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Velasco V, Soucy P, Keynton R, Williams SJ. A microfluidic impedance platform for real-time, in vitro characterization of endothelial cells undergoing fluid shear stress. Lab Chip 2022; 22:4705-4716. [PMID: 36349980 DOI: 10.1039/d2lc00555g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
We introduce a microfluidic impedance platform to electrically monitor in real-time, endothelium monolayers undergoing fluid shear stress. Our platform incorporates sensing electrodes (SEs) that measure cell behavior and cell-free control electrodes that measure cell culture media resistance simultaneously but independently from SEs. We evaluated three different cellular subpopulations sizes through 50, 100, and 200 μm diameter SEs. We tested their utility in measuring the response of human umbilical vein endothelial cells (HUVECs) at static, constant (17.6 dyne per cm2), and stepped (23.7-35-58.1 dyne per cm2) shear stress conditions. For 14 hours, we collected the impedance spectra (100 Hz-1 MHz) of sheared cells. Using equivalent circuit models, we extracted monolayer permeability (RTER), cell membrane capacitance, and cell culture media resistance. Platform evaluation concluded that: (1) 50 μm SEs (∼2 cells) suffered interfacial capacitance and reduced cell measurement sensitivity, (2) 100 μm SEs (∼6 cells) was limited to measuring cell behavior only and cannot measure cell culture media resistance, and (3) 200 μm SEs (∼20 cells) detected cell behavior with accurate prediction of cell culture media resistance. Platform-based shear stress studies indicated a shear magnitude dependent increase in RTER at the onset of acute flow. Consecutive stepped shear conditions did not alter RTER in the same magnitude after shear has been applied. Finally, endpoint staining of VE-cadherin on the actual SEs and endpoint RTER measurements were greater for 23.7-35-58.1 dyne per cm2 than 17.6 dyne per cm2 shear conditions.
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Affiliation(s)
- Vanessa Velasco
- Stanford Genome Technology Center, Stanford University, 3165 Porter Drive, Palo Alto, CA 94304, USA
| | - Patricia Soucy
- Bioengineering Department, University of Louisville, 2301 S. Third St., Paul C. Lutz Hall, # 419, Louisville, KY 40292, USA
| | - Robert Keynton
- William States Lee College of Engineering, University of North Carolina, Charlotte, 28223, USA
| | - Stuart J Williams
- Mechanical Engineering Department, University of Louisville, 332 Eastern Pkwy, Sackett Hall, # 202A, Louisville, KY 40292, USA
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Elsaid N, Saied A, Kandil H, Soliman A, Taher F, Hadi M, Giridharan G, Jennings R, Casanova M, Keynton R, El-Baz A. Impact of stress and hypertension on the cerebrovasculature. Front Biosci (Landmark Ed) 2021; 26:1643-1652. [PMID: 34994178 DOI: 10.52586/5057] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 08/19/2021] [Accepted: 08/26/2021] [Indexed: 11/09/2022]
Abstract
OBJECTIVES Both stress and hypertension (HTN) are considered major health problems that negatively impact the cerebral vasculature. In this article we summarize the possible relationship between stress and HTN. METHODS We conducted a systematic review of the literature using a database search of MEDLINE, PubMed, Scopus, and Web of Science. RESULTS Psychological stress is known to be an important risk factor for essential hypertension. Acute stress can induce transient elevations of blood pressure in the context of the fight-or-flight response. With increased intensity and duration of a perceived harmful event, the normal physiological response is altered, resulting in a failure to return to the resting levels. These changes are responsible for the development of HTN. Genetic and behavioral factors are also very important for the pathogenesis of hypertension under chronic stress situation. In addition, HTN and chronic stress may lead to impaired auto-regulation, regional vascular remodeling, and breakdown of the blood brain barrier (BBB). The effects of both HTN and chronic stress on the cerebral blood vessels shows that both have common structural and functional effects including endothelial damage with subsequent increased wall thickness, vessel resistance, stiffness, arterial atherosclerosis, and altered hemodynamics. CONCLUSION Most of the above mentioned vascular effects of stress were primarily reported in animal models. Further in-vivo standardization of pathological vascular indices and imaging modalities is warranted. Radiological quantification of these cerebrovascular changes is therefore essential for in depth understanding of the healthy and diseased cerebral arteries functions, identification and stratification of patients at risk of cardiovascular and neurological adverse events, enactment of preventive measures prior to the onset of systemic HTN, and the initiation of personalized medical management.
