scholarly journals A Multimodal Imaging–Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study

10.2196/28868 ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. e28868
Author(s):  
Eugene Yu-Chuan Kang ◽  
Ling Yeung ◽  
Yi-Lun Lee ◽  
Cheng-Hsiu Wu ◽  
Shu-Yen Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Multimodal Imaging ◽  
Vascular Diseases ◽  
Learning Model ◽  
Age Related Macular Degeneration ◽  
Fundus Photography ◽  
Retinal Diseases ◽  
Ophthalmic Imaging ◽  
Retinal Fundus ◽  
Deep Learning Model

Background Retinal vascular diseases, including diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), myopic choroidal neovascularization (mCNV), and branch and central retinal vein occlusion (BRVO/CRVO), are considered vision-threatening eye diseases. However, accurate diagnosis depends on multimodal imaging and the expertise of retinal ophthalmologists. Objective The aim of this study was to develop a deep learning model to detect treatment-requiring retinal vascular diseases using multimodal imaging. Methods This retrospective study enrolled participants with multimodal ophthalmic imaging data from 3 hospitals in Taiwan from 2013 to 2019. Eye-related images were used, including those obtained through retinal fundus photography, optical coherence tomography (OCT), and fluorescein angiography with or without indocyanine green angiography (FA/ICGA). A deep learning model was constructed for detecting DME, nAMD, mCNV, BRVO, and CRVO and identifying treatment-requiring diseases. Model performance was evaluated and is presented as the area under the curve (AUC) for each receiver operating characteristic curve. Results A total of 2992 eyes of 2185 patients were studied, with 239, 1209, 1008, 211, 189, and 136 eyes in the control, DME, nAMD, mCNV, BRVO, and CRVO groups, respectively. Among them, 1898 eyes required treatment. The eyes were divided into training, validation, and testing groups in a 5:1:1 ratio. In total, 5117 retinal fundus photos, 9316 OCT images, and 20,922 FA/ICGA images were used. The AUCs for detecting mCNV, DME, nAMD, BRVO, and CRVO were 0.996, 0.995, 0.990, 0.959, and 0.988, respectively. The AUC for detecting treatment-requiring diseases was 0.969. From the heat maps, we observed that the model could identify retinal vascular diseases. Conclusions Our study developed a deep learning model to detect retinal diseases using multimodal ophthalmic imaging. Furthermore, the model demonstrated good performance in detecting treatment-requiring retinal diseases.

scholarly journals A Multimodal Imaging–Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study (Preprint)

2021 ◽  
Author(s):  
Eugene Yu-Chuan Kang ◽  
Ling Yeung ◽  
Yi-Lun Lee ◽  
Cheng-Hsiu Wu ◽  
Shu-Yen Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Multimodal Imaging ◽  
Vascular Diseases ◽  
Learning Model ◽  
Age Related Macular Degeneration ◽  
Fundus Photography ◽  
Retinal Diseases ◽  
Ophthalmic Imaging ◽  
Retinal Fundus ◽  
Deep Learning Model

