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Silva L, da Motta LG, Eberly L. Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning. REVISTA BRASILEIRA DE EPIDEMIOLOGIA 2024; 27:e240024. [PMID: 38747742 PMCID: PMC11093519 DOI: 10.1590/1980-549720240024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 02/17/2024] [Accepted: 03/06/2024] [Indexed: 05/19/2024] Open
Abstract
OBJECTIVE Tuberculosis (TB) is the second most deadly infectious disease globally, posing a significant burden in Brazil and its Amazonian region. This study focused on the "riverine municipalities" and hypothesizes the presence of TB clusters in the area. We also aimed to train a machine learning model to differentiate municipalities classified as hot spots vs. non-hot spots using disease surveillance variables as predictors. METHODS Data regarding the incidence of TB from 2019 to 2022 in the riverine town was collected from the Brazilian Health Ministry Informatics Department. Moran's I was used to assess global spatial autocorrelation, while the Getis-Ord GI* method was employed to detect high and low-incidence clusters. A Random Forest machine-learning model was trained using surveillance variables related to TB cases to predict hot spots among non-hot spot municipalities. RESULTS Our analysis revealed distinct geographical clusters with high and low TB incidence following a west-to-east distribution pattern. The Random Forest Classification model utilizes six surveillance variables to predict hot vs. non-hot spots. The machine learning model achieved an Area Under the Receiver Operator Curve (AUC-ROC) of 0.81. CONCLUSION Municipalities with higher percentages of recurrent cases, deaths due to TB, antibiotic regimen changes, percentage of new cases, and cases with smoking history were the best predictors of hot spots. This prediction method can be leveraged to identify the municipalities at the highest risk of being hot spots for the disease, aiding policymakers with an evidenced-based tool to direct resource allocation for disease control in the riverine municipalities.
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Affiliation(s)
- Luis Silva
- University of Minnesota, Minneapolis – Minneapolis (MN), United States
| | | | - Lynn Eberly
- University of Minnesota, Minneapolis – Minneapolis (MN), United States
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2
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Altulaihan E, Almaiah MA, Aljughaiman A. Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms. SENSORS (BASEL, SWITZERLAND) 2024; 24:713. [PMID: 38276404 PMCID: PMC10820271 DOI: 10.3390/s24020713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 01/27/2024]
Abstract
Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users' security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.
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Affiliation(s)
- Esra Altulaihan
- Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia;
| | - Mohammed Amin Almaiah
- King Abdullah the II IT School, The University of Jordan, Amman 11942, Jordan;
- Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan
- Department of Computer Science, Aqaba University of Technology, Aqaba 11191, Jordan
| | - Ahmed Aljughaiman
- Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia;
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3
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Raja Sankari VM, Snekhalatha U, Chandrasekaran A, Baskaran P. Automated diagnosis of Retinopathy of prematurity from retinal images of preterm infants using hybrid deep learning techniques. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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4
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Bakasa W, Viriri S. VGG16 Feature Extractor with Extreme Gradient Boost Classifier for Pancreas Cancer Prediction. J Imaging 2023; 9:138. [PMID: 37504815 PMCID: PMC10381878 DOI: 10.3390/jimaging9070138] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/19/2023] [Accepted: 07/04/2023] [Indexed: 07/29/2023] Open
Abstract
The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) is greatly improved by an early and accurate diagnosis. Several studies have created automated methods to forecast PDAC development utilising various medical imaging modalities. These papers give a general overview of the classification, segmentation, or grading of many cancer types utilising conventional machine learning techniques and hand-engineered characteristics, including pancreatic cancer. This study uses cutting-edge deep learning techniques to identify PDAC utilising computerised tomography (CT) medical imaging modalities. This work suggests that the hybrid model VGG16-XGBoost (VGG16-backbone feature extractor and Extreme Gradient Boosting-classifier) for PDAC images. According to studies, the proposed hybrid model performs better, obtaining an accuracy of 0.97 and a weighted F1 score of 0.97 for the dataset under study. The experimental validation of the VGG16-XGBoost model uses the Cancer Imaging Archive (TCIA) public access dataset, which has pancreas CT images. The results of this study can be extremely helpful for PDAC diagnosis from computerised tomography (CT) pancreas images, categorising them into five different tumours (T), node (N), and metastases (M) (TNM) staging system class labels, which are T0, T1, T2, T3, and T4.
