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Cheng AMS, Chalam KV, Brar VS, Yang DTY, Bhatt J, Banoub RG, Gupta SK. Recent Advances in Imaging Macular Atrophy for Late-Stage Age-Related Macular Degeneration. Diagnostics (Basel) 2023; 13:3635. [PMID: 38132220 PMCID: PMC10742961 DOI: 10.3390/diagnostics13243635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 12/02/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
Age-related macular degeneration (AMD) is a leading cause of blindness worldwide. In late-stage AMD, geographic atrophy (GA) of dry AMD or choroidal neovascularization (CNV) of neovascular AMD eventually results in macular atrophy (MA), leading to significant visual loss. Despite the development of innovative therapies, there are currently no established effective treatments for MA. As a result, early detection of MA is critical in identifying later central macular involvement throughout time. Accurate and early diagnosis is achieved through a combination of clinical examination and imaging techniques. Our review of the literature depicts advances in retinal imaging to identify biomarkers of progression and risk factors for late AMD. Imaging methods like fundus photography; dye-based angiography; fundus autofluorescence (FAF); near-infrared reflectance (NIR); optical coherence tomography (OCT); and optical coherence tomography angiography (OCTA) can be used to detect and monitor the progression of retinal atrophy. These evolving diverse imaging modalities optimize detection of pathologic anatomy and measurement of visual function; they may also contribute to the understanding of underlying mechanistic pathways, particularly the underlying MA changes in late AMD.
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Affiliation(s)
- Anny M. S. Cheng
- Department of Ophthalmology, Broward Health, Fort Lauderdale, FL 33064, USA; (A.M.S.C.); (R.G.B.)
- Specialty Retina Center, Coral Springs, FL 33067, USA;
- Department of Ophthalmology, Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA
| | - Kakarla V. Chalam
- Department of Ophthalmology, Loma Linda University, Loma Linda, CA 92350, USA;
| | - Vikram S. Brar
- Department of Ophthalmology, Virginia Commonwealth University, Richmond, VA 23298, USA;
| | - David T. Y. Yang
- College of Biological Science, University of California, Davis, Sacramento, CA 95616, USA;
| | - Jineel Bhatt
- Specialty Retina Center, Coral Springs, FL 33067, USA;
| | - Raphael G. Banoub
- Department of Ophthalmology, Broward Health, Fort Lauderdale, FL 33064, USA; (A.M.S.C.); (R.G.B.)
- Specialty Retina Center, Coral Springs, FL 33067, USA;
| | - Shailesh K. Gupta
- Department of Ophthalmology, Broward Health, Fort Lauderdale, FL 33064, USA; (A.M.S.C.); (R.G.B.)
- Specialty Retina Center, Coral Springs, FL 33067, USA;
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Aminizadeh S, Heidari A, Toumaj S, Darbandi M, Navimipour NJ, Rezaei M, Talebi S, Azad P, Unal M. The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107745. [PMID: 37579550 DOI: 10.1016/j.cmpb.2023.107745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 07/15/2023] [Accepted: 08/02/2023] [Indexed: 08/16/2023]
Abstract
Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL algorithm was the Convolutional Neural Network (CNN), with a percentage of 19.4%. Hence, despite how technology changes, delivering appropriate therapy for patients is the primary aim of healthcare-associated departments. Therefore, further studies are recommended to develop more functional architectures based on DL and distributed environments and better evaluate the present healthcare data analysis models.
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Affiliation(s)
| | - Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran; Department of Software Engineering, Haliç University, Istanbul, Turkiye.
