1
|
Qian Y, Tao Y, Wu L, Zhou C, Liu F, Xu S, Miao H, Gao X, Ge X. Model based on the automated AI-driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia. Sci Rep 2024; 14:16172. [PMID: 39003340 PMCID: PMC11246496 DOI: 10.1038/s41598-024-67255-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 07/09/2024] [Indexed: 07/15/2024] Open
Abstract
The prediction of refractory Mycoplasma pneumoniae pneumonia (RMPP) remains a clinically significant challenge. This study aimed to develop an early predictive model utilizing artificial intelligence (AI)-derived quantitative assessment of lung lesion extent on initial computed tomography (CT) scans and clinical indicators for RMPP in pediatric inpatients. A retrospective cohort study was conducted on patients with M. pneumoniae pneumonia (MP) admitted to the Children's Hospital of Nanjing Medical University, China from January 2019 to December 2020. An early prediction model was developed by stratifying the patients with Mycoplasma pneumoniae pneumonia (MPP) into two cohorts according to the presence or absence of refractory pneumonia. A retrospective cohort of 126 children diagnosed with Mycoplasma pneumoniae pneumonia (MPP) was utilized as a training set, with 85 cases classified as RMPP. Subsequently, a prospective cohort comprising 54 MPP cases, including 37 instances of RMPP, was assembled as a validation set to assess the performance of the predictive model for RMPP from January to December 2021. We defined a constant Φ which can combine the volume and CT value of pulmonary lesions and be further used to calculate the logarithm of Φ to the base of 2 (Log2Φ). A clinical-imaging prediction model was then constructed utilizing Log2Φ and clinical characteristics. Performance was evaluated by the area under the receiver operating characteristic curve (AUC). The clinical model demonstrated AUC values of 0.810 and 0.782, while the imaging model showed AUC values of 0.764 and 0.769 in the training and test sets, respectively. The clinical-imaging model, incorporating Log2Φ, temperature(T), aspartate aminotransferase (AST), preadmission fever duration (PFD), and preadmission macrolides therapy duration (PMTD), achieved the highest AUC values of 0.897 and 0.895 in the training and test sets, respectively. A prognostic model developed through automated quantification of lung disease on CT scans, in conjunction with clinical data in MPP may be utilized for the early identification of RMPP.
Collapse
Affiliation(s)
- Yali Qian
- Department of Emergency/Critical Medicine, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yunxi Tao
- School of Pediatrics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Lihui Wu
- Department of Emergency/Critical Medicine, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Changsheng Zhou
- Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China
| | - Feng Liu
- Department of Respiratory Medicine, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Shenglong Xu
- School of Pediatrics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Hongjun Miao
- Department of Emergency/Critical Medicine, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xiucheng Gao
- Department of Radiology, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Xuhua Ge
- Department of Emergency/Critical Medicine, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
| |
Collapse
|
2
|
Chavan S, Choubey N. Self-supervised category selective attention classifier network for diabetic macular edema classification. Acta Diabetol 2024; 61:879-896. [PMID: 38521818 DOI: 10.1007/s00592-024-02257-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 02/11/2024] [Indexed: 03/25/2024]
Abstract
AIMS This study aims to develop an advanced model for the classification of Diabetic Macular Edema (DME) using deep learning techniques. Specifically, the objective is to introduce a novel architecture, SSCSAC-Net, that leverages self-supervised learning and category-selective attention mechanisms to improve the precision of DME classification. METHODS The proposed SSCSAC-Net integrates self-supervised learning to effectively utilize unlabeled data for learning robust features related to DME. Additionally, it incorporates a category-specific attention mechanism and a domain-specific layer into the ResNet-152 base architecture. The model is trained using an ensemble of unsupervised and supervised learning techniques. Benchmark datasets are utilized for testing the model's performance, ensuring its robustness and generalizability across different data distributions. RESULTS Evaluation of the SSCSAC-Net on multiple datasets demonstrates its superior performance compared to existing techniques. The model achieves high accuracy, precision, and recall rates, with an accuracy of 98.7%, precision of 98.6%, and recall of 98.8%. Furthermore, the incorporation of self-supervised learning reduces the dependency on extensive labeled data, making the solution more scalable and cost-effective. CONCLUSIONS The proposed SSCSAC-Net represents a significant advancement in automated DME classification. By effectively using self-supervised learning and attention mechanisms, the model offers improved accuracy in identifying DME-related features within retinal images. Its robustness and generalizability across different datasets highlight its potential for clinical applications, providing a valuable tool for clinicians in diagnosing DME effectively.
