 2014

Purpose The purpose of this work was to evaluate if the use of color maps, instead of conventional grayscale images, would improve the observer's diagnostic confidence in the non-contrast CT evaluation of internal carotid artery dissection (ICAD).Materials and methods One hundred patients (61 men, 39 women; mean age, 51 years; range, 25-78 years), 40 with and 60 without ICAD, underwent non-contrast CT and were included in this the retrospective study. In this study, three groups of patients were considered: patients with MR confirmation of ICAD, n = 40; patients with MR confirmation of ICAD absence, n = 20; patients who underwent CT of the carotid arteries because of atherosclerotic disease, n = 40. Four blinded observers with different levels of expertise (expert, intermediate A, intermediate B and trainee) analyzed the non-contrast CT datasets using a cross model (one case grayscale and the following case using the color scale). The presence of ICAD was scored on a 5-point scale in order to assess the observer's diagnostic confidence. After 3 months the four observers evaluated the same datasets by using the same cross-model for the alternate readings (one case color scale and the following case using the grayscale). Statistical analysis included receiver operating characteristics (ROC) curve analysis, the Cohen weighted test and sensitivity, specificity, PPV, NPV, accuracy, LR+ and LR-.Results The ROC curve analysis showed that, for all observers, the use of color scale resulted in an improved diagnostic confidence with AUC values increasing from 0.896 to 0.936, 0.823 to 0.849, 0.84 to 0.909 and 0.749 to 0.861 for expert, intermediate A, intermediate B and trainee observers, respectively. The increase in diagnostic confidence (between the AUC areas) was statistically significant (p = 0.036) for the trainee. Accuracy as well as sensitivity, specificity, PPV, NPV, LR+ and LR- were improved using the color scale.Conclusion Our study suggests that the use of a color scale instead the conventional grayscale improves the diagnostic confidence, accuracy and inter-observer agreement of the readers, in particular of junior ones, in the diagnosis of ICAD on non-contrast CT. © 2014 Elsevier Ireland Ltd. All rights reserved.


PubMed | SS Trinita Hospital, Ospedale Microcitemico, University of Rome Tor Vergata, Azienda Ospedaliero and AtheroPointTM LLC
Type: Journal Article | Journal: The British journal of radiology | Year: 2016

Vertebral fracture (VF) is a common condition with >160,000 patients affected every year in North America and most of them with affected lumbar vertebrae. The management of VF is well known and defined by many protocols related to associated clinical neurological symptoms, especially in case of the presence or absence of myelopathy or radicular deficit. In this article, we will explore the percutaneous stabilization of the lumbar spine by showing the newest approaches for this condition.

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