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Affiliation(s)
- Wilson Bakasa
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4041, South Africa
| | - Serestina Viriri
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4041, South Africa
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5
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Fang Y, Chen M, Zhu B. Construction and evaluation of in-house methylation-sensitive SNaPshot system and three classification prediction models for identifying the tissue origin of body fluid. J Zhejiang Univ Sci B 2023; 24:839-852. [PMID: 37701959 PMCID: PMC10500097 DOI: 10.1631/jzus.b2200555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 03/06/2023] [Indexed: 06/27/2023]
Abstract
The identification of tissue origin of body fluid can provide clues and evidence for criminal case investigations. To establish an efficient method for identifying body fluid in forensic cases, eight novel body fluid-specific DNA methylation markers were selected in this study, and a multiplex singlebase extension reaction (SNaPshot) system for these markers was constructed for the identification of five common body fluids (venous blood, saliva, menstrual blood, vaginal fluid, and semen). The results indicated that the in-house system showed good species specificity, sensitivity, and ability to identify mixed biological samples. At the same time, an artificial body fluid prediction model and two machine learning prediction models based on the support vector machine (SVM) and random forest (RF) algorithms were constructed using previous research data, and these models were validated using the detection data obtained in this study (n=95). The accuracy of the prediction model based on experience was 95.79%; the prediction accuracy of the SVM prediction model was 100.00% for four kinds of body fluids except saliva (96.84%); and the prediction accuracy of the RF prediction model was 100.00% for all five kinds of body fluids. In conclusion, the in-house SNaPshot system and RF prediction model could achieve accurate tissue origin identification of body fluids.
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Affiliation(s)
- Yating Fang
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230031, China
| | - Man Chen
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China
| | - Bofeng Zhu
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China.
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China.
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6
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Villaplana-Velasco A, Pigeyre M, Engelmann J, Rawlik K, Canela-Xandri O, Tochel C, Lona-Durazo F, Mookiah MRK, Doney A, Parra EJ, Trucco E, MacGillivray T, Rannikmae K, Tenesa A, Pairo-Castineira E, Bernabeu MO. Fine-mapping of retinal vascular complexity loci identifies Notch regulation as a shared mechanism with myocardial infarction outcomes. Commun Biol 2023; 6:523. [PMID: 37188768 PMCID: PMC10185685 DOI: 10.1038/s42003-023-04836-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
There is increasing evidence that the complexity of the retinal vasculature measured as fractal dimension, Df, might offer earlier insights into the progression of coronary artery disease (CAD) before traditional biomarkers can be detected. This association could be partly explained by a common genetic basis; however, the genetic component of Df is poorly understood. We present a genome-wide association study (GWAS) of 38,000 individuals with white British ancestry from the UK Biobank aimed to comprehensively study the genetic component of Df and analyse its relationship with CAD. We replicated 5 Df loci and found 4 additional loci with suggestive significance (P < 1e-05) to contribute to Df variation, which previously were reported in retinal tortuosity and complexity, hypertension, and CAD studies. Significant negative genetic correlation estimates support the inverse relationship between Df and CAD, and between Df and myocardial infarction (MI), one of CAD's fatal outcomes. Fine-mapping of Df loci revealed Notch signalling regulatory variants supporting a shared mechanism with MI outcomes. We developed a predictive model for MI incident cases, recorded over a 10-year period following clinical and ophthalmic evaluation, combining clinical information, Df, and a CAD polygenic risk score. Internal cross-validation demonstrated a considerable improvement in the area under the curve (AUC) of our predictive model (AUC = 0.770 ± 0.001) when comparing with an established risk model, SCORE, (AUC = 0.741 ± 0.002) and extensions thereof leveraging the PRS (AUC = 0.728 ± 0.001). This evidences that Df provides risk information beyond demographic, lifestyle, and genetic risk factors. Our findings shed new light on the genetic basis of Df, unveiling a common control with MI, and highlighting the benefits of its application in individualised MI risk prediction.
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Affiliation(s)
- Ana Villaplana-Velasco
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Marie Pigeyre
- Population Health Research Institute (PHRI), Department of Medicine, Faculty of Health Sciences, McMaster University, McMaster University, Hamilton, Ontario, Canada
| | - Justin Engelmann
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Konrad Rawlik
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit, IGC, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Claire Tochel
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | | | | | - Alex Doney
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, Scotland, UK
| | - Esteban J Parra
- University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Emanuele Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, Scotland, UK
| | - Tom MacGillivray
- VAMPIRE project, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Kristiina Rannikmae
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Albert Tenesa
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK
- MRC Human Genetics Unit, IGC, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Erola Pairo-Castineira
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Miguel O Bernabeu
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, Scotland, UK.
- The Bayes Centre, The University of Edinburgh, Edinburgh, Scotland, UK.
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7
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Bhakar S, Sinwar D, Pradhan N, Dhaka VS, Cherrez-Ojeda I, Parveen A, Hassan MU. Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains. Diagnostics (Basel) 2023; 13:diagnostics13071212. [PMID: 37046431 PMCID: PMC10093052 DOI: 10.3390/diagnostics13071212] [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: 01/29/2023] [Revised: 03/06/2023] [Accepted: 03/08/2023] [Indexed: 04/14/2023] Open
Abstract
Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Artificial intelligence and deep-learning-assisted approaches are proving to be significant in the rapid and accurate diagnosis of several diseases. In addition to disease identification, these approaches have the potential to identify the severity of a disease. The problem of disease severity identification can be considered multi-class classification, where the class labels are the severity levels of the disease. Plenty of computational intelligence-based solutions have been presented by researchers for severity identification. This paper presents a comprehensive review of recent approaches for identifying disease severity levels using computational intelligence-based approaches. We followed the PRISMA guidelines and compiled several works related to the severity identification of multidisciplinary diseases of the last decade from well-known publishers, such as MDPI, Springer, IEEE, Elsevier, etc. This article is devoted toward the severity identification of two main diseases, viz. Parkinson's Disease and Diabetic Retinopathy. However, severity identification of a few other diseases, such as COVID-19, autonomic nervous system dysfunction, tuberculosis, sepsis, sleep apnea, psychosis, traumatic brain injury, breast cancer, knee osteoarthritis, and Alzheimer's disease, was also briefly covered. Each work has been carefully examined against its methodology, dataset used, and the type of disease on several performance metrics, accuracy, specificity, etc. In addition to this, we also presented a few public repositories that can be utilized to conduct research on disease severity identification. We hope that this review not only acts as a compendium but also provides insights to the researchers working on disease severity identification using computational intelligence-based approaches.