Collapse
Affiliation(s)
- Sachin Chavan
- SVKM'S NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur, Maharashtra, India.
| | - Nitin Choubey
- SVKM'S NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur, Maharashtra, India
| |
Collapse
|
3
|
Vasireddi HK, K SD, Reddy GNVR. An enumerative pre-processing approach for retinopathy severity grading using an interpretable classifier: a comparative study. Graefes Arch Clin Exp Ophthalmol 2024; 262:2247-2267. [PMID: 38400856 DOI: 10.1007/s00417-024-06396-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/23/2024] [Accepted: 01/30/2024] [Indexed: 02/26/2024] Open
Abstract
BACKGROUND Diabetic retinopathy (DR) is a serious eye complication that results in permanent vision damage. As the number of patients suffering from DR increases, so does the delay in treatment for DR diagnosis. To bridge this gap, an efficient DR screening system that assists clinicians is required. Although many artificial intelligence (AI) screening systems have been deployed in recent years, accuracy remains a metric that can be improved. METHODS An enumerative pre-processing approach is implemented in the deep learning model to attain better accuracies for DR severity grading. The proposed approach is compared with various pre-trained models, and the necessary performance metrics were tabulated. This paper also presents the comparative analysis of various optimization algorithms that are utilized in the deep network model, and the results were outlined. RESULTS The experimental results are carried out on the MESSIDOR dataset to assess the performance. The experimental results show that an enumerative pipeline combination K1-K2-K3-DFNN-LOA shows better results when compared with other combinations. When compared with various optimization algorithms and pre-trained models, the proposed model has better performance with maximum accuracy, precision, recall, F1 score, and macro-averaged metric of 97.60%, 94.60%, 98.40%, 94.60%, and 0.97, respectively. CONCLUSION This study focussed on developing and implementing a DR screening system on color fundus photographs. This artificial intelligence-based system offers the possibility to enhance the efficacy and approachability of DR diagnosis.
Collapse
Affiliation(s)
- Hemanth Kumar Vasireddi
- Computer Science and Engineering, National Institute of Technology, Silchar, 788010, Assam, India
- Computer Science Engineering, Raghu Engineering College, Visakhapatnam, 531162, Andhra Pradesh, India
| | - Suganya Devi K
- Computer Science and Engineering, National Institute of Technology, Silchar, 788010, Assam, India.
| | - G N V Raja Reddy
- Computer Science and Engineering, National Institute of Technology, Silchar, 788010, Assam, India
- Computer Science Engineering, GITAM University, Visakhapatnam, 530045, Andhra Pradesh, India
| |
Collapse
|
4
|
Lisha LB, Helen Sulochana C. DEC-DRR: deep ensemble of classification model for diabetic retinopathy recognition. Med Biol Eng Comput 2024:10.1007/s11517-024-03076-1. [PMID: 38713340 DOI: 10.1007/s11517-024-03076-1] [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: 02/04/2023] [Accepted: 03/16/2024] [Indexed: 05/08/2024]
Abstract
Most diabetes patients are liable to have diabetic retinopathy (DR); however, the majority of them might not be even aware of the ailment. Therefore, early detection and treatment of DR are necessary to prevent vision loss. But, avoiding DR is not a simple process. An ophthalmologist can typically identify DR through an optical evaluation of the fundus and through the evaluation of color pictures. However, due to the increased count of DR patients, this could not be possible as it consumes more time. To rectify this problem, a novel deep ensemble-based DR classification technique is developed in this work. Initially, a Wiener filter (WF) is applied for preprocessing the image. Then, the enhanced U-Net-based segmentation process is done. Subsequent to the segmentation process, features are extracted that include statistical features, inferior superior nasal temporal (ISNT), cup to disc ratio (CDR), and improved LGBP as well. Further, deep ensemble classifiers (DEC) like CNN, Bi-GRU, and DMN are used to recognize the disease. The outcomes from DMN, CNN, and Bi-GRU are then subjected to improved SLF. Additionally, the weights of DMN, CNN, and Bi-GRU are adjusted via pelican updated Tasmanian devil optimization (PU-TDO). Finally, outputs on DR (microaneurysms, hemorrhages, hard exudates, and soft exudates) are obtained. The performance of DEC + PU-TDO for diabetic retinopathy is computed over extant models with regard to different measures for four datasets. The results on accuracy using the DEC + PU-TDO scheme for the IDRID dataset is maximum around 0.975 at 90th LP while other models have less accuracy. The FPR of DEC + PU-TDO is less around 0.039 at the 90th LP for the SUSTech-SYSU dataset, while other extant models have maximum FPR.
