Quantitative Assessment of Fundus Tessellated Density and Associated Factors in Fundus Images Using Artificial Intelligence

Shao, Lei; Zhang, Qinglin; Li, Teng; Dong, Li; Zhang, Chuan; Zhou, Wen; Wang, Ya Xing; Wei, Wen Bin

doi:10.1167/tvst.10.9.23

Cited by 26 publications

(47 citation statements)

References 28 publications

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“…Increasing the number of independent samples, repeating the model's training process and verifying the results may increase the model's accuracy. Furthermore, this large-scale cohort study based on the elderly population gathered detailed eye and whole-body data, including axis length, diopter, weight, and other characteristics that may be linked to FTD ( 16 ). Various research on FT risk variables might be added to this foundation.…”

Section: Discussionmentioning

confidence: 99%

“…Gender, age, and FTD are employed as major factors in this work to predict FT classification using machine learning methods. Through an artificial intelligence image processing technology, we extracted the exposed choroid from the fundus, and then calculated the average exposed choroidal area per unit fundus area, which is called FTD ( 16 ).…”

Section: Methodsmentioning

confidence: 99%

“…Recently, with the development of artificial intelligence image processing technology, computer vision and region of interest (ROI) extraction can effectively identify subtle texture differences that cannot be recognized by human eyes ( 15 ). In the past, it has been studied to quantify FT using artificial intelligence methods ( 16 ). The purpose of this study is to obtain the threshold range of each grade of FTD by predicting the manual score of FT through machine learning.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of the Fundus Tessellation Severity With Machine Learning Methods

et al. 2022

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PurposeTo predict the fundus tessellation (FT) severity with machine learning methods.MethodsA population-based cross-sectional study with 3,468 individuals (mean age of 64.6 ± 9.8 years) based on Beijing Eye Study 2011. Participants underwent detailed ophthalmic examinations including fundus images. Five machine learning methods including ordinal logistic regression, ordinal probit regression, ordinal log-gamma regression, ordinal forest and neural network were used.Main Outcome MeasureFT precision, recall, F1-score, weighted-average F1-score and AUC value.ResultsObserved from the in-sample fitting performance, the optimal model was ordinal forest, which had correct classification rate (precision) of 81.28%, while 34.75, 93.73, 70.03, and 24.82% in each classified group by FT severity. The AUC value was 0.7249. And the F1-score was 65.05%, weighted-average F1-score was 79.64% on the whole dataset. For out-of-sample prediction performance, the optimal model was ordinal logistic regression, which had precision of 77.12% on the validation dataset, while 19.57, 92.68, 64.74, and 6.76% in each classified group by FT severity. The AUC value was 0.7187. The classification accuracy of light FT group was the highest, while that of severe FT group was the lowest. And the F1-score was 54.46%, weighted-average F1-score was 74.19% on the whole dataset.ConclusionsThe ordinal forest and ordinal logistic regression model had the strong prediction in-sample and out-sample performance, respectively. The threshold ranges of the ordinal forest model for no FT and light, moderate, severe FT were [0, 0.3078], [0.3078, 0.3347], [0.3347, 0.4048], [0.4048, 1], respectively. Likewise, the threshold ranges of ordinal logistic regression model were ≤ 3.7389, [3.7389, 10.5053], [10.5053, 13.9323], > 13.9323. These results can be applied to guide clinical fundus disease screening and FT severity assessment.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of the Fundus Tessellation Severity With Machine Learning Methods

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“…AI has recently achieved high accuracy in recognizing ocular structure. Deep-learning convolutinal neural networks (CNNs) developed by Li Dong et al (69)(70)(71) have shown superior performance in assessing axial length, subfoveal choroidal thickness, and fundus tessellated density with color fundus photographs. In the diagnosis of multiple ocular disorders, AI outperformed human experts with multimodality imaging, including magnetic resonance imaging (MRI), fundus photographs, and fundus fluorescence angiography (FFA).…”

Section: Discussionmentioning

confidence: 99%

Prognosis Prediction of Uveal Melanoma After Plaque Brachytherapy Based on Ultrasound With Machine Learning

et al. 2022

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IntroductionUveal melanoma (UM) is the most common intraocular malignancy in adults. Plaque brachytherapy remains the dominant eyeball-conserving therapy for UM. Tumor regression in UM after plaque brachytherapy has been reported as a valuable prognostic factor. The present study aimed to develop an accurate machine-learning model to predict the 4-year risk of metastasis and death in UM based on ocular ultrasound data.Material and MethodsA total of 454 patients with UM were enrolled in this retrospective, single-center study. All patients were followed up for at least 4 years after plaque brachytherapy and underwent ophthalmologic evaluations before the therapy. B-scan ultrasonography was used to measure the basal diameters and thickness of tumors preoperatively and postoperatively. Random Forest (RF) algorithm was used to construct two prediction models: whether a patient will survive for more than 4 years and whether the tumor will develop metastasis within 4 years after treatment.ResultsOur predictive model achieved an area under the receiver operating characteristic curve (AUC) of 0.708 for predicting death using only a one-time follow-up record. Including the data from two additional follow-ups increased the AUC of the model to 0.883. We attained AUCs of 0.730 and 0.846 with data from one and three-time follow-up, respectively, for predicting metastasis. The model found that the amount of postoperative follow-up data significantly improved death and metastasis prediction accuracy. Furthermore, we divided tumor treatment response into four patterns. The D(decrease)/S(stable) patterns are associated with a significantly better prognosis than the I(increase)/O(other) patterns.ConclusionsThe present study developed an RF model to predict the risk of metastasis and death from UM within 4 years based on ultrasound follow-up records following plaque brachytherapy. We intend to further validate our model in prospective datasets, enabling us to implement timely and efficient treatments.

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“…The detection of fundus lesions and myopia-related complications in high myopia is an important need, for which deep learning methods such as CNNs already have high accuracy ( Shao et al, 2021 ; Sun et al, 2021 ). Compared to the manual, deep learning methods take only a few hours to a few days in the training phase of the model and can produce instant results when interpreting images.…”

Section: Ai Technology For Myopia Screening and Diagnosismentioning

confidence: 99%

Artificial intelligence technology for myopia challenges: A review

Zhang

Zou

2023

Front. Cell Dev. Biol.

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Myopia is a significant global health concern and affects human visual function, resulting in blurred vision at a distance. There are still many unsolved challenges in this field that require the help of new technologies. Currently, artificial intelligence (AI) technology is dominating medical image and data analysis and has been introduced to address challenges in the clinical practice of many ocular diseases. AI research in myopia is still in its early stages. Understanding the strengths and limitations of each AI method in specific tasks of myopia could be of great value and might help us to choose appropriate approaches for different tasks. This article reviews and elaborates on the technical details of AI methods applied for myopia risk prediction, screening and diagnosis, pathogenesis, and treatment.

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Quantitative Assessment of Fundus Tessellated Density and Associated Factors in Fundus Images Using Artificial Intelligence

Cited by 26 publications

References 28 publications

Prediction of the Fundus Tessellation Severity With Machine Learning Methods

Prediction of the Fundus Tessellation Severity With Machine Learning Methods

Prognosis Prediction of Uveal Melanoma After Plaque Brachytherapy Based on Ultrasound With Machine Learning

Artificial intelligence technology for myopia challenges: A review

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