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Affiliation(s)
- Suman Bhakar
- Department of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
| | - Deepak Sinwar
- Department of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
| | - Nitesh Pradhan
- Department of Computer Science and Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
| | - Vijaypal Singh Dhaka
- Department of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
| | - Ivan Cherrez-Ojeda
- Allergy and Pulmonology, Espíritu Santo University, Samborondón 0901-952, Ecuador
| | - Amna Parveen
- College of Pharmacy, Gachon University, Medical Campus, No. 191, Hambakmoero, Yeonsu-gu, Incheon 21936, Republic of Korea
| | - Muhammad Umair Hassan
- Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway
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8
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Liu ZW, Chen G, Dong CF, Qiu WR, Zhang SH. Intelligent assistant diagnosis for pediatric inguinal hernia based on a multilayer and unbalanced classification model. Front Physiol 2023; 14:1105891. [PMID: 36998990 PMCID: PMC10043203 DOI: 10.3389/fphys.2023.1105891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 02/27/2023] [Indexed: 03/17/2023] Open
Abstract
As one of the most common diseases in pediatric surgery, an inguinal hernia is usually diagnosed by medical experts based on clinical data collected from magnetic resonance imaging (MRI), computed tomography (CT), or B-ultrasound. The parameters of blood routine examination, such as white blood cell count and platelet count, are often used as diagnostic indicators of intestinal necrosis. Based on the medical numerical data on blood routine examination parameters and liver and kidney function parameters, this paper used machine learning algorithm to assist the diagnosis of intestinal necrosis in children with inguinal hernia before operation. In the work, we used clinical data consisting of 3,807 children with inguinal hernia symptoms and 170 children with intestinal necrosis and perforation caused by the disease. Three different models were constructed according to the blood routine examination and liver and kidney function. Some missing values were replaced by using the RIN-3M (median, mean, or mode region random interpolation) method according to the actual necessity, and the ensemble learning based on the voting principle was used to deal with the imbalanced datasets. The model trained after feature selection yielded satisfactory results with an accuracy of 86.43%, sensitivity of 84.34%, specificity of 96.89%, and AUC value of 0.91. Therefore, the proposed methods may be a potential idea for auxiliary diagnosis of inguinal hernia in children.
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Affiliation(s)
- Zhi-Wen Liu
- Department of General Surgery, Jiangxi Provincial Children’s Hospital, Nanchang, China
| | - Gang Chen
- Computer Department, Jing-De-Zhen Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Chao-Fan Dong
- Department of General Surgery, Jingdezhen No. 1 People’s Hospital, Jingdezhen, China
| | - Wang-Ren Qiu
- Computer Department, Jing-De-Zhen Jingdezhen Ceramic Institute, Jingdezhen, China
- *Correspondence: Wang-Ren Qiu, , ; Shou-Hua Zhang,
| | - Shou-Hua Zhang
- Department of General Surgery, Jiangxi Provincial Children’s Hospital, Nanchang, China
- *Correspondence: Wang-Ren Qiu, , ; Shou-Hua Zhang,
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9
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Innovation in Hyperinsulinemia Diagnostics with ANN-L( atin square) Models. Diagnostics (Basel) 2023; 13:diagnostics13040798. [PMID: 36832286 PMCID: PMC9955502 DOI: 10.3390/diagnostics13040798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/11/2023] [Accepted: 02/12/2023] [Indexed: 02/22/2023] Open
Abstract
Hyperinsulinemia is a condition characterized by excessively high levels of insulin in the bloodstream. It can exist for many years without any symptomatology. The research presented in this paper was conducted from 2019 to 2022 in cooperation with a health center in Serbia as a large cross-sectional observational study of adolescents of both genders using datasets collected from the field. Previously used analytical approaches of integrated and relevant clinical, hematological, biochemical, and other variables could not identify potential risk factors for developing hyperinsulinemia. This paper aims to present several different models using machine learning (ML) algorithms such as naive Bayes, decision tree, and random forest and compare them with a new methodology constructed based on artificial neural networks using Taguchi's orthogonal vector plans (ANN-L), a special extraction of Latin squares. Furthermore, the experimental part of this study showed that ANN-L models achieved an accuracy of 99.5% with less than seven iterations performed. Furthermore, the study provides valuable insights into the share of each risk factor contributing to the occurrence of hyperinsulinemia in adolescents, which is crucial for more precise and straightforward medical diagnoses. Preventing the risk of hyperinsulinemia in this age group is crucial for the well-being of the adolescents and society as a whole.