Collapse
Affiliation(s)
- L B Lisha
- Department of Computer Science and Engineering, Marthandam College of Engineering and Technology, Kuttakuzhi, Veeyannoor, Kanyakumari, Tamil Nadu, India.
| | - C Helen Sulochana
- Department of Electronics and Communication Engineering, St. Xavier's Catholic College of Engineering, Chunkankadai, Kanyakumari, Tamil Nadu, India
| |
Collapse
|
5
|
Bhulakshmi D, Rajput DS. A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images. PeerJ Comput Sci 2024; 10:e1947. [PMID: 38699206 PMCID: PMC11065411 DOI: 10.7717/peerj-cs.1947] [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: 11/15/2023] [Accepted: 02/28/2024] [Indexed: 05/05/2024]
Abstract
Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.
Collapse
Affiliation(s)
- Dasari Bhulakshmi
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Dharmendra Singh Rajput
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| |
Collapse
|
6
|
Patro KK, Allam JP, Sanapala U, Marpu CK, Samee NA, Alabdulhafith M, Plawiak P. An effective correlation-based data modeling framework for automatic diabetes prediction using machine and deep learning techniques. BMC Bioinformatics 2023; 24:372. [PMID: 37784049 PMCID: PMC10544445 DOI: 10.1186/s12859-023-05488-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 09/19/2023] [Indexed: 10/04/2023] Open
Abstract
The rising risk of diabetes, particularly in emerging countries, highlights the importance of early detection. Manual prediction can be a challenging task, leading to the need for automatic approaches. The major challenge with biomedical datasets is data scarcity. Biomedical data is often difficult to obtain in large quantities, which can limit the ability to train deep learning models effectively. Biomedical data can be noisy and inconsistent, which can make it difficult to train accurate models. To overcome the above-mentioned challenges, this work presents a new framework for data modeling that is based on correlation measures between features and can be used to process data effectively for predicting diabetes. The standard, publicly available Pima Indians Medical Diabetes (PIMA) dataset is utilized to verify the effectiveness of the proposed techniques. Experiments using the PIMA dataset showed that the proposed data modeling method improved the accuracy of machine learning models by an average of 9%, with deep convolutional neural network models achieving an accuracy of 96.13%. Overall, this study demonstrates the effectiveness of the proposed strategy in the early and reliable prediction of diabetes.
Collapse
Affiliation(s)
- Kiran Kumar Patro
- Department of ECE, Aditya Institute of Technology and Management, Tekkali, AP, 532201, India
| | - Jaya Prakash Allam
- School of Computer Science and Engineering, VIT Vellore, Katpadi, Vellore, Tamil Nadu, 632014, India.