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Affiliation(s)
- Nada Elsaid
- Faculty of Medicine, Neurology Department, Mansoura University, 35516 Mansoura, Egypt.,Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Saied
- Faculty of Medicine, Neurology Department, Mansoura University, 35516 Mansoura, Egypt.,Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Heba Kandil
- Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA.,Information Technology Department, Faculty of Computer Science and Information, Mansoura University, 35516 Mansoura, Egypt
| | - Ahmed Soliman
- Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Fatma Taher
- College of Technological Innovation, Zayed University, 4783 Dubai, United Arab Emirates
| | - Mohiuddin Hadi
- Department of Radiology, University of Louisville, Louisville, KY 40292, USA
| | - Guruprasad Giridharan
- Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Richard Jennings
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Manuel Casanova
- Department of Biomedical Sciences, University of South Carolina, Greenville, SC 29208, USA
| | - Robert Keynton
- William States Lee College of Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Ayman El-Baz
- Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
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Sleman AA, Soliman A, Elsharkawy M, Giridharan G, Ghazal M, Sandhu H, Schaal S, Keynton R, Elmaghraby A, El-Baz A. A novel 3D segmentation approach for extracting retinal layers from optical coherence tomography images. Med Phys 2021; 48:1584-1595. [PMID: 33450073 DOI: 10.1002/mp.14720] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 12/06/2020] [Accepted: 12/23/2020] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Accurate segmentation of retinal layers of the eye in 3D Optical Coherence Tomography (OCT) data provides relevant information for clinical diagnosis. This manuscript describes a 3D segmentation approach that uses an adaptive patient-specific retinal atlas, as well as an appearance model for 3D OCT data. METHODS To reconstruct the atlas of 3D retinal scan, the central area of the macula (macula mid-area) where the fovea could be clearly identified, was segmented initially. Markov Gibbs Random Field (MGRF) including intensity, spatial information, and shape of 12 retinal layers were used to segment the selected area of retinal fovea. A set of coregistered OCT scans that were gathered from 200 different individuals were used to build a 2D shape prior. This shape prior was adapted subsequently to the first order appearance and second order spatial interaction MGRF model. After segmenting the center of the macula "foveal area", the labels and appearances of the layers that were segmented were utilized to segment the adjacent slices. The final step was repeated recursively until a 3D OCT scan of the patient was segmented. RESULTS This approach was tested in 50 patients with normal and with ocular pathological conditions. The segmentation was compared to a manually segmented ground truth. The results were verified by clinical retinal experts. Dice Similarity Coefficient (DSC), 95% bidirectional modified Hausdorff Distance (HD), Unsigned Mean Surface Position Error (MSPE), and Average Volume Difference (AVD) metrics were used to quantify the performance of the proposed approach. The proposed approach was proved to be more accurate than the current state-of-the-art 3D OCT approaches. CONCLUSIONS The proposed approach has the advantage of segmenting all the 12 retinal layers rapidly and more accurately than current state-of-the-art 3D OCT approaches.
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Affiliation(s)
- Ahmed A Sleman
- Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA
| | - Ahmed Soliman
- Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA
| | - Mohamed Elsharkawy
- Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA
| | | | - Mohammed Ghazal
- Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, 59911, UAE
| | - Harpal Sandhu
- Department of Ophthalmology, School of Medicine, University of Louisville, Louisville, KY, 40208, USA
| | - Shlomit Schaal
- Ophthalmology and Visual Sciences Department, University of Massachusetts Medical School, Worcester, MA, 01655, USA
| | - Robert Keynton
- Department of Mechanical Engineering and Engineering Science, William States Lee College of Engineering, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA
| | - Adel Elmaghraby
- Computer Science and Computer Engineering Department, University of Louisville, Louisville, KY, 40208, USA
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA
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4
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Shehata M, Ghazal M, Khalifeh HA, Khalil A, Shalaby A, Dwyer AC, Bakr AM, Keynton R, El-Baz A. A DEEP LEARNING-BASED CAD SYSTEM FOR RENAL ALLOGRAFT ASSESSMENT: DIFFUSION, BOLD, AND CLINICAL BIOMARKERS. Proc Int Conf Image Proc 2020; 2020:355-359. [PMID: 34720753 PMCID: PMC8553095 DOI: 10.1109/icip40778.2020.9190818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Recently, studies for non-invasive renal transplant evaluation have been explored to control allograft rejection. In this paper, a computer-aided diagnostic system has been developed to accommodate with an early-stage renal transplant status assessment, called RT-CAD. Our model of this system integrated multiple sources for a more accurate diagnosis: two image-based sources and two clinical-based sources. The image-based sources included apparent diffusion coefficients (ADCs) and the amount of deoxygenated hemoglobin (R2*). More specifically, these ADCs were extracted from 47 diffusion weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, …, b1000 s/mm2), while the R2* values were extracted from 30 blood oxygen level-dependent MRI (BOLD-MRI) scans at 5 different echo times (2ms, 7ms, 12ms, 17ms, and 22ms). The clinical sources included serum creatinine (SCr) and creatinine clearance (CrCl). First, the kidney was segmented through the RT-CAD system using a geometric deformable model called a level-set method. Second, both ADCs and R2* were estimated for common patients (N = 30) and then were integrated with the corresponding SCr and CrCl. Last, these integrated biomarkers were considered the discriminatory features to be used as trainers and testers for future deep learning-based classifiers such as stacked auto-encoders (SAEs). We used a k-fold cross-validation criteria to evaluate the RT-CAD system diagnostic performance, which achieved the following scores: 93.3%, 90.0%, and 95.0% in terms of accuracy, sensitivity, and specificity in differentiating between acute renal rejection (AR) and non-rejection (NR). The reliability and completeness of the RT-CAD system was further accepted by the area under the curve score of 0.92. The conclusions ensured that the presented RT-CAD system has a high reliability to diagnose the status of the renal transplant in a non-invasive way.