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10
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Afriyie Y, Weyori BA, Opoku AA. A scaling up approach: a research agenda for medical imaging analysis with applications in deep learning. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2165721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Yaw Afriyie
- Department of Computer Science and Informatics, University of Energy and Natural Resources, School of Sciences, Sunyani, Ghana
- Department of Computer Science, Faculty of Information and Communication Technology, SD Dombo University of Business and Integrated Development Studies, Wa, Ghana
| | - Benjamin A. Weyori
- Department of Computer Science and Informatics, University of Energy and Natural Resources, School of Sciences, Sunyani, Ghana
| | - Alex A. Opoku
- Department of Mathematics & Statistics, University of Energy and Natural Resources, School of Sciences, Sunyani, Ghana
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Selvachandran G, Quek SG, Paramesran R, Ding W, Son LH. Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods. Artif Intell Rev 2023; 56:915-964. [PMID: 35498558 PMCID: PMC9038999 DOI: 10.1007/s10462-022-10185-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2022] [Indexed: 02/02/2023]
Abstract
The exponential increase in the number of diabetics around the world has led to an equally large increase in the number of diabetic retinopathy (DR) cases which is one of the major complications caused by diabetes. Left unattended, DR worsens the vision and would lead to partial or complete blindness. As the number of diabetics continue to increase exponentially in the coming years, the number of qualified ophthalmologists need to increase in tandem in order to meet the demand for screening of the growing number of diabetic patients. This makes it pertinent to develop ways to automate the detection process of DR. A computer aided diagnosis system has the potential to significantly reduce the burden currently placed on the ophthalmologists. Hence, this review paper is presented with the aim of summarizing, classifying, and analyzing all the recent development on automated DR detection using fundus images from 2015 up to this date. Such work offers an unprecedentedly thorough review of all the recent works on DR, which will potentially increase the understanding of all the recent studies on automated DR detection, particularly on those that deploys machine learning algorithms. Firstly, in this paper, a comprehensive state-of-the-art review of the methods that have been introduced in the detection of DR is presented, with a focus on machine learning models such as convolutional neural networks (CNN) and artificial neural networks (ANN) and various hybrid models. Each AI will then be classified according to its type (e.g. CNN, ANN, SVM), its specific task(s) in performing DR detection. In particular, the models that deploy CNN will be further analyzed and classified according to some important properties of the respective CNN architectures of each model. A total of 150 research articles related to the aforementioned areas that were published in the recent 5 years have been utilized in this review to provide a comprehensive overview of the latest developments in the detection of DR. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-022-10185-6.
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Affiliation(s)
- Ganeshsree Selvachandran
- Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia
| | - Shio Gai Quek
- Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia
| | - Raveendran Paramesran
- Institute of Computer Science and Digital Innovation, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia
| | - Weiping Ding
- School of Information Science and Technology, Nantong University, Nantong, 226019 People’s Republic of China
| | - Le Hoang Son
- VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam
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12
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Dubey S, Dixit M. Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:14471-14525. [PMID: 36185322 PMCID: PMC9510498 DOI: 10.1007/s11042-022-13841-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 04/27/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
Diabetes is a long-term condition in which the pancreas quits producing insulin or the body's insulin isn't utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models.
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Affiliation(s)
- Shradha Dubey
- Madhav Institute of Technology & Science (Department of Computer Science and Engineering), Gwalior, M.P. India
| | - Manish Dixit
- Madhav Institute of Technology & Science (Department of Computer Science and Engineering), Gwalior, M.P. India
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Mishra A, Singh L, Pandey M, Lakra S. Image based early detection of diabetic retinopathy: A systematic review on Artificial Intelligence (AI) based recent trends and approaches. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-220772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Diabetic Retinopathy (DR) is a disease that damages the retina of the human eye due to diabetic complications, resulting in a loss of vision. Blindness may be avoided If the DR disease is detected at an early stage. Unfortunately, DR is irreversible process, however, early detection and treatment of DR can significantly reduce the risk of vision loss. The manual diagnosis done by ophthalmologists on DR retina fundus images is time consuming, and error prone process. Nowadays, machine learning and deep learning have become one of the most effective approaches, which have even surpassed the human performance as well as performance of traditional image processing-based algorithms and other computer aided diagnosis systems in the analysis and classification of medical images. This paper addressed and evaluated the various recent state-of-the-art methodologies that have been used for detection and classification of Diabetic Retinopathy disease using machine learning and deep learning approaches in the past decade. Furthermore, this study also provides the authors observation and performance evaluation of available research using several parameters, such as accuracy, disease status, and sensitivity. Finally, we conclude with limitations, remedies, and future directions in DR detection. In addition, various challenging issues that need further study are also discussed.