BACKGROUND Retinal vascular diseases, including diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), myopic choroidal neovascularization (mCNV), and branch and central retinal vein occlusion (BRVO/CRVO), are considered vision-threatening eye diseases. However, accurate diagnosis depends on multimodal imaging and the expertise of retinal ophthalmologists. OBJECTIVE The aim of this study was to develop a deep learning model to detect treatment-requiring retinal vascular diseases using multimodal imaging. METHODS This retrospective study enrolled participants with multimodal ophthalmic imaging data from 3 hospitals in Taiwan from 2013 to 2019. Eye-related images were used, including those obtained through retinal fundus photography, optical coherence tomography (OCT), and fluorescein angiography with or without indocyanine green angiography (FA/ICGA). A deep learning model was constructed for detecting DME, nAMD, mCNV, BRVO, and CRVO and identifying treatment-requiring diseases. Model performance was evaluated and is presented as the area under the curve (AUC) for each receiver operating characteristic curve. RESULTS A total of 2992 eyes of 2185 patients were studied, with 239, 1209, 1008, 211, 189, and 136 eyes in the control, DME, nAMD, mCNV, BRVO, and CRVO groups, respectively. Among them, 1898 eyes required treatment. The eyes were divided into training, validation, and testing groups in a 5:1:1 ratio. In total, 5117 retinal fundus photos, 9316 OCT images, and 20,922 FA/ICGA images were used. The AUCs for detecting mCNV, DME, nAMD, BRVO, and CRVO were 0.996, 0.995, 0.990, 0.959, and 0.988, respectively. The AUC for detecting treatment-requiring diseases was 0.969. From the heat maps, we observed that the model could identify retinal vascular diseases. CONCLUSIONS Our study developed a deep learning model to detect retinal diseases using multimodal ophthalmic imaging. Furthermore, the model demonstrated good performance in detecting treatment-requiring retinal diseases.

scholarly journals DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs

Ophthalmology ◽  
2019 ◽  
Vol 126 (4) ◽  
pp. 565-575 ◽  
Author(s):  
Yifan Peng ◽  
Shazia Dharssi ◽  
Qingyu Chen ◽  
Tiarnan D. Keenan ◽  
Elvira Agrón ◽  
...  
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Learning Model ◽  
Age Related Macular Degeneration ◽  
Automated Classification ◽  
Age Related ◽  
Fundus Photographs ◽  
Deep Learning Model

scholarly journals Correction: A deep learning model for the detection of both advanced and early glaucoma using fundus photography

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0211579 ◽  
Author(s):  
Jin Mo Ahn ◽  
Sangsoo Kim ◽  
Kwang-Sung Ahn ◽  
Sung-Hoon Cho ◽  
Kwan Bok Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Fundus Photography ◽  
Early Glaucoma ◽  
Deep Learning Model

scholarly journals Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation

10.2196/23472 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e23472
Author(s):  
Eugene Yu-Chuan Kang ◽  
Yi-Ting Hsieh ◽  
Chien-Hung Li ◽  
Yi-Jin Huang ◽  
Chang-Fu Kuo ◽  
...  
Keyword(s):  
Renal Function ◽  
Deep Learning ◽  
Learning Model ◽  
Fundus Images ◽  
Renal Function Impairment ◽  
Function Impairment ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Deep Learning Model ◽  
Hba1c Levels

Background Retinal imaging has been applied for detecting eye diseases and cardiovascular risks using deep learning–based methods. Furthermore, retinal microvascular and structural changes were found in renal function impairments. However, a deep learning–based method using retinal images for detecting early renal function impairment has not yet been well studied. Objective This study aimed to develop and evaluate a deep learning model for detecting early renal function impairment using retinal fundus images. Methods This retrospective study enrolled patients who underwent renal function tests with color fundus images captured at any time between January 1, 2001, and August 31, 2019. A deep learning model was constructed to detect impaired renal function from the images. Early renal function impairment was defined as estimated glomerular filtration rate <90 mL/min/1.73 m2. Model performance was evaluated with respect to the receiver operating characteristic curve and area under the curve (AUC). Results In total, 25,706 retinal fundus images were obtained from 6212 patients for the study period. The images were divided at an 8:1:1 ratio. The training, validation, and testing data sets respectively contained 20,787, 2189, and 2730 images from 4970, 621, and 621 patients. There were 10,686 and 15,020 images determined to indicate normal and impaired renal function, respectively. The AUC of the model was 0.81 in the overall population. In subgroups stratified by serum hemoglobin A1c (HbA1c) level, the AUCs were 0.81, 0.84, 0.85, and 0.87 for the HbA1c levels of ≤6.5%, >6.5%, >7.5%, and >10%, respectively. Conclusions The deep learning model in this study enables the detection of early renal function impairment using retinal fundus images. The model was more accurate for patients with elevated serum HbA1c levels.