| | | | - Chaitanya Kumar Marpu
- Department of ECE, Aditya Institute of Technology and Management, Tekkali, AP, 532201, India
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Maali Alabdulhafith
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Pawel Plawiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155, Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100, Gliwice, Poland
| |
Collapse
|
7
|
Meng M, Li H, Zhang M, He G, Wang L, Shen D. Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method. BMC Med Imaging 2023; 23:82. [PMID: 37312026 DOI: 10.1186/s12880-023-01023-4] [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: 07/07/2022] [Accepted: 05/23/2023] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND In clinical practice, reducing unnecessary biopsies for mammographic BI-RADS 4 lesions is crucial. The objective of this study was to explore the potential value of deep transfer learning (DTL) based on the different fine-tuning strategies for Inception V3 to reduce the number of unnecessary biopsies that residents need to perform for mammographic BI-RADS 4 lesions. METHODS A total of 1980 patients with breast lesions were included, including 1473 benign lesions (185 women with bilateral breast lesions), and 692 malignant lesions collected and confirmed by clinical pathology or biopsy. The breast mammography images were randomly divided into three subsets, a training set, testing set, and validation set 1, at a ratio of 8:1:1. We constructed a DTL model for the classification of breast lesions based on Inception V3 and attempted to improve its performance with 11 fine-tuning strategies. The mammography images from 362 patients with pathologically confirmed BI-RADS 4 breast lesions were employed as validation set 2. Two images from each lesion were tested, and trials were categorized as correct if the judgement (≥ 1 image) was correct. We used precision (Pr), recall rate (Rc), F1 score (F1), and the area under the receiver operating characteristic curve (AUROC) as the performance metrics of the DTL model with validation set 2. RESULTS The S5 model achieved the best fit for the data. The Pr, Rc, F1 and AUROC of S5 were 0.90, 0.90, 0.90, and 0.86, respectively, for Category 4. The proportions of lesions downgraded by S5 were 90.73%, 84.76%, and 80.19% for categories 4 A, 4B, and 4 C, respectively. The overall proportion of BI-RADS 4 lesions downgraded by S5 was 85.91%. There was no significant difference between the classification results of the S5 model and pathological diagnosis (P = 0.110). CONCLUSION The S5 model we proposed here can be used as an effective approach for reducing the number of unnecessary biopsies that residents need to conduct for mammographic BI-RADS 4 lesions and may have other important clinical uses.
Collapse
Affiliation(s)
- Mingzhu Meng
- Department of Radiology, The Affiliated Changzhou No 2 People's Hospital of Nanjing Medical University, Changzhou, 213164, Jiangsu Province, P. R. China
| | - Hong Li
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, Jiangsu Province, P.R. China
| | - Ming Zhang
- Department of Radiology, The Affiliated Changzhou No 2 People's Hospital of Nanjing Medical University, Changzhou, 213164, Jiangsu Province, P. R. China
| | - Guangyuan He
- Department of Radiology, The Affiliated Changzhou No 2 People's Hospital of Nanjing Medical University, Changzhou, 213164, Jiangsu Province, P. R. China
| | - Long Wang
- Department of Radiology, The Affiliated Changzhou No 2 People's Hospital of Nanjing Medical University, Changzhou, 213164, Jiangsu Province, P. R. China.
| | - Dong Shen
- Department of Radiology, The Affiliated Changzhou No 2 People's Hospital of Nanjing Medical University, Changzhou, 213164, Jiangsu Province, P. R. China.
| |
Collapse
|
8
|
Xiao H, Tang J, Zhang F, Liu L, Zhou J, Chen M, Li M, Wu X, Nie Y, Duan J. Global trends and performances in diabetic retinopathy studies: A bibliometric analysis. Front Public Health 2023; 11:1128008. [PMID: 37124794 PMCID: PMC10136779 DOI: 10.3389/fpubh.2023.1128008] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/09/2023] [Indexed: 05/02/2023] Open
Abstract
Objective The objective of this study is to conduct a comprehensive bibliometric analysis to identify and evaluate global trends in diabetic retinopathy (DR) research and visualize the focus and frontiers of this field. Methods Diabetic retinopathy-related publications from the establishment of the Web of Science (WOS) through 1 November 2022 were retrieved for qualitative and quantitative analyses. This study analyzed annual publication counts, prolific countries, institutions, journals, and the top 10 most cited literature. The findings were presented through descriptive statistics. VOSviewer 1.6.17 was used to exhibit keywords with high frequency and national cooperation networks, while CiteSpace 5.5.R2 displayed the timeline and burst keywords for each term. Results A total of 10,709 references were analyzed, and the number of publications continuously increased over the investigated period. America had the highest h-index and citation frequency, contributing to the most influence. China was the most prolific country, producing 3,168 articles. The University of London had the highest productivity. The top three productive journals were from America, and Investigative Ophthalmology Visual Science had the highest number of publications. The article from Gulshan et al. (2016; co-citation counts, 2,897) served as the representative and symbolic reference. The main research topics in this area were incidence, pathogenesis, treatment, and artificial intelligence (AI). Deep learning, models, biomarkers, and optical coherence tomography angiography (OCTA) of DR were frontier hotspots. Conclusion Bibliometric analysis in this study provided valuable insights into global trends in DR research frontiers. Four key study directions and three research frontiers were extracted from the extensive DR-related literature. As the incidence of DR continues to increase, DR prevention and treatment have become a pressing public health concern and a significant area of research interest. In addition, the development of AI technologies and telemedicine has emerged as promising research frontiers for balancing the number of doctors and patients.