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Affiliation(s)
- Anju Mishra
- Manav Rachna University, Faridabad, Haryana, India
| | - Laxman Singh
- Noida Institute of Engineering and Technology, Greater Noida, U.P, India
| | | | - Sachin Lakra
- Manav Rachna University, Faridabad, Haryana, India
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14
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Simon CGK, Jhanjhi NZ, Goh WW, Sukumaran S. Applications of Machine Learning in Knowledge Management System: A Comprehensive Review. JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2022. [DOI: 10.1142/s0219649222500174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
As new generations of technology appear, legacy knowledge management solutions and applications become increasingly out of date, necessitating a paradigm shift. Machine learning presents an opportunity by foregoing rule-based knowledge intensive systems inundating the marketplace. An extensive review was made on the literature pertaining to machine learning which common machine learning algorithms were identified. This study has analysed more than 200 papers extracted from Scopus and IEEE databases. Searches ranged with the bulk of the articles from 2018 to 2021, while some articles ranged from 1959 to 2017. The research gap focusses on implementing machine learning algorithm to knowledge management systems, specifically knowledge management attributes. By investigating and reviewing each algorithm extensively, the usability of each algorithm is identified, with its advantages and disadvantages. From there onwards, these algorithms were mapped for what area of knowledge management it may be beneficial. Based on the findings, it is evidently seen how these algorithms are applicable in knowledge management and how it can enhance knowledge management system further. Based on the findings, the paper aims to bridge the gap between the literature in knowledge management and machine learning. A knowledge management–machine learning framework is conceived based on the review done on each algorithm earlier and to bridge the gap between the two literatures. The framework highlights how machine learning algorithm can play a part in different areas of knowledge management. From the framework, it provides practitioners how and where to implement machine learning in knowledge management.
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Affiliation(s)
| | - Noor Zaman Jhanjhi
- Taylor’s University, 1, Jalan Taylors 47500 Subang Jaya, Selangor, Malaysia
| | - Wei Wei Goh
- Taylor’s University, 1, Jalan Taylors 47500 Subang Jaya, Selangor, Malaysia
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15
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Automated method for real-time AMD screening of fundus images dedicated for mobile devices. Med Biol Eng Comput 2022; 60:1449-1479. [DOI: 10.1007/s11517-022-02546-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 03/06/2022] [Indexed: 01/01/2023]
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16
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Automatic Fingerprint Classification Using Deep Learning Technology (DeepFKTNet). MATHEMATICS 2022. [DOI: 10.3390/math10081285] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Fingerprints are gaining in popularity, and fingerprint datasets are becoming increasingly large. They are often captured utilizing a variety of sensors embedded in smart devices such as mobile phones and personal computers. One of the primary issues with fingerprint recognition systems is their high processing complexity, which is exacerbated when they are gathered using several sensors. One way to address this issue is to categorize fingerprints in a database to condense the search space. Deep learning is effective in designing robust fingerprint classification methods. However, designing the architecture of a CNN model is a laborious and time-consuming task. We proposed a technique for automatically determining the architecture of a CNN model adaptive to fingerprint classification; it automatically determines the number of filters and the layers using Fukunaga–Koontz transform and the ratio of the between-class scatter to within-class scatter. It helps to design lightweight CNN models, which are efficient and speed up the fingerprint recognition process. The method was evaluated two public-domain benchmark datasets FingerPass and FVC2004 benchmark datasets, which contain noisy, low-quality fingerprints obtained using live scan devices and cross-sensor fingerprints. The designed models outperform the well-known pre-trained models and the state-of-the-art fingerprint classification techniques.
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17
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Joint DR-DME classification using deep learning-CNN based modified grey-wolf optimizer with variable weights. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103439] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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18
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Kishore Kumar A, Udhayakumar A, Kalaiselvi K. Convolutional Neural Networks Based Classifier for Diabetic Retinopathy. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2022. [DOI: 10.1166/jmihi.2022.3932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Diabetic Retinopathy (DR) is a consequence of diabetes which causes damage to the retinal blood vessel networks. In most diabetics, this is a major vision-threatening problem. Color fundus pictures are used to diagnose DR, which requires competent doctors to determine lesions presence.
The job of detecting DR in an automated manner is difficult. In terms of automated illness identification, feature extraction is quite useful. In the current setting, Convolutional Neural Networks (CNN) outperforms prior handcrafted feature-based image classification approaches in terms of
image classification efficiency. This paper introduces CNN structure for extracting characteristics from retinal fundus pictures in order to develop the accuracy of classification. This proposed method, the output features of CNN are employed as input to many classifiers of machine learning.
Using images from the MESSIDOR datasets, this method is tested under Random Tree, Hoeffiding Tree and Random Forest classifiers. Accuracy, False Positive Rate (FPR), Precision, Recall, F-1 score, specificity and Kappa-score for used classifiers are compared to find out the efficiency of the
classifier. For the MESSIDOR datasets, the suggested feature extraction approach combined with the Random forest classifier surpasses all other classifiers which gains 88% and 0.7288 of average accuracy and Kappa-score (k-score) respectively.
