scholarly journals Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ayumi Koyama ◽  
Dai Miyazaki ◽  
Yuji Nakagawa ◽  
Yuji Ayatsuka ◽  
Hitomi Miyake ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Accurate Method ◽  
Gradient Boosting ◽  
Computer Assisted ◽  
Causative Pathogen ◽  
Infectious Keratitis ◽  
Slit Lamp ◽  
Deep Learning Algorithm

AbstractCorneal opacities are important causes of blindness, and their major etiology is infectious keratitis. Slit-lamp examinations are commonly used to determine the causative pathogen; however, their diagnostic accuracy is low even for experienced ophthalmologists. To characterize the “face” of an infected cornea, we have adapted a deep learning architecture used for facial recognition and applied it to determine a probability score for a specific pathogen causing keratitis. To record the diverse features and mitigate the uncertainty, batches of probability scores of 4 serial images taken from many angles or fluorescence staining were learned for score and decision level fusion using a gradient boosting decision tree. A total of 4306 slit-lamp images including 312 images obtained by internet publications on keratitis by bacteria, fungi, acanthamoeba, and herpes simplex virus (HSV) were studied. The created algorithm had a high overall accuracy of diagnosis, e.g., the accuracy/area under the curve for acanthamoeba was 97.9%/0.995, bacteria was 90.7%/0.963, fungi was 95.0%/0.975, and HSV was 92.3%/0.946, by group K-fold validation, and it was robust to even the low resolution web images. We suggest that our hybrid deep learning-based algorithm be used as a simple and accurate method for computer-assisted diagnosis of infectious keratitis.

scholarly journals Determination of Probability of Causative Pathogen in Infectious Keratitis Using Deep Learning Algorithm of Slit-Lamp Images

2021 ◽  
Author(s):  
Ayumi Koyama ◽  
Dai Miyazaki ◽  
Yuji Nakagawa ◽  
Yuji Ayatsuka ◽  
Hitomi Miyake ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Accurate Method ◽  
Gradient Boosting ◽  
Computer Assisted ◽  
Causative Pathogen ◽  
Infectious Keratitis ◽  
Slit Lamp ◽  
Deep Learning Algorithm

Abstract Corneal opacities are an important cause of blindness, and its major etiology is infectious keratitis. Slit-lamp examinations are commonly used to determine the causative pathogen; however, their diagnostic accuracy is low even for experienced ophthalmologists. To characterize the “face” of an infected cornea, we have adapted a deep learning architecture used for facial recognition and applied it to determine a probability score for a specific pathogen causing keratitis. To record the diverse features and mitigate the uncertainty, batches of probability scores of 4 serial images taken from many angles or fluorescence staining were learned for score and decision level fusion using a gradient boosting decision tree. A total of 4306 slit-lamp images and 312 images obtained by internet publications on keratitis by bacteria, fungi, acanthamoeba, and herpes simplex virus (HSV) were studied. The created algorithm had a high overall accuracy of diagnosis, e.g., the accuracy/area under the curve (AUC) for acanthamoeba was 97.9%/0.995, bacteria was 90.7%/0.963, fungi was 95.0%/0.975, and HSV was 92.3%/0.946, by group K-fold validation, and it was robust to even the low resolution web images. We suggest that our hybrid deep learning-based algorithm be used as a simple and accurate method for computer-assisted diagnosis of infectious keratitis.

scholarly journals Using Slit-Lamp Images for Deep Learning–Based Identification of Bacterial and Fungal Keratitis

2021 ◽  
Author(s):  
Ning Hung ◽  
Eugene Yu-Chuan Kang ◽  
Andy Guan-Yu Shih ◽  
Chi-Hung Lin ◽  
Ming‐Tse Kuo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Learning Algorithm ◽  
Model Performance ◽  
Area Under The Curve ◽  
Fungal Keratitis ◽  
Classification Model ◽  
Mapping Technique ◽  
Slit Lamp ◽  
Deep Learning Algorithm

In this study, we aimed to develop a deep learning model for identifying bacterial keratitis (BK) and fungal keratitis (FK) by using slit-lamp images. We retrospectively collected slit-lamp images of patients with culture-proven microbial keratitis between January 1, 2010, and December 31, 2019, from two medical centers in Taiwan. We constructed a deep learning algorithm, consisting of a segmentation model for cropping cornea images and a classification model that applies convolutional neural networks to differentiate between FK and BK. The model performance was evaluated and presented as the area under the curve (AUC) of the receiver operating characteristic curves. A gradient-weighted class activation mapping technique was used to plot the heatmap of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved an average diagnostic accuracy of 80.00%. The diagnostic accuracy for BK ranged from 79.59% to 95.91% and that for FK ranged from 26.31% to 63.15%. DenseNet169 showed the best model performance, with an AUC of 0.78 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists.