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
| | - Mehdi Darbandi
- Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Gazimagusa 99628, Turkiye
| | - Nima Jafari Navimipour
- Department of Computer Engineering, Kadir Has University, Istanbul, Turkiye; Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan.
| | - Mahsa Rezaei
- Tabriz University of Medical Sciences, Faculty of Surgery, Tabriz, Iran
| | - Samira Talebi
- Department of Computer Science, University of Texas at San Antonio, TX, USA
| | - Poupak Azad
- Department of Computer Science, University of Manitoba, Winnipeg, Canada
| | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkiye
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Wei W, Southern J, Zhu K, Li Y, Cordeiro MF, Veselkov K. Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography. Sci Rep 2023; 13:8296. [PMID: 37217770 DOI: 10.1038/s41598-023-35414-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 05/17/2023] [Indexed: 05/24/2023] Open
Abstract
Here, we have developed a deep learning method to fully automatically detect and quantify six main clinically relevant atrophic features associated with macular atrophy (MA) using optical coherence tomography (OCT) analysis of patients with wet age-related macular degeneration (AMD). The development of MA in patients with AMD results in irreversible blindness, and there is currently no effective method of early diagnosis of this condition, despite the recent development of unique treatments. Using OCT dataset of a total of 2211 B-scans from 45 volumetric scans of 8 patients, a convolutional neural network using one-against-all strategy was trained to present all six atrophic features followed by a validation to evaluate the performance of the models. The model predictive performance has achieved a mean dice similarity coefficient score of 0.706 ± 0.039, a mean Precision score of 0.834 ± 0.048, and a mean Sensitivity score of 0.615 ± 0.051. These results show the unique potential of using artificially intelligence-aided methods for early detection and identification of the progression of MA in wet AMD, which can further support and assist clinical decisions.
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Affiliation(s)
- Wei Wei
- Department of Surgery and Cancer, Imperial College London, London, UK
- Ningbo Medical Center Lihuili Hospital, Ningbo, China
- Imperial College Ophthalmology Research Group, London, UK
| | | | - Kexuan Zhu
- Ningbo Medical Center Lihuili Hospital, Ningbo, China
| | - Yefeng Li
- School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo, China
| | - Maria Francesca Cordeiro
- Department of Surgery and Cancer, Imperial College London, London, UK.
- Imperial College Ophthalmology Research Group, London, UK.
| | - Kirill Veselkov
- Department of Surgery and Cancer, Imperial College London, London, UK.
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Wei W, Anantharanjit R, Patel RP, Cordeiro MF. Detection of macular atrophy in age-related macular degeneration aided by artificial intelligence. Expert Rev Mol Diagn 2023:1-10. [PMID: 37144908 DOI: 10.1080/14737159.2023.2208751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
INTRODUCTION Age-related macular degeneration (AMD) is a leading cause of irreversible visual impairment worldwide. The endpoint of AMD, both in its dry or wet form, is macular atrophy (MA) which is characterized by the permanent loss of the RPE and overlying photoreceptors either in dry AMD or in wet AMD. A recognized unmet need in AMD is the early detection of MA development. AREAS COVERED Artificial Intelligence (AI) has demonstrated great impact in detection of retinal diseases, especially with its robust ability to analyze big data afforded by ophthalmic imaging modalities, such as color fundus photography (CFP), fundus autofluorescence (FAF), near-infrared reflectance (NIR), and optical coherence tomography (OCT). Among these, OCT has been shown to have great promise in identifying early MA using the new criteria in 2018. EXPERT OPINION There are few studies in which AI-OCT methods have been used to identify MA; however, results are very promising when compared to other imaging modalities. In this paper, we review the development and advances of ophthalmic imaging modalities and their combination with AI technology to detect MA in AMD. In addition, we emphasize the application of AI-OCT as an objective, cost-effective tool for the early detection and monitoring of the progression of MA in AMD.