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Affiliation(s)
- Mohamed Shehata
- BioImaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - Mohammed Ghazal
- Faculty of Engineering, Abu Dhabi University, Abu Dhabi, UAE
| | | | - Ashraf Khalil
- Faculty of Engineering, Abu Dhabi University, Abu Dhabi, UAE
| | - Ahmed Shalaby
- BioImaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - Amy C Dwyer
- Pediatric Nephrology Unit, Mansoura University Children's Hospital, University of Mansoura, Egypt
| | - Ashraf M Bakr
- Kidney Disease Program, University of Louisville, Louisville, KY, USA
| | - Robert Keynton
- BioImaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - Ayman El-Baz
- BioImaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, USA
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Sandhu HS, Elmogy M, Taher Sharafeldeen A, Elsharkawy M, El-Adawy N, Eltanboly A, Shalaby A, Keynton R, El-Baz A. Automated Diagnosis of Diabetic Retinopathy Using Clinical Biomarkers, Optical Coherence Tomography, and Optical Coherence Tomography Angiography. Am J Ophthalmol 2020; 216:201-206. [PMID: 31982407 DOI: 10.1016/j.ajo.2020.01.016] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 12/29/2019] [Accepted: 01/10/2020] [Indexed: 01/19/2023]
Abstract
PURPOSE To determine if combining clinical, demographic, and imaging data improves automated diagnosis of nonproliferative diabetic retinopathy (NPDR). DESIGN Cross-sectional imaging and machine learning study. METHODS This was a retrospective study performed at a single academic medical center in the United States. Inclusion criteria were age >18 years and a diagnosis of diabetes mellitus (DM). Exclusion criteria were non-DR retinal disease and inability to image the macula. Optical coherence tomography (OCT) and OCT angiography (OCTA) were performed, and data on age, sex, hypertension, hyperlipidemia, and hemoglobin A1c were collected. Machine learning techniques were then applied. Multiple pathophysiologically important features were automatically extracted from each layer on OCT and each OCTA plexus and combined with clinical data in a random forest classifier to develop the system, whose results were compared to the clinical grading of NPDR, the gold standard. RESULTS A total of 111 patients with DM II were included in the study, 36 with DM without DR, 53 with mild NPDR, and 22 with moderate NPDR. When OCT images alone were analyzed by the system, accuracy of diagnosis was 76%, sensitivity 85%, specificity 87%, and area under the curve (AUC) was 0.78. When OCT and OCTA data together were analyzed, accuracy was 92%, sensitivity 95%, specificity 98%, and AUC 0.92. When all data modalities were combined, the system achieved an accuracy of 96%, sensitivity 100%, specificity 94%, and AUC 0.96. CONCLUSIONS Combining common clinical data points with OCT and OCTA data enhances the power of computer-aided diagnosis of NPDR.
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Dekhil O, Ali M, Haweel R, Elnakib Y, Ghazal M, Hajjdiab H, Fraiwan L, Shalaby A, Soliman A, Mahmoud A, Keynton R, Casanova MF, Barnes G, El-Baz A. A Comprehensive Framework for Differentiating Autism Spectrum Disorder From Neurotypicals by Fusing Structural MRI and Resting State Functional MRI. Semin Pediatr Neurol 2020; 34:100805. [PMID: 32446442 DOI: 10.1016/j.spen.2020.100805] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Autism spectrum disorder is a neurodevelopmental disorder characterized by impaired social abilities and communication difficulties. The golden standard for autism diagnosis in research rely on behavioral features, for example, the autism diagnosis observation schedule, the Autism Diagnostic Interview-Revised. In this study we introduce a computer-aided diagnosis system that uses features from structural MRI (sMRI) and resting state functional MRI (fMRI) to help predict an autism diagnosis by clinicians. The proposed system is capable of parcellating brain regions to show which areas are most likely affected by autism related abnormalities and thus help in targeting potential therapeutic interventions. When tested on 18 data sets (n = 1060) from the ABIDE consortium, our system was able to achieve high accuracy (sMRI 0.75-1.00; fMRI 0.79-1.00), sensitivity (sMRI 0.73-1.00; fMRI 0.78-1.00), and specificity (sMRI 0.78-1.00; fMRI 0.79-1.00). The proposed system could be considered an important step toward helping physicians interpret results of neuroimaging studies and personalize treatment options. To the best of our knowledge, this work is the first to combine features from structural and functional MRI, use them for personalized diagnosis and achieve high accuracies on a relatively large population.
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Affiliation(s)
- Omar Dekhil
- Department of Bioengineering, University of Louisville, Louisville, KY
| | - Mohamed Ali
- Department of Bioengineering, University of Louisville, Louisville, KY
| | - Reem Haweel
- Department of Bioengineering, University of Louisville, Louisville, KY
| | - Yaser Elnakib
- Department of Bioengineering, University of Louisville, Louisville, KY
| | - Mohammed Ghazal
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Hassan Hajjdiab
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Luay Fraiwan
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Ahmed Shalaby
- Department of Bioengineering, University of Louisville, Louisville, KY
| | - Ahmed Soliman
- Department of Bioengineering, University of Louisville, Louisville, KY
| | - Ali Mahmoud
- Department of Bioengineering, University of Louisville, Louisville, KY
| | - Robert Keynton
- Department of Bioengineering, University of Louisville, Louisville, KY
| | - Manuel F Casanova
- Department of Biomedical Sciences, University of South Carolina, Greenville, SC
| | - Gregory Barnes
- Department of Neurology, University of Louisville, Louisville, KY
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY.
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7
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Shehata M, Shalaby A, Switala AE, El-Baz M, Ghazal M, Fraiwan L, Khalil A, El-Ghar MA, Badawy M, Bakr AM, Dwyer A, Elmaghraby A, Giridharan G, Keynton R, El-Baz A. A multimodal computer-aided diagnostic system for precise identification of renal allograft rejection: Preliminary results. Med Phys 2020; 47:2427-2440. [PMID: 32130734 DOI: 10.1002/mp.14109] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 02/18/2020] [Accepted: 02/18/2020] [Indexed: 12/26/2022] Open
Abstract
PURPOSE Early assessment of renal allograft function post-transplantation is crucial to minimize and control allograft rejection. Biopsy - the gold standard - is used only as a last resort due to its invasiveness, high cost, adverse events (e.g., bleeding, infection, etc.), and the time for reporting. To overcome these limitations, a renal computer-assisted diagnostic (Renal-CAD) system was developed to assess kidney transplant function. METHODS The developed Renal-CAD system integrates data collected from two image-based sources and two clinical-based sources to assess renal transplant function. The imaging sources were the apparent diffusion coefficients (ADCs) extracted from 47 diffusion-weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, ..., b1000 s/mm 2 ), and the transverse relaxation rate (R2*) extracted from 30 blood oxygen level-dependent MRI (BOLD-MRI) scans at 5 different echo times (TEs = 2, 7, 12, 17, and 22 ms). Serum creatinine (SCr) and creatinine clearance (CrCl) were the clinical sources for kidney function evaluation. The Renal-CAD system initially performed kidney segmentation using the level-set method, followed by estimation of the ADCs from DW-MRIs and the R2* from BOLD-MRIs. ADCs and R2* estimates from 30 subjects that have both types of scans were integrated with their associated SCr and CrCl. The integrated biomarkers were then used as our discriminatory features to train and test a deep learning-based classifier, namely stacked autoencoders (SAEs) to differentiate non-rejection (NR) from acute rejection (AR) renal transplants. RESULTS Using a leave-one-subject-out cross-validation approach along with SAEs, the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified tenfold cross-validation approach, the Renal-CAD system demonstrated its reproducibility and robustness by a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88. CONCLUSION The obtained results demonstrate the feasibility and efficacy of accurate, noninvasive identification of AR at an early stage using the Renal-CAD system.