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Affiliation(s)
- A. Kishore Kumar
- Department of Robotics and Automation, Sri Ramakrishna Engineering College, Coimbatore 641022, Tamilnadu, India
| | - A. Udhayakumar
- Department of ECE, Hindustan College of Engineering and Technology, Coimbatore 641022, Tamilnadu, India
| | - K. Kalaiselvi
- Department of ECE, Hindustan College of Engineering and Technology, Coimbatore 641022, Tamilnadu, India
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Hossain MM, Hasan MM, Rahim MA, Rahman MM, Yousuf MA, Al-Ashhab S, Akhdar HF, Alyami SA, Azad A, Moni MA. Particle Swarm Optimized Fuzzy CNN With Quantitative Feature Fusion for Ultrasound Image Quality Identification. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 10:1800712. [PMID: 36226132 PMCID: PMC9550163 DOI: 10.1109/jtehm.2022.3197923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 07/04/2022] [Accepted: 08/03/2022] [Indexed: 11/07/2022]
Abstract
Inherently ultrasound images are susceptible to noise which leads to several image quality issues. Hence, rating of an image’s quality is crucial since diagnosing diseases requires accurate and high-quality ultrasound images. This research presents an intelligent architecture to rate the quality of ultrasound images. The formulated image quality recognition approach fuses feature from a Fuzzy convolutional neural network (fuzzy CNN) and a handcrafted feature extraction method. We implement the fuzzy layer in between the last max pooling and the fully connected layer of the multiple state-of-the-art CNN models to handle the uncertainty of information. Moreover, the fuzzy CNN uses Particle swarm optimization (PSO) as an optimizer. In addition, a novel Quantitative feature extraction machine (QFEM) extracts hand-crafted features from ultrasound images. Next, the proposed method uses different classifiers to predict the image quality. The classifiers categories ultrasound images into four types (normal, noisy, blurry, and distorted) instead of binary classification into good or poor-quality images. The results of the proposed method exhibit a significant performance in accuracy (99.62%), precision (99.62%), recall (99.61%), and f1-score (99.61%). This method will assist a physician in automatically rating informative ultrasound images with steadfast operation in real-time medical diagnosis.
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Affiliation(s)
- Muhammad Minoar Hossain
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Md. Mahmodul Hasan
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Md. Abdur Rahim
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Mohammad Motiur Rahman
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Mohammad Abu Yousuf
- Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh
| | - Samer Al-Ashhab
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Hanan F. Akhdar
- Department of Physics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Salem A. Alyami
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Akm Azad
- Faculty of Science, Engineering and Technology, Swinburne University of Technology Sydney, Parramatta, NSW, Australia
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, QLD, Australia
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20
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Ding M, Pan SY, Huang J, Yuan C, Zhang Q, Zhu XL, Cai Y. Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm. PLoS One 2021; 16:e0260600. [PMID: 34971557 PMCID: PMC8719667 DOI: 10.1371/journal.pone.0260600] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 11/14/2021] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE To explore the feasibility of using random forest (RF) machine learning algorithm in assessing normal and malignant peripheral pulmonary nodules based on in vivo endobronchial optical coherence tomography (EB-OCT). METHODS A total of 31 patients with pulmonary nodules were admitted to Department of Respiratory Medicine, Zhongda Hospital, Southeast University, and underwent chest CT, EB-OCT and biopsy. Attenuation coefficient and up to 56 different image features were extracted from A-line and B-scan of 1703 EB-OCT images. Attenuation coefficient and 29 image features with significant p-values were used to analyze the differences between normal and malignant samples. A RF classifier was trained using 70% images as training set, while 30% images were included in the testing set. The accuracy of the automated classification was validated by clinically proven pathological results. RESULTS Attenuation coefficient and 29 image features were found to present different properties with significant p-values between normal and malignant EB-OCT images. The RF algorithm successfully classified the malignant pulmonary nodules with sensitivity, specificity, and accuracy of 90.41%, 77.87% and 83.51% respectively. CONCLUSION It is clinically practical to distinguish the nature of pulmonary nodules by integrating EB-OCT imaging with automated machine learning algorithm. Diagnosis of malignant pulmonary nodules by analyzing quantitative features from EB-OCT images could be a potentially powerful way for early detection of lung cancer.