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Affiliation(s)
- Wei Wei
- Department of Ophthalmology, Ningbo Medical Center Lihuili Hospital, Ningbo, China
- Department of Surgery & Cancer, Imperial College London, London, UK
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Ophthalmology Research Group, London, UK
| | - Rajeevan Anantharanjit
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Ophthalmology Research Group, London, UK
- Western Eye Hospital, Imperial College Healthcare NHS trust, London, UK
| | - Radhika Pooja Patel
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Ophthalmology Research Group, London, UK
- Western Eye Hospital, Imperial College Healthcare NHS trust, London, UK
| | - Maria Francesca Cordeiro
- Department of Surgery & Cancer, Imperial College London, London, UK
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Ophthalmology Research Group, London, UK
- Western Eye Hospital, Imperial College Healthcare NHS trust, London, UK
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Mahoto NA, Shaikh A, Sulaiman A, Reshan MSA, Rajab A, Rajab K. A machine learning based data modeling for medical diagnosis. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Coan LJ, Williams BM, Krishna Adithya V, Upadhyaya S, Alkafri A, Czanner S, Venkatesh R, Willoughby CE, Kavitha S, Czanner G. Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review. Surv Ophthalmol 2023; 68:17-41. [PMID: 35985360 DOI: 10.1016/j.survophthal.2022.08.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 08/04/2022] [Accepted: 08/08/2022] [Indexed: 02/01/2023]
Abstract
Glaucoma is a leading cause of irreversible vision impairment globally, and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention that can prevent further visual field loss. To detect glaucoma an examination of the optic nerve head via fundus imaging can be performed, at the center of which is the assessment of the optic cup and disc boundaries. Fundus imaging is noninvasive and low-cost; however, image examination relies on subjective, time-consuming, and costly expert assessments. A timely question to ask is: "Can artificial intelligence mimic glaucoma assessments made by experts?" Specifically, can artificial intelligence automatically find the boundaries of the optic cup and disc (providing a so-called segmented fundus image) and then use the segmented image to identify glaucoma with high accuracy? We conducted a comprehensive review on artificial intelligence-enabled glaucoma detection frameworks that produce and use segmented fundus images and summarized the advantages and disadvantages of such frameworks. We identified 36 relevant papers from 2011 to 2021 and 2 main approaches: 1) logical rule-based frameworks, based on a set of rules; and 2) machine learning/statistical modeling-based frameworks. We critically evaluated the state-of-art of the 2 approaches, identified gaps in the literature and pointed at areas for future research.
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Affiliation(s)
- Lauren J Coan
- School of Computer Science and Mathematics, Liverpool John Moores University, UK.
| | - Bryan M Williams
- School of Computing and Communications, Lancaster University, UK
| | | | - Swati Upadhyaya
- Department of Glaucoma, Aravind Eye Hospital, Pondicherry, India
| | - Ala Alkafri
- School of Computing, Engineering & Digital Technologies, Teesside University, UK
| | - Silvester Czanner
- School of Computer Science and Mathematics, Liverpool John Moores University, UK; Faculty of Informatics and Information Technologies, Slovak University of Technology, Slovakia
| | - Rengaraj Venkatesh
- Department of Glaucoma and Chief Medical Officer, Aravind Eye Hospital, Pondicherry, India
| | | | | | - Gabriela Czanner
- School of Computer Science and Mathematics, Liverpool John Moores University, UK; Faculty of Informatics and Information Technologies, Slovak University of Technology, Slovakia
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7
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Multi-layer segmentation of retina OCT images via advanced U-net architecture. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2022.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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8
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Quek TC, Takahashi K, Kang HG, Thakur S, Deshmukh M, Tseng RMWW, Nguyen H, Tham YC, Rim TH, Kim SS, Yanagi Y, Liew G, Cheng CY. Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans. EPMA J 2022; 13:547-560. [PMID: 36505893 PMCID: PMC9727042 DOI: 10.1007/s13167-022-00301-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/25/2022] [Indexed: 11/21/2022]
Abstract
