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Wu MN, He K, Yu YB, Zheng B, Zhu SJ, Hong XQ, Xi WQ, Zhang Z. Intelligent diagnostic model for pterygium by combining attention mechanism and MobileNetV2. Int J Ophthalmol 2024; 17:1184-1192. [PMID: 39026919 PMCID: PMC11246929 DOI: 10.18240/ijo.2024.07.02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 04/01/2024] [Indexed: 07/20/2024] Open
Abstract
AIM To evaluate the application of an intelligent diagnostic model for pterygium. METHODS For intelligent diagnosis of pterygium, the attention mechanisms-SENet, ECANet, CBAM, and Self-Attention-were fused with the lightweight MobileNetV2 model structure to construct a tri-classification model. The study used 1220 images of three types of anterior ocular segments of the pterygium provided by the Eye Hospital of Nanjing Medical University. Conventional classification models-VGG16, ResNet50, MobileNetV2, and EfficientNetB7-were trained on the same dataset for comparison. To evaluate model performance in terms of accuracy, Kappa value, test time, sensitivity, specificity, the area under curve (AUC), and visual heat map, 470 test images of the anterior segment of the pterygium were used. RESULTS The accuracy of the MobileNetV2+Self-Attention model with 281 MB in model size was 92.77%, and the Kappa value of the model was 88.92%. The testing time using the model was 9ms/image in the server and 138ms/image in the local computer. The sensitivity, specificity, and AUC for the diagnosis of pterygium using normal anterior segment images were 99.47%, 100%, and 100%, respectively; using anterior segment images in the observation period were 88.30%, 95.32%, and 96.70%, respectively; and using the anterior segment images in the surgery period were 88.18%, 94.44%, and 97.30%, respectively. CONCLUSION The developed model is lightweight and can be used not only for detection but also for assessing the severity of pterygium.
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Affiliation(s)
- Mao-Nian Wu
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000, Zhejiang Province, China
| | - Kai He
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- School of Mathematical Information, Shaoxing University, Shaoxing 312000, Zhejiang Province, China
| | - Yi-Bei Yu
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
| | - Bo Zheng
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000, Zhejiang Province, China
| | - Shao-Jun Zhu
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000, Zhejiang Province, China
| | - Xiang-Qian Hong
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| | - Wen-Qun Xi
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| | - Zhe Zhang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
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Chen B, Fang XW, Wu MN, Zhu SJ, Zheng B, Liu BQ, Wu T, Hong XQ, Wang JT, Yang WH. Artificial intelligence assisted pterygium diagnosis: current status and perspectives. Int J Ophthalmol 2023; 16:1386-1394. [PMID: 37724272 PMCID: PMC10475638 DOI: 10.18240/ijo.2023.09.04] [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: 04/17/2023] [Accepted: 05/24/2023] [Indexed: 09/20/2023] Open
Abstract
Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment. Early and accurate diagnosis is essential for effective management. Recently, artificial intelligence (AI) has shown promising potential in assisting clinicians with pterygium diagnosis. This paper provides an overview of AI-assisted pterygium diagnosis, including the AI techniques used such as machine learning, deep learning, and computer vision. Furthermore, recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection, classification and segmentation were summarized. The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed. The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis, which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.
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Affiliation(s)
- Bang Chen
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Xin-Wen Fang
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Mao-Nian Wu
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Shao-Jun Zhu
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Bo Zheng
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Bang-Quan Liu
- College of Digital Technology and Engineering, Ningbo University of Finance & Economics, Ningbo 315000, Zhejiang Province, China
| | - Tao Wu
- Huzhou Institute, Zhejiang University of Technology, Huzhou 313000, Zhejiang Province, China
| | - Xiang-Qian Hong
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| | - Jian-Tao Wang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| | - Wei-Hua Yang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
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Zhang Z, Wang Y, Zhang H, Samusak A, Rao H, Xiao C, Abula M, Cao Q, Dai Q. Artificial intelligence-assisted diagnosis of ocular surface diseases. Front Cell Dev Biol 2023; 11:1133680. [PMID: 36875760 PMCID: PMC9981656 DOI: 10.3389/fcell.2023.1133680] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/08/2023] [Indexed: 02/19/2023] Open
Abstract
With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.
