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Abd El-Khalek AA, Balaha HM, Alghamdi NS, Ghazal M, Khalil AT, Abo-Elsoud MEA, El-Baz A. A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images. Sci Rep 2024; 14:2434. [PMID: 38287062 PMCID: PMC10825213 DOI: 10.1038/s41598-024-52131-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/14/2024] [Indexed: 01/31/2024] Open
Abstract
The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular eye examinations. Age-related macular degeneration (AMD), a prevalent condition in individuals over 45, is a leading cause of vision impairment in the elderly. This paper presents a comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. This is crucial for early detection and precise diagnosis of age-related macular degeneration (AMD), enabling timely intervention and personalized treatment strategies. We have developed a novel system that extracts both local and global appearance markers from fundus images. These markers are obtained from the entire retina and iso-regions aligned with the optical disc. Applying weighted majority voting on the best classifiers improves performance, resulting in an accuracy of 96.85%, sensitivity of 93.72%, specificity of 97.89%, precision of 93.86%, F1 of 93.72%, ROC of 95.85%, balanced accuracy of 95.81%, and weighted sum of 95.38%. This system not only achieves high accuracy but also provides a detailed assessment of the severity of each retinal region. This approach ensures that the final diagnosis aligns with the physician's understanding of AMD, aiding them in ongoing treatment and follow-up for AMD patients.
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Affiliation(s)
- Aya A Abd El-Khalek
- Communications and Electronics Engineering Department, Nile Higher Institute for Engineering and Technology, Mansoura, Egypt
| | - Hossam Magdy Balaha
- BioImaging Lab, Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY, USA
| | - Norah Saleh Alghamdi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Depatrment, Abu Dhabi University, Abu Dhabi, UAE
| | - Abeer T Khalil
- Communications and Electronics Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Mohy Eldin A Abo-Elsoud
- Communications and Electronics Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Ayman El-Baz
- BioImaging Lab, Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY, USA.
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Farahat IS, Sharafeldeen A, Ghazal M, Alghamdi NS, Mahmoud A, Connelly J, van Bogaert E, Zia H, Tahtouh T, Aladrousy W, Tolba AE, Elmougy S, El-Baz A. An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis. Sci Rep 2024; 14:851. [PMID: 38191606 PMCID: PMC10774502 DOI: 10.1038/s41598-023-51053-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/29/2023] [Indexed: 01/10/2024] Open
Abstract
The proposed AI-based diagnostic system aims to predict the respiratory support required for COVID-19 patients by analyzing the correlation between COVID-19 lesions and the level of respiratory support provided to the patients. Computed tomography (CT) imaging will be used to analyze the three levels of respiratory support received by the patient: Level 0 (minimum support), Level 1 (non-invasive support such as soft oxygen), and Level 2 (invasive support such as mechanical ventilation). The system will begin by segmenting the COVID-19 lesions from the CT images and creating an appearance model for each lesion using a 2D, rotation-invariant, Markov-Gibbs random field (MGRF) model. Three MGRF-based models will be created, one for each level of respiratory support. This suggests that the system will be able to differentiate between different levels of severity in COVID-19 patients. The system will decide for each patient using a neural network-based fusion system, which combines the estimates of the Gibbs energy from the three MGRF-based models. The proposed system were assessed using 307 COVID-19-infected patients, achieving an accuracy of [Formula: see text], a sensitivity of [Formula: see text], and a specificity of [Formula: see text], indicating a high level of prediction accuracy.
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Affiliation(s)
- Ibrahim Shawky Farahat
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | | | - Mohammed Ghazal
- Electrical, Computer and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi, UAE
| | - Norah Saleh Alghamdi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Ali Mahmoud
- Department of Bioengineering, University of Louisville, Louisville, USA
| | - James Connelly
- Department of Radiology, University of Louisville, Louisville, USA
| | - Eric van Bogaert
- Department of Radiology, University of Louisville, Louisville, USA
| | - Huma Zia
- Electrical, Computer and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi, UAE
| | - Tania Tahtouh
- College of Health Sciences, Abu Dhabi University, Abu Dhabi, UAE
| | - Waleed Aladrousy
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Ahmed Elsaid Tolba
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
- The Higher Institute of Engineering and Automotive Technology and Energy, Kafr El Sheikh, Egypt
| | - Samir Elmougy
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, USA.
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Mhlanga ST, Viriri S. Deep learning techniques for isointense infant brain tissue segmentation: a systematic literature review. Front Med (Lausanne) 2023; 10:1240360. [PMID: 38193036 PMCID: PMC10773803 DOI: 10.3389/fmed.2023.1240360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/01/2023] [Indexed: 01/10/2024] Open
Abstract
Introduction To improve comprehension of initial brain growth in wellness along with sickness, it is essential to precisely segment child brain magnetic resonance imaging (MRI) into white matter (WM) and gray matter (GM), along with cerebrospinal fluid (CSF). Nonetheless, in the isointense phase (6-8 months of age), the inborn myelination and development activities, WM along with GM display alike stages of intensity in both T1-weighted and T2-weighted MRI, making tissue segmentation extremely difficult. Methods The comprehensive review of studies related to isointense brain MRI segmentation approaches is highlighted in this publication. The main aim and contribution of this study is to aid researchers by providing a thorough review to make their search for isointense brain MRI segmentation easier. The systematic literature review is performed from four points of reference: (1) review of studies concerning isointense brain MRI segmentation; (2) research contribution and future works and limitations; (3) frequently applied evaluation metrics and datasets; (4) findings of this studies. Results and discussion The systemic review is performed on studies that were published in the period of 2012 to 2022. A total of 19 primary studies of isointense brain MRI segmentation were selected to report the research question stated in this review.
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Affiliation(s)
| | - Serestina Viriri
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa
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Saleh GA, Batouty NM, Gamal A, Elnakib A, Hamdy O, Sharafeldeen A, Mahmoud A, Ghazal M, Yousaf J, Alhalabi M, AbouEleneen A, Tolba AE, Elmougy S, Contractor S, El-Baz A. Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review. Cancers (Basel) 2023; 15:5216. [PMID: 37958390 PMCID: PMC10650187 DOI: 10.3390/cancers15215216] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/13/2023] [Accepted: 10/21/2023] [Indexed: 11/15/2023] Open
Abstract
Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.
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Affiliation(s)
- Gehad A. Saleh
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt; (G.A.S.)
| | - Nihal M. Batouty
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt; (G.A.S.)
| | - Abdelrahman Gamal
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
| | - Ahmed Elnakib
- Electrical and Computer Engineering Department, School of Engineering, Penn State Erie, The Behrend College, Erie, PA 16563, USA;
| | - Omar Hamdy
- Surgical Oncology Department, Oncology Centre, Mansoura University, Mansoura 35516, Egypt;
| | - Ahmed Sharafeldeen
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.)
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.)
| | - Marah Alhalabi
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.)
| | - Amal AbouEleneen
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
| | - Ahmed Elsaid Tolba
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
- The Higher Institute of Engineering and Automotive Technology and Energy, New Heliopolis, Cairo 11829, Egypt
| | - Samir Elmougy
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
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