Aims Computer-aided detection systems for retinal fluid could be beneficial for disease monitoring and management by chronic age-related macular degeneration (AMD) and diabetic retinopathy (DR) patients, to assist in disease prevention via early detection before the disease progresses to a "wet AMD" pathology or diabetic macular edema (DME), requiring treatment. We propose a proof-of-concept AI-based app to help predict fluid via a "fluid score", prevent fluid progression, and provide personalized, serial monitoring, in the context of predictive, preventive, and personalized medicine (PPPM) for patients at risk of retinal fluid complications. Methods The app comprises a convolutional neural network-Vision Transformer (CNN-ViT)-based segmentation deep learning (DL) network, trained on a small dataset of 100 training images (augmented to 992 images) from the Singapore Epidemiology of Eye Diseases (SEED) study, together with a CNN-based classification network trained on 8497 images, that can detect fluid vs. non-fluid optical coherence tomography (OCT) scans. Both networks are validated on external datasets. Results Internal testing for our segmentation network produced an IoU score of 83.0% (95% CI = 76.7-89.3%) and a DICE score of 90.4% (86.3-94.4%); for external testing, we obtained an IoU score of 66.7% (63.5-70.0%) and a DICE score of 78.7% (76.0-81.4%). Internal testing of our classification network produced an area under the receiver operating characteristics curve (AUC) of 99.18%, and a Youden index threshold of 0.3806; for external testing, we obtained an AUC of 94.55%, and an accuracy of 94.98% and an F1 score of 85.73% with Youden index. Conclusion We have developed an AI-based app with an alternative transformer-based segmentation algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring, and could allow for the generation of retrospective data to research into the varied use of treatments for AMD and DR. The modular system of our app can be scaled to add more iterative features based on user feedback for more efficient monitoring. Further study and scaling up of the algorithm dataset could potentially boost its usability in a real-world clinical setting. Supplementary information The online version contains supplementary material available at 10.1007/s13167-022-00301-5.
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Affiliation(s)
- Ten Cheer Quek
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
| | | | - Hyun Goo Kang
- Department of Ophthalmology, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sahil Thakur
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
| | - Mihir Deshmukh
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
| | - Rachel Marjorie Wei Wen Tseng
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
| | - Helen Nguyen
- Department of Ophthalmology, Centre for Vision Research, Westmead Institute for Medical Research, University of Sydney, Sydney, Australia
- School of Optometry and Vision Science, Faculty of Science, The University of New South Wales, Sydney, NSW Australia
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Medi Whale Inc, Seoul, South Korea
| | - Sung Soo Kim
- Department of Ophthalmology, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Yasuo Yanagi
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology and Microtechnology, Yokohama City University, Yokohama, Japan
| | - Gerald Liew
- Department of Ophthalmology, Centre for Vision Research, Westmead Institute for Medical Research, University of Sydney, Sydney, Australia
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
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Iqbal S, Khan TM, Naveed K, Naqvi SS, Nawaz SJ. Recent trends and advances in fundus image analysis: A review. Comput Biol Med 2022; 151:106277. [PMID: 36370579 DOI: 10.1016/j.compbiomed.2022.106277] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/19/2022] [Accepted: 10/30/2022] [Indexed: 11/05/2022]
Abstract
Automated retinal image analysis holds prime significance in the accurate diagnosis of various critical eye diseases that include diabetic retinopathy (DR), age-related macular degeneration (AMD), atherosclerosis, and glaucoma. Manual diagnosis of retinal diseases by ophthalmologists takes time, effort, and financial resources, and is prone to error, in comparison to computer-aided diagnosis systems. In this context, robust classification and segmentation of retinal images are primary operations that aid clinicians in the early screening of patients to ensure the prevention and/or treatment of these diseases. This paper conducts an extensive review of the state-of-the-art methods for the detection and segmentation of retinal image features. Existing notable techniques for the detection of retinal features are categorized into essential groups and compared in depth. Additionally, a summary of quantifiable performance measures for various important stages of retinal image analysis, such as image acquisition and preprocessing, is provided. Finally, the widely used in the literature datasets for analyzing retinal images are described and their significance is emphasized.