Collapse
Affiliation(s)
- Huan Xiao
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jinfan Tang
- School of Acupuncture-Moxibustion and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Feng Zhang
- School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
| | - Luping Liu
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jing Zhou
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Meiqi Chen
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Mengyue Li
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xiaoxiao Wu
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yingying Nie
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Junguo Duan
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| |
Collapse
|
9
|
Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks. Diagnostics (Basel) 2023; 13:diagnostics13051001. [PMID: 36900145 PMCID: PMC10000375 DOI: 10.3390/diagnostics13051001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/23/2023] [Accepted: 03/02/2023] [Indexed: 03/09/2023] Open
Abstract
Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person's growth of this condition. Among the essential elements at the top of the list are anxiety and long-term diabetes. If not detected early, this illness might result in permanent eyesight loss. The damage can be reduced or avoided if it is recognized ahead of time. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Even though this procedure is reasonably accurate, it is quite pricey. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. For contrast enhancement, the Harris hawks optimization (HHO) technique is presented. Finally, the experiments were conducted for two kinds of datasets: IDRiR and Messidor for accuracy, precision, recall, F-score, computational time, and error rate.
Collapse
|
10
|
Venkaiahppalaswamy B, Prasad Reddy PVGD, Batha S. Hybrid deep learning approaches for the detection of diabetic retinopathy using optimized wavelet based model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
11
|
Ji Y, Liu S, Hong X, Lu Y, Wu X, Li K, Li K, Liu Y. Advances in artificial intelligence applications for ocular surface diseases diagnosis. Front Cell Dev Biol 2022; 10:1107689. [PMID: 36605721 PMCID: PMC9808405 DOI: 10.3389/fcell.2022.1107689] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 12/05/2022] [Indexed: 01/07/2023] Open
Abstract
In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.
Collapse
Affiliation(s)
- Yuke Ji
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Sha Liu
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangqian Hong
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Yi Lu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Xingyang Wu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Kunke Li
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Keran Li
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Yunfang Liu
- Department of Ophthalmology, First Affiliated Hospital of Huzhou University, Huzhou, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| |
Collapse
|
12
|
Jiang W, Li M, Shabaz M, Sharma A, Haq MA. Generation of Voice Signal Tone Sandhi and Melody Based on Convolutional Neural Network. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3545569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
There is a need to prevent the generation of criminal activities in the voice signals due to changing voices by intruders to cover up their personal identities. The voice signal change detection based on convolutional neural network is proposed in this work that uses three commonly used voice processing software to change the tone of the voice library: Audacity, CoolEdit and RTISI. The research further raises 5 semitones for each voice, which are recorded at different levels, as +4, +5, +6, +7 and +8 respectively. Simultaneously, every speech is lowered by 5 halftones and which are further represented as -4, -5, -6, -7 and -8 respectively. The convolution neural network corresponding to network b-3 is determined as the final classifier in this article through experiments. The average accuracy A1 of its three categories has reached more than 97%, the detection accuracy A2 of electronic tone sandhi speech has reached more than 97%, and the false alarm rate FAR of the original speech is less than 1.9%. The outcomes obtained shows that the detection algorithm in this paper is effective, and it has good generalization ability.