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Affiliation(s)
- Ming Ding
- Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China
| | - Shi-yu Pan
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China
| | - Jing Huang
- Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China
| | - Cheng Yuan
- Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China
| | - Qiang Zhang
- Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China
| | - Xiao-li Zhu
- Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China
| | - Yan Cai
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China
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21
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Wu JH, Liu TYA, Hsu WT, Ho JHC, Lee CC. Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis. J Med Internet Res 2021; 23:e23863. [PMID: 34407500 PMCID: PMC8406115 DOI: 10.2196/23863] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 11/19/2020] [Accepted: 04/30/2021] [Indexed: 12/23/2022] Open
Abstract
Background Diabetic retinopathy (DR), whose standard diagnosis is performed by human experts, has high prevalence and requires a more efficient screening method. Although machine learning (ML)–based automated DR diagnosis has gained attention due to recent approval of IDx-DR, performance of this tool has not been examined systematically, and the best ML technique for use in a real-world setting has not been discussed. Objective The aim of this study was to systematically examine the overall diagnostic accuracy of ML in diagnosing DR of different categories based on color fundus photographs and to determine the state-of-the-art ML approach. Methods Published studies in PubMed and EMBASE were searched from inception to June 2020. Studies were screened for relevant outcomes, publication types, and data sufficiency, and a total of 60 out of 2128 (2.82%) studies were retrieved after study selection. Extraction of data was performed by 2 authors according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), and the quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis of diagnostic accuracy was pooled using a bivariate random effects model. The main outcomes included diagnostic accuracy, sensitivity, and specificity of ML in diagnosing DR based on color fundus photographs, as well as the performances of different major types of ML algorithms. Results The primary meta-analysis included 60 color fundus photograph studies (445,175 interpretations). Overall, ML demonstrated high accuracy in diagnosing DR of various categories, with a pooled area under the receiver operating characteristic (AUROC) ranging from 0.97 (95% CI 0.96-0.99) to 0.99 (95% CI 0.98-1.00). The performance of ML in detecting more-than-mild DR was robust (sensitivity 0.95; AUROC 0.97), and by subgroup analyses, we observed that robust performance of ML was not limited to benchmark data sets (sensitivity 0.92; AUROC 0.96) but could be generalized to images collected in clinical practice (sensitivity 0.97; AUROC 0.97). Neural network was the most widely used method, and the subgroup analysis revealed a pooled AUROC of 0.98 (95% CI 0.96-0.99) for studies that used neural networks to diagnose more-than-mild DR. Conclusions This meta-analysis demonstrated high diagnostic accuracy of ML algorithms in detecting DR on color fundus photographs, suggesting that state-of-the-art, ML-based DR screening algorithms are likely ready for clinical applications. However, a significant portion of the earlier published studies had methodology flaws, such as the lack of external validation and presence of spectrum bias. The results of these studies should be interpreted with caution.
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Affiliation(s)
- Jo-Hsuan Wu
- Shiley Eye Institute and Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, CA, United States
| | - T Y Alvin Liu
- Retina Division, Wilmer Eye Institute, The Johns Hopkins Medicine, Baltimore, MD, United States
| | - Wan-Ting Hsu
- Harvard TH Chan School of Public Health, Boston, MA, United States
| | | | - Chien-Chang Lee
- Health Data Science Research Group, National Taiwan University Hospital, Taipei, Taiwan.,The Centre for Intelligent Healthcare, National Taiwan University Hospital, Taipei, Taiwan.,Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan
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22
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Fernandez-Lozano C, Hervella P, Mato-Abad V, Rodríguez-Yáñez M, Suárez-Garaboa S, López-Dequidt I, Estany-Gestal A, Sobrino T, Campos F, Castillo J, Rodríguez-Yáñez S, Iglesias-Rey R. Random forest-based prediction of stroke outcome. Sci Rep 2021; 11:10071. [PMID: 33980906 PMCID: PMC8115135 DOI: 10.1038/s41598-021-89434-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 04/26/2021] [Indexed: 11/09/2022] Open
Abstract
We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3-months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p-value < 2.2e-16). Conditions required for a parametric test do not hold, and we performed a paired Wilcoxon Test assuming the null hypothesis that all the groups have the same performance. The null hypothesis was rejected with a value < 2.2e-16, so there are statistical differences between IS and ICH groups. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.
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Affiliation(s)
- Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, Universidade da Coruña, A Coruña, Spain.,Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR). Instituto de Investigación Biomédica de A Coruña (INIBIC). Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Universidade da Coruña, A Coruña, Spain
| | - Pablo Hervella
- Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Virginia Mato-Abad
- Software Engineering Laboratory, Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
| | - Manuel Rodríguez-Yáñez
- Stroke Unit, Department of Neurology, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario, Rúa Travesa da Choupana, s/n, 15706Santiago de Compostela, Spain
| | - Sonia Suárez-Garaboa
- Software Engineering Laboratory, Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
| | - Iria López-Dequidt
- Stroke Unit, Department of Neurology, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario, Rúa Travesa da Choupana, s/n, 15706Santiago de Compostela, Spain
| | - Ana Estany-Gestal
- Unit of Methodology of the Research, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Tomás Sobrino
- Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Francisco Campos
- Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - José Castillo
- Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Santiago Rodríguez-Yáñez
- Software Engineering Laboratory, Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain.
| | - Ramón Iglesias-Rey
- Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.
