A deep learning system for predicting time to progression of diabetic retinopathy

Dai, Ling; Sheng, Bin; Chen, Tingli; Wu, Qiang; Liu, Ruhan; Cai, Chun; Wu, Liang; Yang, Dawei; Hamzah, Haslina; Liu, Yuexing; Wang, Xiangning; Guan, Zhouyu; Yu, Shujie; Li, Tingyao; Tang, Ziqi; Ran, Anran; Che, Haoxuan; Chen, Hao; Zheng, Yingfeng; Shu, Jia; Huang, Shan; Wu, Chan; Lin, Shiqun; Liu, Dan; Li, Jiajia; Wang, Zheyuan; Meng, Ziyao; Shen, Jie; Hou, Xuhong; Deng, Chenxin; Ruan, Lei; Lu, Feng; Chee, Miaoli; Quek, Ten Cheer; Srinivasan, Ramyaa; Raman, Rajiv; Sun, Xiaodong; Wang, Ya Xing; Wu, Jiarui; Jin, Hai; Dai, Rongping; Shen, Dinggang; Yang, Xiaokang; Guo, Minyi; Zhang, Cuntai; Cheung, Carol Y.; Tan, Gavin Siew Wei; Tham, Yih-Chung; Cheng, Ching-Yu; Li, Huating; Wong, Tien Yin; Jia, Weiping

doi:10.1038/s41591-023-02702-z

Cited by 64 publications

(8 citation statements)

References 61 publications

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“…Compared with the existing works [14][15][16][17][18], a thorough analysis of the features of fundus image processing in diabetic retinopathy was carried out and a balanced database of images and informative indicators was formed for further work on improving the existing method of early diagnosis.…”

Section: Discussion Of the Results Of The Development Of A Methods Fo...mentioning

confidence: 99%

“…In the follow-up paper [18], the researchers went further, by analyzing the progression of diabetic retinopathy on retinal images, they developed and tested a deep learning system (DeepDR Plus) to predict the time to progression of DR over 5 years. The strength of this work is that considerable attention was paid to the pre-processing of images to extract informative indicators.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Increasing the reliability of diagnosis of diabetic retinopathy based on machine learning

Mamyrbayev,

Pavlov,

Karas

et al. 2024

EEJET

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This paper discusses the method of measuring and analyzing the parameters of the retina with subsequent diagnosis based on them of pathological changes due to diabetic retinopathy, which is crucial in the field of medicine to help doctors in timely detection and treatment of the disease. The main problem of biomedical image data analysis is insufficient pre-processing of images for further clear determination of informative indicators. This paper explores the application of machine learning and image processing techniques to develop an effective method for the diagnosis of diabetic retinopathy. The main focus is on obtaining the optimal model using machine learning and different types of neural networks. This paper considered and analyzed such methods of image preprocessing as: median filtering, grayscale conversion, cropping of non-informative areas of the image, selection of contours. The classification results of three rules (Classical Neural Networks (CNNs), Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) were analyzed, and through experimental studies it was determined that the ANN performed the task best (accuracy=87.1 %, reliability=84.6 %, sensitivity=91.6 %, specificity=84 %). An information model was obtained to support decision-making in assessing the condition of the retina using the processing of the obtained microscopic images and further analysis of informative parameters, and a database of more than 35,000 samples and informative features of the retina was formed. Given the sufficient quality of classification and the availability of software and hardware, this method can be developed and applied in practice in medical institutions after conducting all the necessary clinical studies

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Section: Discussion Of the Results Of The Development Of A Methods Fo...mentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Increasing the reliability of diagnosis of diabetic retinopathy based on machine learning

Mamyrbayev,

Pavlov,

Karas

et al. 2024

EEJET

View full text Add to dashboard Cite

show abstract

“…In recent years, the advancement of hardware computing power in parallel computing technology and the progress in deep learning have mutually benefited each other [50][51][52][53]. Deep learning-based computer vision techniques have been widely applied in specialized fields such as medicine, sports, and industrial monitoring [54][55][56]. Motion capture based on computer vision offers advantages such as higher accuracy, faster speed, and reduced workload [57,58].…”

Section: Computer Vision Capture Systemsmentioning

confidence: 99%

Motion Capture Technology in Sports Scenarios: A Survey

Suo,

Tang,

2024

Sensors

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Motion capture technology plays a crucial role in optimizing athletes’ skills, techniques, and strategies by providing detailed feedback on motion data. This article presents a comprehensive survey aimed at guiding researchers in selecting the most suitable motion capture technology for sports science investigations. By comparing and analyzing the characters and applications of different motion capture technologies in sports scenarios, it is observed that cinematography motion capture technology remains the gold standard in biomechanical analysis and continues to dominate sports research applications. Wearable sensor-based motion capture technology has gained significant traction in specialized areas such as winter sports, owing to its reliable system performance. Computer vision-based motion capture technology has made significant advancements in recognition accuracy and system reliability, enabling its application in various sports scenarios, from single-person technique analysis to multi-person tactical analysis. Moreover, the emerging field of multimodal motion capture technology, which harmonizes data from various sources with the integration of artificial intelligence, has proven to be a robust research method for complex scenarios. A comprehensive review of the literature from the past 10 years underscores the increasing significance of motion capture technology in sports, with a notable shift from laboratory research to practical training applications on sports fields. Future developments in this field should prioritize research and technological advancements that cater to practical sports scenarios, addressing challenges such as occlusion, outdoor capture, and real-time feedback.

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“…6D pose estimation plays a crucial role in various fields such as augmented reality [1], robot grasping [2], and autonomous driving [3]. In recent years, many instance-level pose estimation methods [4][5][6][7] have demonstrated good performance.…”

Section: Introductionmentioning

confidence: 99%

AG-Net: Category-level 6D Pose Estimation Network Based on Attention Mechanism and Global Enhancement

Bowen,

Jinlong,

Suqin

et al. 2024

Preprint

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How to effectively enhance feature extraction is a challenge faced by current 6D pose estimation methods. To address this issue, we propose a novel 6D pose estimation network based on VI-Net, shortened as AG-Net, which uses ECA block and Global enhancev module to enhance feature extraction: ECA block embeds a channel attention mechanism into the convolutional layers, replacing the fully connected layers with 1×1Conv to capture relationships between different channels and improves the performance of feature extraction. The Global enhancev module further processes the received information and enhances feature extraction by fusing global features, effectively balancing performance and speed, and better estimating the translation and size of objects. We applied the proposed AG-Net to category-level 6D pose estimation tasks and tested it on the REAL275 and CAMER25 datasets using IOU 3D intersection and n◦mcm evaluation metrics. The results showed that AG-Net outperformed current state-of-the-art methods. Our code and models are available at https://github.com/AFESDTTM/AG-Net

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A deep learning system for predicting time to progression of diabetic retinopathy

Cited by 64 publications

References 61 publications

Increasing the reliability of diagnosis of diabetic retinopathy based on machine learning

Increasing the reliability of diagnosis of diabetic retinopathy based on machine learning

Motion Capture Technology in Sports Scenarios: A Survey

AG-Net: Category-level 6D Pose Estimation Network Based on Attention Mechanism and Global Enhancement

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