Detection of oedema on optical coherence tomography images using deep learning model trained on noisy clinical data

2021 ◽  
Author(s):  
Ivan Potapenko ◽  
Mads Kristensen ◽  
Bo Thiesson ◽  
Tomas Ilginis ◽  
Torben Lykke Sørensen ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Clinical Data ◽  
Learning Model ◽  
Optical Coherence ◽  
Deep Learning Model

Denoising Swept Source Optical Coherence Tomography Volumetric Scans using a Deep Learning Model

Retina ◽  
2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Gerardo Ledesma-Gil ◽  
Zaixing Mao ◽  
Jonathan Liu ◽  
Richard F. Spaide
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Model ◽  
Optical Coherence ◽  
Swept Source ◽  
Deep Learning Model

Deep Learning Model Based on Optical Coherence Tomography for Automated Detection of Pathologic Myopia: Prediction Model Development Study (Preprint)

2021 ◽  
Author(s):  
So Jin Park ◽  
Tae Hoon Ko ◽  
Chan Kee Park ◽  
Yong Chan Kim ◽  
In Young Choi
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Characteristic Curve ◽  
Model Development ◽  
Learning Model ◽  
Pathologic Myopia ◽  
Optical Coherence ◽  
Timely Manner ◽  
Model Based ◽  
Deep Learning Model

BACKGROUND Pathologic myopia is a disease that causes vision impairment and blindness. Therefore, it is essential to diagnose it in a timely manner. However, there is no standardized definition for pathologic myopia, and the interpretation of pathologic myopia by optical coherence tomography is subjective and requires considerable time and money. Therefore, there is a need for a diagnostic tool that can diagnose pathologic myopia in patients automatically and in a timely manner. OBJECTIVE The purpose of this study was to develop an algorithm that uses optical coherence tomography (OCT) to automatically diagnose patients with pathologic myopia who require treatment. METHODS This study was conducted using patient data from patients who underwent optical coherence tomography tests at the Ophthalmology Department of Incheon St. Mary's Hospital and Seoul St. Mary's Hospital from January 2012 to May 2020. To automatically diagnose pathologic myopia, a deep learning model was developed using 3D optical coherence tomography images. A model was developed using transfer learning based on four pre-trained convolutional neural networks (ResNet18, ResNext50, EfficientNetB0, EfficientNetB4). The performance of each model was evaluated and compared based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). RESULTS Four models developed using test datasets were evaluated and compared. The model based on EfficientNetB4 showed the best performance (95% accuracy, 93% sensitivity, 96% specificity, and 98% AUROC). CONCLUSIONS In our study, we developed a deep learning model that can automatically diagnose pathologic myopia without segmentation of 3D optical coherence tomography images. Our deep learning model based on EfficientNetB4 demonstrated excellent performance in identifying pathologic myopia.

scholarly journals Cardiac or Infectious? Transfer Learning with Chest X-Rays for ER Patient Classification

2020 ◽  
Author(s):  
Jonathan Stubblefield ◽  
Mitchell Hervert ◽  
Jason Causey ◽  
Jake Qualls ◽  
Wei Dong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Data ◽  
Image Data ◽  
Learning Model ◽  
Patient Classification ◽  
X Rays ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model

AbstractOne of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We evaluated ER patient classification for cardiac and infection causes with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. We also conducted clinical feature importance analysis and identified the most important clinical features for ER patient classification. This model can be upgraded to include a SARS-CoV-2 specific classification with COVID-19 patients data. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/.Data statementThe clinical data and chest x-ray image data for this study were collected and prepared by the residents and researchers of the Joint Translational Research Lab of Arkansas State University (A-State) and St. Bernards Medical Center (SBMC) Internal Medicine Residency Program. As data collection is on-going for the project stage-II of clinical testing, raw data is not currently available for data sharing to the public.EthicsThis study was approved by the St. Bernards Medical Center’s Institutional Review Board (IRB).

scholarly journals A Feature Optimized Deep Learning Model for Clinical Data Mining

2020 ◽  
Vol 29 (3) ◽  
pp. 476-481
Author(s):  
Tianshu Wu ◽  
Shuyu Chen ◽  
Yingming Tian ◽  
Peng Wu
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Clinical Data ◽  
Learning Model ◽  
Clinical Data Mining ◽  
Deep Learning Model

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.

scholarly journals Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images

2020 ◽  
pp. bjophthalmol-2020-317825
Author(s):  
Yonghao Li ◽  
Weibo Feng ◽  
Xiujuan Zhao ◽  
Bingqian Liu ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
High Myopia ◽  
Large Scale ◽  
Learning System ◽  
Youden Index ◽  
Optical Coherence ◽  
Test Dataset ◽  
Independent Test ◽  
Independent Test Dataset

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

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