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Affiliation(s)
- Shahzaib Iqbal
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
| | - Tariq M Khan
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
| | - Khuram Naveed
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan; Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
| | - Syed S Naqvi
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
| | - Syed Junaid Nawaz
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
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Multiple Ocular Disease Diagnosis Using Fundus Images Based on Multi-Label Deep Learning Classification. ELECTRONICS 2022. [DOI: 10.3390/electronics11131966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Designing computer-aided diagnosis (CAD) systems that can automatically detect ocular diseases (ODs) has become an active research field in the health domain. Although the human eye might have more than one OD simultaneously, most existing systems are designed to detect specific eye diseases. Therefore, it is crucial to develop new CAD systems that can detect multiple ODs simultaneously. This paper presents a novel multi-label convolutional neural network (ML-CNN) system based on ML classification (MLC) to diagnose various ODs from color fundus images. The proposed ML-CNN-based system consists of three main phases: the preprocessing phase, which includes normalization and augmentation using several transformation processes, the modeling phase, and the prediction phase. The proposed ML-CNN consists of three convolution (CONV) layers and one max pooling (MP) layer. Then, two CONV layers are performed, followed by one MP and dropout (DO). After that, one flatten layer is performed, followed by one fully connected (FC) layer. We added another DO once again, and finally, one FC layer with 45 nodes is performed. The system outputs the probabilities of all 45 diseases in each image. We validated the model by using cross-validation (CV) and measured the performance by five different metrics: accuracy (ACC), recall, precision, Dice similarity coefficient (DSC), and area under the curve (AUC). The results are 94.3%, 80%, 91.5%, 99%, and 96.7%, respectively. The comparisons with the existing built-in models, such as MobileNetV2, DenseNet201, SeResNext50, InceptionV3, and InceptionresNetv2, demonstrate the superiority of the proposed ML-CNN model.
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12
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Aljuaid A, Anwar M. Survey of Supervised Learning for Medical Image Processing. SN COMPUTER SCIENCE 2022; 3:292. [PMID: 35602289 PMCID: PMC9112642 DOI: 10.1007/s42979-022-01166-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/20/2022] [Indexed: 12/20/2022]
Abstract
Medical image interpretation is an essential task for the correct diagnosis of many diseases. Pathologists, radiologists, physicians, and researchers rely heavily on medical images to perform diagnoses and develop new treatments. However, manual medical image analysis is tedious and time consuming, making it necessary to identify accurate automated methods. Deep learning—especially supervised deep learning—shows impressive performance in the classification, detection, and segmentation of medical images and has proven comparable in ability to humans. This survey aims to help researchers and practitioners of medical image analysis understand the key concepts and algorithms of supervised learning techniques. Specifically, this survey explains the performance metrics of supervised learning methods; summarizes the available medical datasets; studies the state-of-the-art supervised learning architectures for medical imaging processing, including convolutional neural networks (CNNs) and their corresponding algorithms, region-based CNNs and their variants, fully convolutional networks (FCN) and U-Net architecture; and discusses the trends and challenges in the application of supervised learning methods to medical image analysis. Supervised learning requires large labeled datasets to learn and achieve good performance, and data augmentation, transfer learning, and dropout techniques have widely been employed in medical image processing to overcome the lack of such datasets.
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Affiliation(s)
- Abeer Aljuaid
- Department of Computer Science, North Carolina A&T State University, 1601 E Market St, Greensboro, NC 27411 USA
| | - Mohd Anwar
- Department of Computer Science, North Carolina A&T State University, 1601 E Market St, Greensboro, NC 27411 USA
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13
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Wang C, Yang J, Sawan M. NeuroSEE: A Neuromorphic Energy Efficient Processing Framework for Visual Prostheses. IEEE J Biomed Health Inform 2022; 26:4132-4141. [PMID: 35503849 DOI: 10.1109/jbhi.2022.3172306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Visual prostheses with both comprehensive visual signal processing capability and energy efficiency are becoming increasingly demanded in the age of intelligent personal healthcare, particularly with the rise of wearable and implantable devices. To address this trend, we propose NeuroSEE, a neuromorphic energy-efficient processing framework that combines a spike representation encoding technique and a bio-inspired processing method. This framework first utilizes sparse spike trains to represent visual information, and then a bio-inspired spiking neural network (SNN) is adopted to process the spike trains. The SNN model makes use of an IF neuron with multiple spikefiring rates to decrease the energy consumption without compensating for prediction performance. The experimental results indicate that when predicting the response of the primary visual cortex, the framework achieves a state-ofthe- art Pearson correlation coefficient performance. Spikebased recording and processing methods simplify the storage and transmission of redundant scene information and complex calculation processes. It could reduce power consumption by 15 times compared with the existing Convolutional neural network (CNN) processing framework. The proposed NeuroSEE framework predicts the response of the primary visual cortex in an energy efficient manner, making it a powerful tool for visual prostheses.
