Optimized-Unet: Novel Algorithm for Parapapillary Atrophy Segmentation

Wan, Cheng; Wu, Jiasheng; Han, Ling; Yan, Zhipeng; Wang, Chenghu; Jiang, Qin; Cao, Guiying; Xu, Yanwu; Yang, Weihua

doi:10.3389/fnins.2021.758887

Cited by 13 publications

(8 citation statements)

References 25 publications

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“…Ophthalmologists are confused by the quality differences among fundus diagnostic images [ 38 ]. Enhancing the analysis of retinal image structures requires the development of a computer-assisted algorithm to correct the low fundus image quality [ 39 ]. Wan et al [ 40 ] proposed a deep learning–based technique that overcomes the limitations of current imaging algorithms and improves the low retinal image quality.…”

Section: Discussionmentioning

confidence: 99%

Emerging Trends and Research Foci in Artificial Intelligence for Retinal Diseases: Bibliometric and Visualization Study

Zhao¹,

Lü²,

Qian³

et al. 2022

J Med Internet Res

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Background Patients with retinal diseases may exhibit serious complications that cause severe visual impairment owing to a lack of awareness of retinal diseases and limited medical resources. Understanding how artificial intelligence (AI) is used to make predictions and perform relevant analyses is a very active area of research on retinal diseases. In this study, the relevant Science Citation Index (SCI) literature on the AI of retinal diseases published from 2012 to 2021 was integrated and analyzed. Objective The aim of this study was to gain insights into the overall application of AI technology to the research of retinal diseases from set time and space dimensions. Methods Citation data downloaded from the Web of Science Core Collection database for AI in retinal disease publications from January 1, 2012, to December 31, 2021, were considered for this analysis. Information retrieval was analyzed using the online analysis platforms of literature metrology: Bibliometrc, CiteSpace V, and VOSviewer. Results A total of 197 institutions from 86 countries contributed to relevant publications; China had the largest number and researchers from University College London had the highest H-index. The reference clusters of SCI papers were clustered into 12 categories. “Deep learning” was the cluster with the widest range of cocited references. The burst keywords represented the research frontiers in 2018-2021, which were “eye disease” and “enhancement.” Conclusions This study provides a systematic analysis method on the literature regarding AI in retinal diseases. Bibliometric analysis enabled obtaining results that were objective and comprehensive. In the future, high-quality retinal image–forming AI technology with strong stability and clinical applicability will continue to be encouraged.

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Section: Discussionmentioning

confidence: 99%

Emerging Trends and Research Foci in Artificial Intelligence for Retinal Diseases: Bibliometric and Visualization Study

Zhao¹,

Lü²,

Qian³

et al. 2022

J Med Internet Res

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“…From the analysis of the research field of the journal, it is evident that in recent years, research focused on the use of computer engineering technology combined with a knowledge base of ophthalmology to develop more suitable ophthalmic disease detection systems. AI is widely used to identify ophthalmic diseases, which is typically based on the analysis of ophthalmic images ( Xu et al, 2021a ; Wan et al, 2021b ; Xu et al, 2021b ). In addition, this research also includes the detection of genes related to ophthalmic diseases ( Saikia and Nirmala, 2022 ), ocular metabolites ( Myer et al, 2020 ), and pathology ocular metabolites ( Nezu et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis

Zhao

Zhu

et al. 2022

Front. Pharmacol.

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Background: Artificial intelligence (AI) has been used in the research of ophthalmic disease diagnosis, and it may have an impact on medical and ophthalmic practice in the future. This study explores the general application and research frontier of artificial intelligence in ophthalmic disease detection.Methods: Citation data were downloaded from the Web of Science Core Collection database to evaluate the extent of the application of Artificial intelligence in ophthalmic disease diagnosis in publications from 1 January 2012, to 31 December 2021. This information was analyzed using CiteSpace.5.8. R3 and Vosviewer.Results: A total of 1,498 publications from 95 areas were examined, of which the United States was determined to be the most influential country in this research field. The largest cluster labeled “Brownian motion” was used prior to the application of AI for ophthalmic diagnosis from 2007 to 2017, and was an active topic during this period. The burst keywords in the period from 2020 to 2021 were system, disease, and model.Conclusion: The focus of artificial intelligence research in ophthalmic disease diagnosis has transitioned from the development of AI algorithms and the analysis of abnormal eye physiological structure to the investigation of more mature ophthalmic disease diagnosis systems. However, there is a need for further studies in ophthalmology and computer engineering.

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“…This study selected this as the baseline model. Additionally, U-Net has been adopted as a classic model for medical image segmentation [ 35 ]. Slices of size 512 × 512 were input into the baseline models and ResNet-101 was selected as the backbone of DeepLab V3+.…”

Section: Methodsmentioning

confidence: 99%

MVI-Mind: A Novel Deep-Learning Strategy Using Computed Tomography (CT)-Based Radiomics for End-to-End High Efficiency Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Wang

et al. 2022

Cancers

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Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) directly affects a patient’s prognosis. The development of preoperative noninvasive diagnostic methods is significant for guiding optimal treatment plans. In this study, we investigated 138 patients with HCC and presented a novel end-to-end deep learning strategy based on computed tomography (CT) radiomics (MVI-Mind), which integrates data preprocessing, automatic segmentation of lesions and other regions, automatic feature extraction, and MVI prediction. A lightweight transformer and a convolutional neural network (CNN) were proposed for the segmentation and prediction modules, respectively. To demonstrate the superiority of MVI-Mind, we compared the framework’s performance with that of current, mainstream segmentation, and classification models. The test results showed that MVI-Mind returned the best performance in both segmentation and prediction. The mean intersection over union (mIoU) of the segmentation module was 0.9006, and the area under the receiver operating characteristic curve (AUC) of the prediction module reached 0.9223. Additionally, it only took approximately 1 min to output a prediction for each patient, end-to-end using our computing device, which indicated that MVI-Mind could noninvasively, efficiently, and accurately predict the presence of MVI in HCC patients before surgery. This result will be helpful for doctors to make rational clinical decisions.

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Optimized-Unet: Novel Algorithm for Parapapillary Atrophy Segmentation

Cited by 13 publications

References 25 publications

Emerging Trends and Research Foci in Artificial Intelligence for Retinal Diseases: Bibliometric and Visualization Study

Emerging Trends and Research Foci in Artificial Intelligence for Retinal Diseases: Bibliometric and Visualization Study

Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis

MVI-Mind: A Novel Deep-Learning Strategy Using Computed Tomography (CT)-Based Radiomics for End-to-End High Efficiency Prediction of Microvascular Invasion in Hepatocellular Carcinoma

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