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Affiliation(s)
- Zuhui Zhang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China.,National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Ying Wang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Hongzhen Zhang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Arzigul Samusak
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Huimin Rao
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Chun Xiao
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Muhetaer Abula
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Qixin Cao
- Huzhou Traditional Chinese Medicine Hospital Affiliated to Zhejiang University of Traditional Chinese Medicine, Huzhou, China
| | - Qi Dai
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China.,National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
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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.
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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,
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Pterygium Screening and Lesion Area Segmentation Based on Deep Learning. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3942110. [DOI: 10.1155/2022/3942110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 04/18/2022] [Indexed: 11/23/2022]
Abstract
A two-category model and a segmentation model of pterygium were proposed to assist ophthalmologists in establishing the diagnosis of ophthalmic diseases. A total of 367 normal anterior segment images and 367 pterygium anterior segment images were collected at the Affiliated Eye Hospital of Nanjing Medical University. AlexNet, VGG16, ResNet18, and ResNet50 models were used to train the two-category pterygium models. A total of 150 normal and 150 pterygium anterior segment images were used to test the models, and the results were compared. The main evaluation indicators, including sensitivity, specificity, area under the curve, kappa value, and receiver operator characteristic curves of the four models, were compared. Simultaneously, 367 pterygium anterior segment images were used to train two improved pterygium segmentation models based on PSPNet. A total of 150 pterygium images were used to test the models, and the results were compared with those of the other four segmentation models. The main evaluation indicators included mean intersection over union (MIOU), IOU, mean average precision (MPA), and PA. Among the two-category models of pterygium, the best diagnostic result was obtained using the VGG16 model. The diagnostic accuracy, kappa value, diagnostic sensitivity of pterygium, diagnostic specificity of pterygium, and F1-score were 99%, 98%, 98.67%, 99.33%, and 99%, respectively. Among the pterygium segmentation models, the double phase-fusion PSPNet model had the best results, with MIOU, IOU, MPA, and PA of 86.57%, 78.1%, 92.3%, and 86.96%, respectively. This study designed a pterygium two-category model and a pterygium segmentation model for the images of the normal anterior and pterygium anterior segments, which could help patients self-screen easily and assist ophthalmologists in establishing the diagnosis of ophthalmic diseases and marking the actual scope of surgery.
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Artificial Intelligence in Eye Disease: Recent Developments, Applications, and Surveys. Diagnostics (Basel) 2022; 12:diagnostics12081927. [PMID: 36010277 PMCID: PMC9406878 DOI: 10.3390/diagnostics12081927] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/05/2022] [Accepted: 08/09/2022] [Indexed: 11/16/2022] Open
Abstract
Artificial intelligence (AI) has expanded by finding applications in medical diagnosis for clinical support systems [...]
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Computer-Assisted Pterygium Screening System: A Review. Diagnostics (Basel) 2022; 12:diagnostics12030639. [PMID: 35328192 PMCID: PMC8947201 DOI: 10.3390/diagnostics12030639] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/25/2022] [Accepted: 03/04/2022] [Indexed: 12/10/2022] Open
Abstract
Pterygium is an eye condition that causes the fibrovascular tissues to grow towards the corneal region. At the early stage, it is not a harmful condition, except for slight discomfort for the patients. However, it will start to affect the eyesight of the patient once the tissues encroach towards the corneal region, with a more serious impact if it has grown into the pupil region. Therefore, this condition needs to be identified as early as possible to halt its growth, with the use of simple eye drops and sunglasses. One of the associated risk factors for this condition is a low educational level, which explains the reason that the majority of the patients are not aware of this condition. Hence, it is important to develop an automated pterygium screening system based on simple imaging modalities such as a mobile phone camera so that it can be assessed by many people. During the early stage of automated pterygium screening system development, conventional machine learning techniques such as support vector machines and artificial neural networks are the de facto algorithms to detect the presence of pterygium tissues. However, with the arrival of the deep learning era, coupled with the availability of large training data, deep learning networks have replaced the conventional networks in screening for the pterygium condition. The deep learning networks have been successfully implemented for three major purposes, which are to classify an image regarding whether there is the presence of pterygium tissues or not, to localize the lesion tissues through object detection methodology, and to semantically segment the lesion tissues at the pixel level. This review paper summarizes the type, severity, risk factors, and existing state-of-the-art technology in automated pterygium screening systems. A few available datasets are also discussed in this paper for both classification and segmentation tasks. In conclusion, a computer-assisted pterygium screening system will benefit many people all over the world, especially in alerting them to the possibility of having this condition so that preventive actions can be advised at an early stage.