Collapse
Affiliation(s)
- Wei Jiang
- Department of Music, Shandong University of Science and Technology, Qingdao Shandong, 266590, China
| | - Mengqi Li
- Department of Music, Shandong University of Science and Technology, Qingdao Shandong, 266590, China
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology, Jammu, J&K, India
| | - Ashutosh Sharma
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
| | - Mohd Anul Haq
- Department of Computer Science, College of Computer Science and Information Science, Majmaah University, Saudi Arabia
| |
Collapse
|
13
|
Construction of a Prediction Model for the Mortality of Elderly Patients with Diabetic Nephropathy. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5724050. [PMID: 36133909 PMCID: PMC9484980 DOI: 10.1155/2022/5724050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/09/2022] [Accepted: 07/26/2022] [Indexed: 11/20/2022]
Abstract
To construct a prediction model for all-cause mortality in elderly diabetic nephropathy (DN) patients, in this cohort study, the data of 511 DN patients aged ≥65 years were collected and the participants were divided into the training set (n = 358) and the testing set (n = 153). The median survival time of all participants was 2 years. The data in the training set were grouped into the survival group (n = 203) or the death group (n = 155). Variables with P ≤ 0.1 between the two groups were selected as preliminary predictors and involved into the multivariable logistic regression model and the covariables were gradually adjusted. The receiver operator characteristic (ROC), Kolmogorov-Smirnov (KS), and calibration curves were plotted for evaluating the predictive performance of the model. Internal validation of the performance of the model was verified in the testing set. The predictive values of the model were also conducted in terms of people with different genders and ages or accompanied with chronic kidney disease (CKD) or cardiovascular diseases (CVD), respectively. In total, 216 (42.27%) elderly DN patients were dead within 2 years. The prediction model for the 2-year mortality of elderly patients with DN was established based on length of stay (LOS), temperature, heart rate, peripheral oxygen saturation (SpO2), serum creatinine (Scr), red cell distribution width (RDW), the simplified acute physiology score-II (SAPS-II), hyperlipidemia, and the Chronic Kidney Disease Epidemiology Collaboration equation for estimated glomerular filtration rate (eGFR-CKD-EPI). The AUC of the model was 0.78 (95% CI: 0.73–0.83) in the training set and 0.72 (95% CI: 0.63–0.80) in the testing set. The AUC of the model was 0.78 (95% CI: 0.65–0.91) in females and 0.78 (95%CI: 0.68–0.88) in patients ≤75 years. The AUC of the model was 0.74 (95% CI: 0.64–0.84) in patients accompanied with CKD. The model had good predictive value for the mortality of elderly patients with DN within 2 years. In addition, the model showed good predictive values for female DN patients, DN patients ≤75 years, and DN patients accompanied with CKD.
Collapse
|
14
|
Nadeem MW, Goh HG, Hussain M, Liew SY, Andonovic I, Khan MA. Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22186780. [PMID: 36146130 PMCID: PMC9505428 DOI: 10.3390/s22186780] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/02/2022] [Accepted: 08/08/2022] [Indexed: 05/12/2023]
Abstract
Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding promising results in applications across a growing number of fields, most notably in image processing, medical image analysis, data analysis, and bioinformatics. DL algorithms have also had a significant positive impact through yielding improvements in screening, recognition, segmentation, prediction, and classification applications across different domains of healthcare, such as those concerning the abdomen, cardiac, pathology, and retina. Given the extensive body of recent scientific contributions in this discipline, a comprehensive review of deep learning developments in the domain of diabetic retinopathy (DR) analysis, viz., screening, segmentation, prediction, classification, and validation, is presented here. A critical analysis of the relevant reported techniques is carried out, and the associated advantages and limitations highlighted, culminating in the identification of research gaps and future challenges that help to inform the research community to develop more efficient, robust, and accurate DL models for the various challenges in the monitoring and diagnosis of DR.