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23
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Yang L, Qin Y, Jian C. Screening for Core Genes Related to Pathogenesis of Alzheimer's Disease. Front Cell Dev Biol 2021; 9:668738. [PMID: 33968940 PMCID: PMC8101499 DOI: 10.3389/fcell.2021.668738] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 04/01/2021] [Indexed: 12/18/2022] Open
Abstract
Alzheimer’s disease (AD), a nervous system disease, lacks effective therapies at present. RNA expression is the basic way to regulate life activities, and identifying related characteristics in AD patients may aid the exploration of AD pathogenesis and treatment. This study developed a classifier that could accurately classify AD patients and healthy people, and then obtained 3 core genes that may be related to the pathogenesis of AD. To this end, RNA expression data of the middle temporal gyrus of AD patients were firstly downloaded from GEO database, and the data were then normalized using limma package following a supplementation of missing data by k-Nearest Neighbor (KNN) algorithm. Afterwards, the top 500 genes of the most feature importance were obtained through Max-Relevance and Min-Redundancy (mRMR) analysis, and based on these genes, a series of AD classifiers were constructed through Support Vector Machine (SVM), Random Forest (RF), and KNN algorithms. Then, the KNN classifier with the highest Matthews correlation coefficient (MCC) value composed of 14 genes in incremental feature selection (IFS) analysis was identified as the best AD classifier. As analyzed, the 14 genes played a pivotal role in determination of AD and may be core genes associated with the pathogenesis of AD. Finally, protein-protein interaction (PPI) network and Random Walk with Restart (RWR) analysis were applied to obtain core gene-associated genes, and key pathways related to AD were further analyzed. Overall, this study contributed to a deeper understanding of AD pathogenesis and provided theoretical guidance for related research and experiments.
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Affiliation(s)
- Longxiu Yang
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yuan Qin
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chongdong Jian
- Department of Neurology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
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24
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Bilal A, Sun G, Mazhar S. Survey on recent developments in automatic detection of diabetic retinopathy. J Fr Ophtalmol 2021; 44:420-440. [PMID: 33526268 DOI: 10.1016/j.jfo.2020.08.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 08/24/2020] [Indexed: 12/13/2022]
Abstract
Diabetic retinopathy (DR) is a disease facilitated by the rapid spread of diabetes worldwide. DR can blind diabetic individuals. Early detection of DR is essential to restoring vision and providing timely treatment. DR can be detected manually by an ophthalmologist, examining the retinal and fundus images to analyze the macula, morphological changes in blood vessels, hemorrhage, exudates, and/or microaneurysms. This is a time consuming, costly, and challenging task. An automated system can easily perform this function by using artificial intelligence, especially in screening for early DR. Recently, much state-of-the-art research relevant to the identification of DR has been reported. This article describes the current methods of detecting non-proliferative diabetic retinopathy, exudates, hemorrhage, and microaneurysms. In addition, the authors point out future directions in overcoming current challenges in the field of DR research.
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Affiliation(s)
- A Bilal
- Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing 100124, China.
| | - G Sun
- Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing 100124, China
| | - S Mazhar
- Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing 100124, China
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Roy Chowdhury A, Banerjee S, Chatterjee T. A cybernetic systems approach to abnormality detection in retina images using case based reasoning. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-3187-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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26
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Anoop V, Bipin PR. Super-Resolution Based Automatic Diagnosis of Retinal Disease Detection for Clinical Applications. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10292-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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27
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Bazaga A, Roldán M, Badosa C, Jiménez-Mallebrera C, Porta JM. A Convolutional Neural Network for the automatic diagnosis of collagen VI-related muscular dystrophies. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105772] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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28
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A reliable method for colorectal cancer prediction based on feature selection and support vector machine. Med Biol Eng Comput 2018; 57:901-912. [PMID: 30478811 DOI: 10.1007/s11517-018-1930-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Accepted: 11/17/2018] [Indexed: 02/07/2023]
Abstract
Colorectal cancer (CRC) is a common cancer responsible for approximately 600,000 deaths per year worldwide. Thus, it is very important to find the related factors and detect the cancer accurately. However, timely and accurate prediction of the disease is challenging. In this study, we build an integrated model based on logistic regression (LR) and support vector machine (SVM) to classify the CRC into cancer and normal samples. From various factors, human location, age, gender, BMI, and cancer tumor type, tumor grade, and DNA, of the cancer, we select the most significant factors (p < 0.05) using logistic regression as main features, and with these features, a grid-search SVM model is designed using different kernel types (Linear, radial basis function (RBF), Sigmoid, and Polynomial). The result of the logistic regression indicates that the Firmicutes (AUC 0.918), Bacteroidetes (AUC 0.856), body mass index (BMI) (AUC 0.777), and age (AUC 0.710) and their combined factors (AUC 0.942) are effective for CRC detection. And the best kernel type is RBF, which achieves an accuracy of 90.1% when k = 5, and 91.2% when k = 10. This study provides a new method for colorectal cancer prediction based on independent risky factors. Graphical abstract Flow chart depicting the method adopted in the study. LR (logistic regression) and ROC curve are used to select independent features as input of SVM. SVM kernel selection aims to find the best kernel function for classification by comparing Linear, RBF, Sigmoid, and Polynomial kernel types of SVM, and the result shows the best kernel is RBF. Classification performance of LR + RF, LR + NB, LR + KNN, and LR + ANNs models are compared with LR + SVM. After these steps, the cancer and healthy individuals can be classified, and the best model is selected.
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