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Xue X, Yang X, Deng Z, Tu H, Kong D, Li N, Xu F. Global Trends and Hotspots in Research on Rehabilitation Robots: A Bibliometric Analysis From 2010 to 2020. Front Public Health 2022; 9:806723. [PMID: 35087788 PMCID: PMC8788947 DOI: 10.3389/fpubh.2021.806723] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 12/02/2021] [Indexed: 12/21/2022] Open
Abstract
Background: In recent years, with the development of medical science and artificial intelligence, research on rehabilitation robots has gained more and more attention, for nearly 10 years in the Web of Science database by journal of rehabilitation robot-related research literature analysis, to parse and track rehabilitation robot research hotspot and front, and provide some guidance for future research. Methods: This study employed computer retrieval of rehabilitation robot-related research published in the core data collection of the Web of Science database from 2010 to 2020, using CiteSpace 5.7 visualization software. The hotspots and frontiers of rehabilitation robot research are analyzed from the aspects of high-influence countries or regions, institutions, authors, high-frequency keywords, and emergent words. Results: A total of 3,194 articles were included. In recent years, the research on rehabilitation robots has been continuously hot, and the annual publication of relevant literature has shown a trend of steady growth. The United States ranked first with 819 papers, and China ranked second with 603 papers. Northwestern University ranked first with 161 publications. R. Riener, a professor at the University of Zurich, Switzerland, ranked as the first author with 48 articles. The Journal of Neural Engineering and Rehabilitation has the most published research, with 211 publications. In the past 10 years, research has focused on intelligent control, task analysis, and the learning, performance, and reliability of rehabilitation robots to realize the natural and precise interaction between humans and machines. Research on neural rehabilitation robots, brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeletons has attracted more and more attention. Conclusions: At present, the brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeleton rehabilitation robots are the research trends and hotspots. Future research should focus on the application of machine learning (ML), dimensionality reduction, and feature engineering technologies in the research and development of rehabilitation robots to improve the speed and accuracy of algorithms. To achieve wide application and commercialization, future rehabilitation robots should also develop toward mass production and low cost. We should pay attention to the functional needs of patients, strengthen multidisciplinary communication and cooperation, and promote rehabilitation robots to better serve the rehabilitation medical field.
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Affiliation(s)
- Xiali Xue
- Institute of Sports Medicine and Health, Chengdu Sport University, Chengdu, China
| | - Xinwei Yang
- School of Sports Medicine and Health, Chengdu Sport University, Chengdu, China
| | - Zhongyi Deng
- Institute of Sports Medicine and Health, Chengdu Sport University, Chengdu, China
| | - Huan Tu
- Institute of Sports Medicine and Health, Chengdu Sport University, Chengdu, China
| | - Dezhi Kong
- Institute of Sports Medicine and Health, Chengdu Sport University, Chengdu, China
| | - Ning Li
- Institute of Sports Medicine and Health, Chengdu Sport University, Chengdu, China
| | - Fan Xu
- School of Public Health, Chengdu Medical College, Chengdu, China
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15
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Ensemble Learning-Based Reactive Power Optimization for Distribution Networks. ENERGIES 2022. [DOI: 10.3390/en15061966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Reactive power optimization of distribution networks is of great significance to improve power quality and reduce power loss. However, traditional methods for reactive power optimization of distribution networks either consume a lot of calculation time or have limited accuracy. In this paper, a novel data-driven-based approach is proposed to simultaneously improve the accuracy and reduce calculation time for reactive power optimization using ensemble learning. Specifically, k-fold cross-validation is used to train multiple sub-models, which are merged to obtain high-quality optimization results through the proposed ensemble framework. The simulation results show that the proposed approach outperforms popular baselines, such as light gradient boosting machine, convolutional neural network, case-based reasoning, and multi-layer perceptron. Moreover, the calculation time is much lower than the traditional heuristic methods, such as the genetic algorithm.