Collapse
Affiliation(s)
- Muhammad Waqas Nadeem
- Faculty of Information and Communication Technology (FICT), Universiti Tunku Abdul Rahman (UTAR), Kampar 31900, Malaysia
| | - Hock Guan Goh
- Faculty of Information and Communication Technology (FICT), Universiti Tunku Abdul Rahman (UTAR), Kampar 31900, Malaysia
- Correspondence: (H.G.G.); (I.A.)
| | - Muzammil Hussain
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54000, Pakistan
| | - Soung-Yue Liew
- Faculty of Information and Communication Technology (FICT), Universiti Tunku Abdul Rahman (UTAR), Kampar 31900, Malaysia
| | - Ivan Andonovic
- Department of Electronic and Electrical Engineering, Royal College Building, University of Strathclyde, 204 George St., Glasgow G1 1XW, UK
- Correspondence: (H.G.G.); (I.A.)
| | - Muhammad Adnan Khan
- Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam 13557, Korea
- Faculty of Computing, Riphah School of Computing and Innovation, Riphah International University, Lahore Campus, Lahore 54000, Pakistan
| |
Collapse
|
15
|
Elwin JGR, Mandala J, Maram B, Kumar RR. Ar-HGSO: Autoregressive-Henry Gas Sailfish Optimization enabled deep learning model for diabetic retinopathy detection and severity level classification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
16
|
Yaghy A, Lee AY, Keane PA, Keenan TDL, Mendonca LSM, Lee CS, Cairns AM, Carroll J, Chen H, Clark J, Cukras CA, de Sisternes L, Domalpally A, Durbin MK, Goetz KE, Grassmann F, Haines JL, Honda N, Hu ZJ, Mody C, Orozco LD, Owsley C, Poor S, Reisman C, Ribeiro R, Sadda SR, Sivaprasad S, Staurenghi G, Ting DS, Tumminia SJ, Zalunardo L, Waheed NK. Artificial intelligence-based strategies to identify patient populations and advance analysis in age-related macular degeneration clinical trials. Exp Eye Res 2022; 220:109092. [PMID: 35525297 PMCID: PMC9405680 DOI: 10.1016/j.exer.2022.109092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/18/2022] [Accepted: 04/20/2022] [Indexed: 11/04/2022]
Affiliation(s)
- Antonio Yaghy
- New England Eye Center, Tufts University Medical Center, Boston, MA, USA
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA; Karalis Johnson Retina Center, Seattle, WA, USA
| | - Pearse A Keane
- Moorfields Eye Hospital & UCL Institute of Ophthalmology, London, UK
| | - Tiarnan D L Keenan
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA; Karalis Johnson Retina Center, Seattle, WA, USA
| | | | - Joseph Carroll
- Department of Ophthalmology & Visual Sciences, Medical College of Wisconsin, 925 N 87th Street, Milwaukee, WI, 53226, USA
| | - Hao Chen
- Genentech, South San Francisco, CA, USA
| | | | - Catherine A Cukras
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Amitha Domalpally
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA
| | | | - Kerry E Goetz
- Office of the Director, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Jonathan L Haines
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA; Cleveland Institute of Computational Biology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | | | - Zhihong Jewel Hu
- Doheny Eye Institute, University of California, Los Angeles, CA, USA
| | | | - Luz D Orozco
- Department of Bioinformatics, Genentech, South San Francisco, CA, 94080, USA
| | - Cynthia Owsley
- Department of Ophthalmology and Visual Sciences, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Stephen Poor
- Department of Ophthalmology, Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | | | - Srinivas R Sadda
- Doheny Eye Institute, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA
| | - Sobha Sivaprasad
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
| | - Giovanni Staurenghi
- Department of Biomedical and Clinical Sciences Luigi Sacco, Luigi Sacco Hospital, University of Milan, Italy
| | - Daniel Sw Ting
- Singapore Eye Research Institute, Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Santa J Tumminia
- Office of the Director, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Nadia K Waheed
- New England Eye Center, Tufts University Medical Center, Boston, MA, USA.