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Affiliation(s)
- Mohamed Shehata
- BioImaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA
| | - Ahmed Shalaby
- BioImaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA
| | - Andrew E Switala
- BioImaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA
| | - Maryam El-Baz
- BioImaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA
| | - Mohammed Ghazal
- Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, 59911, UAE
| | - Luay Fraiwan
- Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, 59911, UAE
| | - Ashraf Khalil
- Computer Science and Information Technology Department, Abu Dhabi University, Abu Dhabi, 59911, UAE
| | - Mohamed Abou El-Ghar
- Urology and Nephrology Center, Radiology Department, Mansoura University, Mansoura, 35516, Egypt
| | - Mohamed Badawy
- Urology and Nephrology Center, Radiology Department, Mansoura University, Mansoura, 35516, Egypt
| | - Ashraf M Bakr
- Pediatric Nephrology Unit, Mansoura University Children's Hospital, University of Mansoura, Mansoura, 35516, Egypt
| | - Amy Dwyer
- Kidney Disease Program, University of Louisville, Louisville, KY, 40202, USA
| | - Adel Elmaghraby
- Computer Engineering and Computer Science Department, University of Louisville, Louisville, KY, 40208, USA
| | | | - Robert Keynton
- Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA.,200 E Shipp Ave, Lutz 390 Hall, Room 419, Louisville, KY, 40208, USA
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Abdeltawab H, Khalifa F, Taher F, Alghamdi NS, Ghazal M, Beache G, Mohamed T, Keynton R, El-Baz A. A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images. Comput Med Imaging Graph 2020; 81:101717. [PMID: 32222684 PMCID: PMC7232687 DOI: 10.1016/j.compmedimag.2020.101717] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 02/14/2020] [Accepted: 03/10/2020] [Indexed: 12/15/2022]
Abstract
Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and function as well as heart diagnosis. The estimation of physiological heart parameters for heart diagnosis essentially require accurate segmentation of the Left ventricle (LV) from cardiac MRI. Therefore, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aim to achieve lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Our framework starts by an accurate localization of the LV blood pool center-point using a fully convolutional neural network (FCN) architecture called FCN1. Then, a region of interest (ROI) that contains the LV is extracted from all heart sections. The extracted ROIs are used for the segmentation of LV cavity and myocardium via a novel FCN architecture called FCN2. The FCN2 network has several bottleneck layers and uses less memory footprint than conventional architectures such as U-net. Furthermore, a new loss function called radial loss that minimizes the distance between the predicted and true contours of the LV is introduced into our model. Following myocardial segmentation, functional and mass parameters of the LV are estimated. Automated Cardiac Diagnosis Challenge (ACDC-2017) dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. To sum up, we propose a deep learning approach that can be translated into a clinical tool for heart diagnosis.
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Affiliation(s)
- Hisham Abdeltawab
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Fahmi Khalifa
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Fatma Taher
- College of Technological Innovation, Zayed University, Dubai, United Arab Emirates
| | - Norah Saleh Alghamdi
- College of Computer and Information Science, Princess Nourah bint Abdulrahman University, Saudi Arabia
| | - Mohammed Ghazal
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Garth Beache
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Tamer Mohamed
- Institute of Molecular Cardiology, University of Louisville, Louisville, KY 40202, USA
| | - Robert Keynton
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA.
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9
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Kandil H, Soliman A, Taher F, Ghazal M, Khalil A, Giridharan G, Keynton R, Jennings JR, El-Baz A. A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations. Neuroimage Clin 2019; 25:102107. [PMID: 31830715 PMCID: PMC6926373 DOI: 10.1016/j.nicl.2019.102107] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 10/31/2019] [Accepted: 11/19/2019] [Indexed: 01/21/2023]
Abstract
3-D CNN segmentation succeeded in delineating cerebrovasculature accurately. Segmentation approach is automatic and applicable on healthy/pathological vessels. Blood flow variability challenge was addressed by processing MRA scans locally. Proposed vascular features were efficient to quantify cerebral changes. Proposed CAD system could help clinicians predict hypertension before its onset.
Hypertension is a leading cause of mortality in the USA. While simple tools such as the sphygmomanometer are widely used to diagnose hypertension, they could not predict the disease before its onset. Clinical studies suggest that alterations in the structure of human brains’ cerebrovasculature start to develop years before the onset of hypertension. In this research, we present a novel computer-aided diagnosis (CAD) system for the early detection of hypertension. The proposed CAD system analyzes magnetic resonance angiography (MRA) data of human brains to detect and track the cerebral vascular alterations and this is achieved using the following steps: i) MRA data are preprocessed to eliminate noise effects, correct the bias field effect, reduce the contrast inhomogeneity using the generalized Gauss-Markov random field (GGMRF) model, and normalize the MRA data, ii) the cerebral vascular tree of each MRA volume is segmented using a 3-D convolutional neural network (3D-CNN), iii) cerebral features in terms of diameters and tortuosity of blood vessels are estimated and used to construct feature vectors, iv) feature vectors are then used to train and test various artificial neural networks to classify data into two classes; normal and hypertensive. A balanced data set of 66 subjects were used to test the CAD system. Experimental results reported a classification accuracy of 90.9% which supports the efficacy of the CAD system components to accurately model and discriminate between normal and hypertensive subjects. Clinicians would benefit from the proposed CAD system to detect and track cerebral vascular alterations over time for people with high potential of developing hypertension and to prepare appropriate treatment plans to mitigate adverse events.