scholarly journals Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jinho Lee ◽  
Young Kook Kim ◽  
Ahnul Ha ◽  
Sukkyu Sun ◽  
Yong Woo Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ganglion Cell ◽  
Layer Thickness ◽  
Plexiform Layer ◽  
Learning Model ◽  
Fundus Photography ◽  
Inner Plexiform Layer ◽  
Thickness Prediction ◽  
Deep Learning Model

scholarly journals A deep learning model for the detection of both advanced and early glaucoma using fundus photography

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0207982 ◽  
Author(s):  
Jin Mo Ahn ◽  
Sangsoo Kim ◽  
Kwang-Sung Ahn ◽  
Sung-Hoon Cho ◽  
Kwan Bok Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Fundus Photography ◽  
Early Glaucoma ◽  
Deep Learning Model

scholarly journals Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Eugene Yu-Chuan Kang ◽  
Yi-Ting Hsieh ◽  
Chien-Hung Li ◽  
Yi-Jin Huang ◽  
Chang-Fu Kuo ◽  
...  
Keyword(s):  
Renal Function ◽  
Deep Learning ◽  
Impaired Renal Function ◽  
Learning Model ◽  
Fundus Images ◽  
Renal Function Impairment ◽  
Function Impairment ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Deep Learning Model

BACKGROUND Retinal imaging has been applied for detecting eye diseases and cardiovascular risks using deep learning–based methods. Furthermore, retinal microvascular and structural changes were found in renal function impairments. However, a deep learning–based method using retinal images for detecting early renal function impairment has not yet been well studied. OBJECTIVE This study aimed to develop and evaluate a deep learning model for detecting early renal function impairment using retinal fundus images. METHODS This retrospective study enrolled patients who underwent renal function tests with color fundus images captured at any time between January 1, 2001, and August 31, 2019. A deep learning model was constructed to detect impaired renal function from the images. Early renal function impairment was defined as estimated glomerular filtration rate &lt;90 mL/min/1.73 m<sup>2</sup>. Model performance was evaluated with respect to the receiver operating characteristic curve and area under the curve (AUC). RESULTS In total, 25,706 retinal fundus images were obtained from 6212 patients for the study period. The images were divided at an 8:1:1 ratio. The training, validation, and testing data sets respectively contained 20,787, 2189, and 2730 images from 4970, 621, and 621 patients. There were 10,686 and 15,020 images determined to indicate normal and impaired renal function, respectively. The AUC of the model was 0.81 in the overall population. In subgroups stratified by serum hemoglobin A<sub>1c</sub> (HbA<sub>1c</sub>) level, the AUCs were 0.81, 0.84, 0.85, and 0.87 for the HbA<sub>1c</sub> levels of ≤6.5%, &gt;6.5%, &gt;7.5%, and &gt;10%, respectively. CONCLUSIONS The deep learning model in this study enables the detection of early renal function impairment using retinal fundus images. The model was more accurate for patients with elevated serum HbA<sub>1c</sub> levels. CLINICALTRIAL

Deep Learning Model Selection of Suboptimal Complexity

2018 ◽  
pp. 129-147
Author(s):  
Oleg Bakhteev ◽  
◽  
Vadim Strijov ◽  
Keyword(s):  
Deep Learning ◽  
Model Selection ◽  
Learning Model ◽  
Deep Learning Model ◽  
Selection Of

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance

2019 ◽  
Vol 59 (7) ◽  
pp. 3240-3250 ◽  
Author(s):  
Chunlai Feng ◽  
Hengwei Chen ◽  
Xianqin Yuan ◽  
Mengqiu Sun ◽  
Kexin Chu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Liver Injury ◽  
Gene Expression Data ◽  
Learning Model ◽  
Accurate Prediction ◽  
Expression Data ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Deep Learning Model
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