| |
Collapse
|
17
|
Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy. DISEASE MARKERS 2022; 2022:3406890. [PMID: 35783011 PMCID: PMC9249504 DOI: 10.1155/2022/3406890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 06/09/2022] [Indexed: 11/28/2022]
Abstract
The eye is one of the most important organs of the human body. Eye diseases are closely related to other systemic diseases, both of which influence each other. Numerous systemic diseases lead to special clinical manifestations and complications in the eyes. Typical diseases include diabetic retinopathy, hypertensive retinopathy, thyroid associated ophthalmopathy, optic neuromyelitis, and Behcet's disease. Systemic disease-related ophthalmopathy is usually a chronic disease, and the analysis of imaging markers is helpful for a comprehensive diagnosis of these diseases. Recently, artificial intelligence (AI) technology based on deep learning has rapidly developed, leading to numerous achievements and arousing widespread concern. Presently, AI technology has made significant progress in research on imaging markers of systemic disease-related ophthalmopathy; however, there are also many limitations and challenges. This article reviews the research achievements, limitations, and future prospects of AI image analysis technology in systemic disease-related ophthalmopathy.
Collapse
|
18
|
Ai Z, Huang X, Feng J, Wang H, Tao Y, Zeng F, Lu Y. FN-OCT: Disease Detection Algorithm for Retinal Optical Coherence Tomography Based on a Fusion Network. Front Neuroinform 2022; 16:876927. [PMID: 35784186 PMCID: PMC9243322 DOI: 10.3389/fninf.2022.876927] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/04/2022] [Indexed: 01/31/2023] Open
Abstract
Optical coherence tomography (OCT) is a new type of tomography that has experienced rapid development and potential in recent years. It is playing an increasingly important role in retinopathy diagnoses. At present, due to the uneven distributions of medical resources in various regions, the uneven proficiency levels of doctors in grassroots and remote areas, and the development needs of rare disease diagnosis and precision medicine, artificial intelligence technology based on deep learning can provide fast, accurate, and effective solutions for the recognition and diagnosis of retinal OCT images. To prevent vision damage and blindness caused by the delayed discovery of retinopathy, a fusion network (FN)-based retinal OCT classification algorithm (FN-OCT) is proposed in this paper to improve upon the adaptability and accuracy of traditional classification algorithms. The InceptionV3, Inception-ResNet, and Xception deep learning algorithms are used as base classifiers, a convolutional block attention mechanism (CBAM) is added after each base classifier, and three different fusion strategies are used to merge the prediction results of the base classifiers to output the final prediction results (choroidal neovascularization (CNV), diabetic macular oedema (DME), drusen, normal). The results show that in a classification problem involving the UCSD common retinal OCT dataset (108,312 OCT images from 4,686 patients), compared with that of the InceptionV3 network model, the prediction accuracy of FN-OCT is improved by 5.3% (accuracy = 98.7%, area under the curve (AUC) = 99.1%). The predictive accuracy and AUC achieved on an external dataset for the classification of retinal OCT diseases are 92 and 94.5%, respectively, and gradient-weighted class activation mapping (Grad-CAM) is used as a visualization tool to verify the effectiveness of the proposed FNs. This finding indicates that the developed fusion algorithm can significantly improve the performance of classifiers while providing a powerful tool and theoretical support for assisting with the diagnosis of retinal OCT.
Collapse
Affiliation(s)
- Zhuang Ai
- Department of Research and Development, Sinopharm Genomics Technology Co., Ltd., Jiangsu, China
| | - Xuan Huang
- Department of Ophthalmology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Medical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Jing Feng
- Department of Ophthalmology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Hui Wang
- Department of Ophthalmology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yong Tao
- Department of Ophthalmology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Fanxin Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, Sichuan, China
| | - Yaping Lu
- Department of Research and Development, Sinopharm Genomics Technology Co., Ltd., Jiangsu, China
| |
Collapse
|
19
|
Lakshminarayanan V, Kheradfallah H, Sarkar A, Jothi Balaji J. Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey. J Imaging 2021; 7:165. [PMID: 34460801 PMCID: PMC8468161 DOI: 10.3390/jimaging7090165] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/23/2021] [Accepted: 08/24/2021] [Indexed: 12/16/2022] Open
Abstract
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016-2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented.
Collapse
Affiliation(s)
- Vasudevan Lakshminarayanan
- Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Hoda Kheradfallah
- Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Arya Sarkar
- Department of Computer Engineering, University of Engineering and Management, Kolkata 700 156, India;
| | | |
Collapse
|