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Affiliation(s)
- Heba Kandil
- Bioimaging Laboratory, J.B Speed School of Engineering, University of Louisville, KY, USA; Information Technology Department, Faculty of Computer Science and Information, Mansoura University, Egypt
| | - Ahmed Soliman
- Bioimaging Laboratory, J.B Speed School of Engineering, University of Louisville, KY, USA
| | | | - Mohammed Ghazal
- Electrical and Computer Engineering Department, Abu Dhabi University, UAE
| | - Ashraf Khalil
- Electrical and Computer Engineering Department, Abu Dhabi University, UAE
| | - Guruprasad Giridharan
- Bioimaging Laboratory, J.B Speed School of Engineering, University of Louisville, KY, USA
| | - Robert Keynton
- Bioimaging Laboratory, J.B Speed School of Engineering, University of Louisville, KY, USA
| | - J Richard Jennings
- Department of Psychiatry and Psychology, University of Pittsburgh, PA, USA
| | - Ayman El-Baz
- Bioimaging Laboratory, J.B Speed School of Engineering, University of Louisville, KY, USA.
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10
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Shehata M, Shalaby A, Ghazal M, Abou El-Ghar M, Badawy MA, Beache G, Dwyer A, El-Melegy M, Giridharan G, Keynton R, El-Baz A. EARLY ASSESSMENT OF RENAL TRANSPLANTS USING BOLD-MRI: PROMISING RESULTS. Proc Int Conf Image Proc 2019; 2019:1395-1399. [PMID: 34690556 DOI: 10.1109/icip.2019.8803042] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Non-invasive evaluation of renal transplant function is essential to minimize and manage renal rejection. A computer-assisted diagnostic (CAD) system was developed to evaluate kidney function post-transplantation. The developed CAD system utilizes the amount of blood-oxygenation extracted from 3D (2D + time) blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) to estimate renal function. BOLD-MRI scans were acquired at five different echo-times (2, 7, 12, 17, and 22) ms from 15 transplant patients. The developed CAD system first segments kidneys using the level-sets method followed by estimation of the amount of deoxyhemoglobin, also known as apparent relaxation rate (R2*). These R2* estimates were used as discriminatory features (global features (mean R2*) and local features (pixel-wise R2*)) to train and test state-of-the-art machine learning classifiers to differentiate between non-rejection (NR) and acute renal rejection. Using a leave-one-out cross-validation approach along with an artificial neural network (ANN) classifier, the CAD system demonstrated 93.3% accuracy, 100% sensitivity, and 90% specificity in distinguishing AR from non-rejection . These preliminary results demonstrate the efficacy of the CAD system to detect renal allograft status non-invasively.
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Affiliation(s)
- M Shehata
- Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - A Shalaby
- Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - M Ghazal
- Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, UAE.,Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - M Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura, Egypt
| | - M A Badawy
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura, Egypt
| | - G Beache
- Radiology Department, University of Louisville, Louisville, KY, USA
| | - A Dwyer
- Kidney Disease Program, University of Louisville, Louisville, KY, USA
| | - M El-Melegy
- Department of Electrical Engineering, Assiut University, Assiut, Egypt
| | - G Giridharan
- Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - R Keynton
- Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - A El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY, USA
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11
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Kandil H, Soliman A, Ghazal M, Mahmoud A, Shalaby A, Keynton R, Elmaghraby A, Giridharan G, El-Baz A. A Novel Framework for Early Detection of Hypertension using Magnetic Resonance Angiography. Sci Rep 2019; 9:11105. [PMID: 31366941 PMCID: PMC6668478 DOI: 10.1038/s41598-019-47368-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 07/11/2019] [Indexed: 11/16/2022] Open
Abstract
Hypertension is a leading mortality cause of 410,000 patients in USA. Cerebrovascular structural changes that occur as a result of chronically elevated cerebral perfusion pressure are hypothesized to precede the onset of systemic hypertension. A novel framework is presented in this manuscript to detect and quantify cerebrovascular changes (i.e. blood vessel diameters and tortuosity changes) using magnetic resonance angiography (MRA) data. The proposed framework consists of: 1) A novel adaptive segmentation algorithm to delineate large as well as small blood vessels locally using 3-D spatial information and appearance features of the cerebrovascular system; 2) Estimating the cumulative distribution function (CDF) of the 3-D distance map of the cerebrovascular system to quantify alterations in cerebral blood vessels' diameters; 3) Calculation of mean and Gaussian curvatures to quantify cerebrovascular tortuosity; and 4) Statistical and correlation analyses to identify the relationship between mean arterial pressure (MAP) and cerebral blood vessels' diameters and tortuosity alterations. The proposed framework was validated using MAP and MRA data collected from 15 patients over a 700-days period. The novel adaptive segmentation algorithm recorded a 92.23% Dice similarity coefficient (DSC), a 94.82% sensitivity, a 99.00% specificity, and a 10.00% absolute vessels volume difference (AVVD) in delineating cerebral blood vessels from surrounding tissues compared to the ground truth. Experiments demonstrated that MAP is inversely related to cerebral blood vessel diameters (p-value < 0.05) globally (over the whole brain) and locally (at circle of Willis and below). A statistically significant direct correlation (p-value < 0.05) was found between MAP and tortuosity (medians of Gaussian and mean curvatures, and average of mean curvature) globally and locally (at circle of Willis and below). Quantification of the cerebrovascular diameter and tortuosity changes may enable clinicians to predict elevated blood pressure before its onset and optimize medical treatment plans of pre-hypertension and hypertension.