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16
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Rahman L, Hafejee A, Anantharanjit R, Wei W, Cordeiro MF. Accelerating precision ophthalmology: recent advances. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2022. [DOI: 10.1080/23808993.2022.2154146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Loay Rahman
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Healthcare NHS Trust, London, UK
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, UK
| | - Ammaarah Hafejee
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Healthcare NHS Trust, London, UK
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, UK
| | - Rajeevan Anantharanjit
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Healthcare NHS Trust, London, UK
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, UK
| | - Wei Wei
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Healthcare NHS Trust, London, UK
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, UK
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17
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Shurrab S, Duwairi R. Self-supervised learning methods and applications in medical imaging analysis: a survey. PeerJ Comput Sci 2022; 8:e1045. [PMID: 36091989 PMCID: PMC9455147 DOI: 10.7717/peerj-cs.1045] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 06/27/2022] [Indexed: 05/12/2023]
Abstract
The scarcity of high-quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement. Self-supervised learning is a recent training paradigm that enables learning robust representations without the need for human annotation which can be considered an effective solution for the scarcity of annotated medical data. This article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with a concentration on their applications in the field of medical imaging analysis. The article covers a set of the most recent self-supervised learning methods from the computer vision field as they are applicable to the medical imaging analysis and categorize them as predictive, generative, and contrastive approaches. Moreover, the article covers 40 of the most recent research papers in the field of self-supervised learning in medical imaging analysis aiming at shedding the light on the recent innovation in the field. Finally, the article concludes with possible future research directions in the field.
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Affiliation(s)
- Saeed Shurrab
- Department of Computer Information Systems, Jordan University of Science and Technology, Irbid, Jordan
| | - Rehab Duwairi
- Department of Computer Information Systems, Jordan University of Science and Technology, Irbid, Jordan
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18
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Pettit RW, Fullem R, Cheng C, Amos CI. Artificial intelligence, machine learning, and deep learning for clinical outcome prediction. Emerg Top Life Sci 2021; 5:ETLS20210246. [PMID: 34927670 PMCID: PMC8786279 DOI: 10.1042/etls20210246] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/03/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022]
Abstract
AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.
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Affiliation(s)
- Rowland W. Pettit
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
| | - Robert Fullem
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, U.S.A
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, U.S.A
| | - Christopher I. Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, U.S.A
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, U.S.A
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19
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Ursin F, Timmermann C, Orzechowski M, Steger F. Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent? Front Med (Lausanne) 2021; 8:695217. [PMID: 34368192 PMCID: PMC8333706 DOI: 10.3389/fmed.2021.695217] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 06/22/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting. Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a broad search on general issues of AI-based diagnosis. An ethics content analysis was conducted inductively to extract two features of included publications: (1) novel information content for AI-aided diagnosis and (2) the ethical justification for its disclosure. Results: The narrow search yielded n = 537 records of which n = 4 met the inclusion criteria. The information process was scarcely addressed for primary care setting. The broad search yielded n = 60 records of which n = 11 were included. In total, eight novel elements were identified to be included in the information process for ethical reasons, all of which stem from the technical specifics of medical AI. Conclusions: Implications for the general practitioner are two-fold: First, doctors need to be better informed about the ethical implications of novel technologies and must understand them to properly inform patients. Second, patient's overconfidence or fears can be countered by communicating the risks, limitations, and potential benefits of diagnostic AI systems. If patients accept and are aware of the limitations of AI-aided diagnosis, they increase their chances of being diagnosed and treated in time.
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Cheng J, Fu H, Cabrera DeBuc D, Tian J. Guest Editorial Ophthalmic Image Analysis and Informatics. IEEE J Biomed Health Inform 2020. [DOI: 10.1109/jbhi.2020.3037388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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