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Zulkifley MA, Abd Sukor ME, Munir AF, Mohd Shafiai MH. Stock Market Manipulation Detection using Artificial Intelligence: A Concise Review. 2021 INTERNATIONAL CONFERENCE ON DECISION AID SCIENCES AND APPLICATION (DASA) 2021. [DOI: 10.1109/dasa53625.2021.9682322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
- Mohd Asyraf Zulkifley
- Universiti Kebangsaan Malaysia,Department of Electrical, Electronic and Systems Engineering,Bangi,Malaysia
| | - Mohd Edil Abd Sukor
- Universiti Malaya,Faculty of Business and Accountancy,Department of Finance and Banking,Kuala Lumpur,Malaysia
| | - Ali Fayyaz Munir
- Virtual University of Pakistan,Faculty of Management,Lahore,Pakistan
| | - Muhammad Hakimi Mohd Shafiai
- Universiti Kebangsaan Malaysia,Center of Sustainable and Inclusive Development Faculty of Economics and Management,Bangi,Malaysia
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Residual-Shuffle Network with Spatial Pyramid Pooling Module for COVID-19 Screening. Diagnostics (Basel) 2021; 11:diagnostics11081497. [PMID: 34441431 PMCID: PMC8394651 DOI: 10.3390/diagnostics11081497] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 08/14/2021] [Accepted: 08/16/2021] [Indexed: 12/24/2022] Open
Abstract
Since the start of the COVID-19 pandemic at the end of 2019, more than 170 million patients have been infected with the virus that has resulted in more than 3.8 million deaths all over the world. This disease is easily spreadable from one person to another even with minimal contact, even more for the latest mutations that are more deadly than its predecessor. Hence, COVID-19 needs to be diagnosed as early as possible to minimize the risk of spreading among the community. However, the laboratory results on the approved diagnosis method by the World Health Organization, the reverse transcription-polymerase chain reaction test, takes around a day to be processed, where a longer period is observed in the developing countries. Therefore, a fast screening method that is based on existing facilities should be developed to complement this diagnosis test, so that a suspected patient can be isolated in a quarantine center. In line with this motivation, deep learning techniques were explored to provide an automated COVID-19 screening system based on X-ray imaging. This imaging modality is chosen because of its low-cost procedures that are widely available even in many small clinics. A new convolutional neural network (CNN) model is proposed instead of utilizing pre-trained networks of the existing models. The proposed network, Residual-Shuffle-Net, comprises four stacks of the residual-shuffle unit followed by a spatial pyramid pooling (SPP) unit. The architecture of the residual-shuffle unit follows an hourglass design with reduced convolution filter size in the middle layer, where a shuffle operation is performed right after the split branches have been concatenated back. Shuffle operation forces the network to learn multiple sets of features relationship across various channels instead of a set of global features. The SPP unit, which is placed at the end of the network, allows the model to learn multi-scale features that are crucial to distinguish between the COVID-19 and other types of pneumonia cases. The proposed network is benchmarked with 12 other state-of-the-art CNN models that have been designed and tuned specially for COVID-19 detection. The experimental results show that the Residual-Shuffle-Net produced the best performance in terms of accuracy and specificity metrics with 0.97390 and 0.98695, respectively. The model is also considered as a lightweight model with slightly more than 2 million parameters, which makes it suitable for mobile-based applications. For future work, an attention mechanism can be integrated to target certain regions of interest in the X-ray images that are deemed to be more informative for COVID-19 diagnosis.
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