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Affiliation(s)
- Heba Kandil
- Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
- Computer Engineering and Computer Science Department, University of Louisville, Louisville, KY, USA
- Faculty of Computer Science and Information, Information Technology Department, Mansoura University, Mansoura, 35516, Egypt
| | - Ahmed Soliman
- Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Mohammed Ghazal
- Electrical and Computer Engineering Department, University of Abu Dhabi, Abu Dhabi, UAE
| | - Ali Mahmoud
- Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Ahmed Shalaby
- Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Robert Keynton
- Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Adel Elmaghraby
- Computer Engineering and Computer Science Department, University of Louisville, Louisville, KY, USA
| | - Guruprasad Giridharan
- Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Ayman El-Baz
- Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA.
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12
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Abdeltawab H, Shehata M, Shalaby A, Khalifa F, Mahmoud A, El-Ghar MA, Dwyer AC, Ghazal M, Hajjdiab H, Keynton R, El-Baz A. A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction. Sci Rep 2019; 9:5948. [PMID: 30976081 PMCID: PMC6459833 DOI: 10.1038/s41598-019-42431-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Accepted: 03/29/2019] [Indexed: 12/30/2022] Open
Abstract
This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance imaging (DW-MRI) by estimating the apparent diffusion coefficients (ADC) representing the perfusion of the blood and the diffusion of the water inside the transplanted kidney. The clinical biomarkers, namely: creatinine clearance (CrCl) and serum plasma creatinine (SPCr), are integrated into the proposed CAD system as kidney functionality indexes to enhance its diagnostic performance. The ADC maps are estimated for a user-defined region of interest (ROI) that encompasses the whole kidney. The estimated ADCs are fused with the clinical biomarkers and the fused data is then used as an input to train and test a convolutional neural network (CNN) based classifier. The CAD system is tested on DW-MRI scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. These results demonstrate the potential of the proposed system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol.
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Affiliation(s)
- Hisham Abdeltawab
- Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - Mohamed Shehata
- Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - Ahmed Shalaby
- Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - Fahmi Khalifa
- Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - Mohamed Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura, Egypt
| | - Amy C Dwyer
- Kidney Disease Program, University of Louisville, Louisville, KY, USA
| | - Mohammed Ghazal
- Bioengineering Department, University of Louisville, Louisville, KY, USA.,Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, UAE
| | - Hassan Hajjdiab
- Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, UAE
| | - Robert Keynton
- Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY, USA.
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13
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Shaffie A, Soliman A, Fraiwan L, Ghazal M, Taher F, Dunlap N, Wang B, van Berkel V, Keynton R, Elmaghraby A, El-Baz A. A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules. Technol Cancer Res Treat 2019; 17:1533033818798800. [PMID: 30244648 PMCID: PMC6153532 DOI: 10.1177/1533033818798800] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
A novel framework for the classification of lung nodules using computed tomography scans is proposed in this article. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following 2 groups of features: (1) appearance features modeled using the higher order Markov Gibbs random field model that has the ability to describe the spatial inhomogeneities inside the lung nodule and (2) geometric features that describe the shape geometry of the lung nodules. The novelty of this article is to accurately model the appearance of the detected lung nodules using a new developed seventh-order Markov Gibbs random field model that has the ability to model the existing spatial inhomogeneities for both small and large detected lung nodules, in addition to the integration with the extracted geometric features. Finally, a deep autoencoder classifier is fed by the above 2 feature groups to distinguish between the malignant and benign nodules. To evaluate the proposed framework, we used the publicly available data from the Lung Image Database Consortium. We used a total of 727 nodules that were collected from 467 patients. The proposed system demonstrates the promise to be a valuable tool for the detection of lung cancer evidenced by achieving a nodule classification accuracy of 91.20%.
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Affiliation(s)
- Ahmed Shaffie
- 1 Bioengineering Department, University of Louisville, Louisville, KY, USA.,2 Computer Engineering and Computer Science Department, University of Louisville, Louisville, KY, USA
| | - Ahmed Soliman
- 1 Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - Luay Fraiwan
- 3 Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Mohammed Ghazal
- 1 Bioengineering Department, University of Louisville, Louisville, KY, USA.,3 Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Fatma Taher
- 4 College of Technological Innovation, Zayed University, Dubai, United Arab Emirates
| | - Neal Dunlap
- 5 Department of Radiation Oncology, University of Louisville, Louisville, KY, USA
| | - Brian Wang
- 5 Department of Radiation Oncology, University of Louisville, Louisville, KY, USA
| | - Victor van Berkel
- 6 Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, KY, USA
| | - Robert Keynton
- 1 Bioengineering Department, University of Louisville, Louisville, KY, USA
| | - Adel Elmaghraby
- 2 Computer Engineering and Computer Science Department, University of Louisville, Louisville, KY, USA
| | - Ayman El-Baz
- 1 Bioengineering Department, University of Louisville, Louisville, KY, USA
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14
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Dekhil O, Ali M, El-Nakieb Y, Shalaby A, Soliman A, Switala A, Mahmoud A, Ghazal M, Hajjdiab H, Casanova MF, Elmaghraby A, Keynton R, El-Baz A, Barnes G. A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data. Front Psychiatry 2019; 10:392. [PMID: 31333507 PMCID: PMC6620533 DOI: 10.3389/fpsyt.2019.00392] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2018] [Accepted: 05/17/2019] [Indexed: 01/08/2023] Open
Abstract
Autism spectrum disorder is a neuro-developmental disorder that affects the social abilities of the patients. Yet, the gold standard of autism diagnosis is the autism diagnostic observation schedule (ADOS). In this study, we are implementing a computer-aided diagnosis system that utilizes structural MRI (sMRI) and resting-state functional MRI (fMRI) to demonstrate that both anatomical abnormalities and functional connectivity abnormalities have high prediction ability of autism. The proposed system studies how the anatomical and functional connectivity metrics provide an overall diagnosis of whether the subject is autistic or not and are correlated with ADOS scores. The system provides a personalized report per subject to show what areas are more affected by autism-related impairment. Our system achieved accuracies of 75% when using fMRI data only, 79% when using sMRI data only, and 81% when fusing both together. Such a system achieves an important next step towards delineating the neurocircuits responsible for the autism diagnosis and hence may provide better options for physicians in devising personalized treatment plans.
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Affiliation(s)
- Omar Dekhil
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States
| | - Mohamed Ali
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States
| | - Yaser El-Nakieb
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States
| | - Ahmed Shalaby
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States
| | - Ahmed Soliman
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States
| | - Andrew Switala
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States
| | - Ali Mahmoud
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States
| | - Mohammed Ghazal
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States.,Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Hassan Hajjdiab
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Manuel F Casanova
- Department of Biomedical Sciences, University of South Carolina, Greenville, SC, United States
| | - Adel Elmaghraby
- Computer Engineering and Computer Science Department, University of Louisville, Louisville, KY, United States
| | - Robert Keynton
- Bioengineering Department, University of Louisville, Louisville, KY, United States
| | - Ayman El-Baz
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States
| | - Gregory Barnes
- Department of Neurology, University of Louisville, Louisville, KY, United States
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15
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Dekhil O, Hajjdiab H, Shalaby A, Ali MT, Ayinde B, Switala A, Elshamekh A, Ghazal M, Keynton R, Barnes G, El-Baz A. Using resting state functional MRI to build a personalized autism diagnosis system. PLoS One 2018; 13:e0206351. [PMID: 30379950 PMCID: PMC6209234 DOI: 10.1371/journal.pone.0206351] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 10/11/2018] [Indexed: 11/19/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neuro-developmental disorder associated with social impairments, communication difficulties, and restricted and repetitive behaviors. Yet, there is no confirmed cause identified for ASD. Studying the functional connectivity of the brain is an emerging technique used in diagnosing and understanding ASD. In this study, we obtained the resting state functional MRI data of 283 subjects from the National Database of Autism Research (NDAR). An automated autism diagnosis system was built using the data from NDAR. The proposed system is machine learning based. Power spectral densities (PSDs) of time courses corresponding to the spatial activation areas are used as input features, feeds them to a stacked autoencoder then builds a classifier using probabilistic support vector machines. Over the used dataset, around 90% of sensitivity, specificity and accuracy was achieved by our machine learning system. Moreover, the system generalization ability was checked over two different prevalence values, one for the general population and the other for the of high risk population, and the system proved to be very generalizable, especially among the population of high risk. The proposed system generates a full personalized report for each subject, along with identifying the global differences between ASD and typically developed (TD) subjects and its ability to diagnose autism. It shows the impacted areas and the severity of implications. From the clinical aspect, this report is considered very valuable as it helps in both predicting and understanding behavior of autistic subjects. Moreover, it helps in designing a plan for personalized treatment per each individual subject. The proposed work is taking a step towards achieving personalized medicine in autism which is the ultimate goal of our group's research efforts in this area.
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Affiliation(s)
- Omar Dekhil
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States of America
| | - Hassan Hajjdiab
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Ahmed Shalaby
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States of America
| | - Mohamed T. Ali
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States of America
| | - Babajide Ayinde
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States of America
| | - Andy Switala
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States of America
| | - Aliaa Elshamekh
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States of America
| | - Mohamed Ghazal
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States of America
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Robert Keynton
- Bioengineering Department, University of Louisville, Louisville, KY, United States of America
| | - Gregory Barnes
- Department of Neurology, University of Louisville, Louisville, KY, United States of America
| | - Ayman El-Baz
- Bioimaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, United States of America
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16
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Sandhu HS, Eladawi N, Elmogy M, Keynton R, Helmy O, Schaal S, El-Baz A. Automated diabetic retinopathy detection using optical coherence tomography angiography: a pilot study. Br J Ophthalmol 2018; 102:1564-1569. [PMID: 29363532 DOI: 10.1136/bjophthalmol-2017-311489] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 12/11/2017] [Accepted: 01/03/2018] [Indexed: 11/03/2022]
Abstract
BACKGROUND Optical coherence tomography angiography (OCTA) is increasingly being used to evaluate diabetic retinopathy, but the interpretation of OCTA remains largely subjective. The purpose of this study was to design a computer-aided diagnostic (CAD) system to diagnose non-proliferative diabetic retinopathy (NPDR) in an automated fashion using OCTA images. METHODS This was a two-centre, cross-sectional study. Adults with type II diabetes mellitus (DMII) were eligible for inclusion. OCTA scans of the macula were taken, and the five vascular maps generated per eye were analysed by a novel CAD system. For the purpose of classification/diagnosis, three different local features-blood vessel density, blood vessel calibre and the size of the foveal avascular zone (FAZ)-were segmented from these images and used to train a new, automated classifier. RESULTS One hundred and six patients with DMII were included in the study, 23 with no DR and 83 with mild NPDR. When using features of the superficial retinal map alone, the system demonstrated an accuracy of 80.0% and area under the curve (AUC) of 76.2%. Using the features of the deep retinal map alone, accuracy was 91.4% and AUC 89.2%. When data from both maps were combined, the presented CAD system demonstrated overall accuracy of 94.3%, sensitivity of 97.9%, specificity of 87.0%, area under curve (AUC) of 92.4% and dice similarity coefficient of 95.8%. CONCLUSION Automated diagnosis of NPDR using OCTA images is feasible and accurate. Combining this system with OCT data is a plausible next step that would likely improve its robustness.
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Affiliation(s)
- Harpal Singh Sandhu
- Department of Ophthalmology and Visual Sciences, School of Medicine, University of Louisville, Louisville, Kentucky, USA
| | - Nabila Eladawi
- Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Mohammed Elmogy
- Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Robert Keynton
- Bioengineering Department, University of Louisville, Louisville, Kentucky, USA
| | - Omar Helmy
- Department of Ophthalmology and Visual Sciences, University of Massachusetts Medical School, Worchester, Massachusetts, USA
| | - Shlomit Schaal
- Department of Ophthalmology and Visual Sciences, University of Massachusetts Medical School, Worchester, Massachusetts, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, Kentucky, USA
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17
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El-Gamal FEA, Elmogy MM, Ghazal M, Atwan A, Casanova MF, Barnes GN, Keynton R, El-Baz AS, Khalil A. A Novel Early Diagnosis System for Mild Cognitive Impairment Based on Local Region Analysis: A Pilot Study. Front Hum Neurosci 2018; 11:643. [PMID: 29375343 PMCID: PMC5767309 DOI: 10.3389/fnhum.2017.00643] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 12/18/2017] [Indexed: 11/24/2022] Open
Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that accounts for 60–70% of cases of dementia in the elderly. An early diagnosis of AD is usually hampered for many reasons including the variable clinical and pathological features exhibited among affected individuals. This paper presents a computer-aided diagnosis (CAD) system with the primary goal of improving the accuracy, specificity, and sensitivity of diagnosis. In this system, PiB-PET scans, which were obtained from the ADNI database, underwent five essential stages. First, the scans were standardized and de-noised. Second, an Automated Anatomical Labeling (AAL) atlas was utilized to partition the brain into 116 regions or labels that served for local (region-based) diagnosis. Third, scale-invariant Laplacian of Gaussian (LoG) was used, per brain label, to detect the discriminant features. Fourth, the regions' features were analyzed using a general linear model in the form of a two-sample t-test. Fifth, the support vector machines (SVM) and their probabilistic variant (pSVM) were constructed to provide local, followed by global diagnosis. The system was evaluated on scans of normal control (NC) vs. mild cognitive impairment (MCI) (19 NC and 65 MCI scans). The proposed system showed superior accuracy, specificity, and sensitivity as compared to other related work.
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Affiliation(s)
- Fatma E A El-Gamal
- Faculty of Computers and Information, Information Technology Department, Mansoura University, Mansoura, Egypt.,BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY, United States
| | - Mohammed M Elmogy
- Faculty of Computers and Information, Information Technology Department, Mansoura University, Mansoura, Egypt.,BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY, United States
| | - Mohammed Ghazal
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY, United States.,Department of Electrical and Computer Engineering, College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Ahmed Atwan
- Faculty of Computers and Information, Information Technology Department, Mansoura University, Mansoura, Egypt
| | - Manuel F Casanova
- School of Medicine, University of South Carolina, Greenville, SC, United States
| | - Gregory N Barnes
- University of Louisville Autism Center, Department of Neurology, University of Louisville, Louisville, KY, United States
| | - Robert Keynton
- Department of Bioengineering, University of Louisville, Louisville, KY, United States
| | - Ayman S El-Baz
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY, United States
| | - Ashraf Khalil
- Department of Computer Science and Information Technology, College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
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18
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Soliman A, Khalifa F, Elnakib A, Abou El-Ghar M, Dunlap N, Wang B, Gimel'farb G, Keynton R, El-Baz A. Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling. IEEE Trans Med Imaging 2017; 36:263-276. [PMID: 27705854 DOI: 10.1109/tmi.2016.2606370] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
To accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub-models with an adaptive lung shape submodel. The first-order appearance submodel accounts for both the original CT image and its Gaussian scale space (GSS) filtered version to specify local and global signal properties, respectively. Each empirical marginal probability distribution of signals is closely approximated with a linear combination of discrete Gaussians (LCDG), containing two positive dominant and multiple sign-alternate subordinate DGs. The approximation is separated into two LCDGs to describe individually the lungs and their background, i.e., all other chest tissues. The second-order appearance submodel quantifies conditional pairwise intensity dependencies in the nearest voxel 26-neighborhood in both the original and GSS-filtered images. The shape submodel is built for a set of training data and is adapted during segmentation using both the lung and chest appearances. The accuracy of the proposed segmentation framework is quantitatively assessed using two public databases (ISBI VESSEL12 challenge and MICCAI LOLA11 challenge) and our own database with, respectively, 20, 55, and 30 CT images of various lung pathologies acquired with different scanners and protocols. Quantitative assessment of our framework in terms of Dice similarity coefficients, 95-percentile bidirectional Hausdorff distances, and percentage volume differences confirms the high accuracy of our model on both our database (98.4±1.0%, 2.2±1.0 mm, 0.42±0.10%) and the VESSEL12 database (99.0±0.5%, 2.1±1.6 mm, 0.39±0.20%), respectively. Similarly, the accuracy of our approach is further verified via a blind evaluation by the organizers of the LOLA11 competition, where an average overlap of 98.0% with the expert's segmentation is yielded on all 55 subjects with our framework being ranked first among all the state-of-the-art techniques compared.
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19
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Martin MD, Roussel TJ, Cambron S, Aebersold J, Jackson D, Walsh K, Lin JT, O’Toole MG, Keynton R. Performance of stacked, flow-through micropreconcentrators for portable trace detection. ACTA ACUST UNITED AC 2010. [DOI: 10.1007/s12127-010-0048-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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