scholarly journals Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 250
Author(s):  
Yejin Jeon ◽  
Kyeorye Lee ◽  
Leonard Sunwoo ◽  
Dongjun Choi ◽  
Dong Yul Oh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Imaging Modality ◽  
Maxillary Sinusitis ◽  
Image Interpretation ◽  
Area Under The Curve ◽  
Temporal Separation ◽  
Deep Learning Algorithm ◽  
Line Imaging ◽  
The One

Accurate image interpretation of Waters’ and Caldwell view radiographs used for sinusitis screening is challenging. Therefore, we developed a deep learning algorithm for diagnosing frontal, ethmoid, and maxillary sinusitis on both Waters’ and Caldwell views. The datasets were selected for the training and validation set (n = 1403, sinusitis% = 34.3%) and the test set (n = 132, sinusitis% = 29.5%) by temporal separation. The algorithm can simultaneously detect and classify each paranasal sinus using both Waters’ and Caldwell views without manual cropping. Single- and multi-view models were compared. Our proposed algorithm satisfactorily diagnosed frontal, ethmoid, and maxillary sinusitis on both Waters’ and Caldwell views (area under the curve (AUC), 0.71 (95% confidence interval, 0.62–0.80), 0.78 (0.72–0.85), and 0.88 (0.84–0.92), respectively). The one-sided DeLong’s test was used to compare the AUCs, and the Obuchowski–Rockette model was used to pool the AUCs of the radiologists. The algorithm yielded a higher AUC than radiologists for ethmoid and maxillary sinusitis (p = 0.012 and 0.013, respectively). The multi-view model also exhibited a higher AUC than the single Waters’ view model for maxillary sinusitis (p = 0.038). Therefore, our algorithm showed diagnostic performances comparable to radiologists and enhanced the value of radiography as a first-line imaging modality in assessing multiple sinusitis.

scholarly journals Using Slit-Lamp Images for Deep Learning-Based Identification of Bacterial and Fungal Keratitis: Model Development and Validation with Different Convolutional Neural Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1246
Author(s):  
Ning Hung ◽  
Andy Kuan-Yu Shih ◽  
Chihung Lin ◽  
Ming-Tse Kuo ◽  
Yih-Shiou Hwang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Convolutional Neural Networks ◽  
Learning Algorithm ◽  
Model Performance ◽  
Fungal Keratitis ◽  
Classification Model ◽  
Slit Lamp ◽  
Deep Learning Algorithm

In this study, we aimed to develop a deep learning model for identifying bacterial keratitis (BK) and fungal keratitis (FK) by using slit-lamp images. We retrospectively collected slit-lamp images of patients with culture-proven microbial keratitis between 1 January 2010 and 31 December 2019 from two medical centers in Taiwan. We constructed a deep learning algorithm consisting of a segmentation model for cropping cornea images and a classification model that applies different convolutional neural networks (CNNs) to differentiate between FK and BK. The CNNs included DenseNet121, DenseNet161, DenseNet169, DenseNet201, EfficientNetB3, InceptionV3, ResNet101, and ResNet50. The model performance was evaluated and presented as the area under the curve (AUC) of the receiver operating characteristic curves. A gradient-weighted class activation mapping technique was used to plot the heat map of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved the highest average accuracy of 80.0%. Using different CNNs, the diagnostic accuracy for BK ranged from 79.6% to 95.9%, and that for FK ranged from 26.3% to 65.8%. The CNN of DenseNet161 showed the best model performance, with an AUC of 0.85 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed a better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists.

scholarly journals Construction of cardiac arrhythmia prediction model using deep learning and gradient boosting

2021 ◽  
Vol 13 (3) ◽  
pp. 114-119
Author(s):  
Dhanar Bintang Pratama ◽  
Favian Dewanta ◽  
Syamsul Rizal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Medical Staff ◽  
Heart Attack ◽  
Learning Algorithm ◽  
Gradient Boosting ◽  
Ecg Signal ◽  
Deep Learning Algorithm ◽  
Boosting Algorithm

Arrhythmia is a condition in which the rhythm of heartbeat becomes irregular. This condition in extreme cases can lead to fatal heart attack accidents. In order to reduce heart attack risk, appropriate early treatments should be conducted right after getting results of Arrhythmia condition, which is generated by electrocardiography ECG tools. However, reading ECG results should be done by qualified medical staff in order to diagnose the existence of arrhythmia accurately. This paper proposes a deep learning algorithm method to classify and detect the existence of arrhythmia from ECG reading. Our proposed method relies on Convolutional Neural Network (CNN) to extract feature from a single lead ECG signal and also Gradient Boosting algorithm to predict the final outcome of single lead ECG reading. This method achieved the accuracy of 96.18% and minimized the number of parameters used in CNN Layer.

scholarly journals Detecting objects behind scattering media using vortex beams and deep learning

2021 ◽  
Author(s):  
Ganesh M. Balasubramaniam ◽  
Netanel Biton ◽  
Shlomi Arnon
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Computer Assisted Surgery ◽  
Computer Assisted ◽  
Scattering Media ◽  
Deep Learning Algorithm ◽  
Main Challenge ◽  
Vortex Beams ◽  
Set Up

Abstract Reconstructing objects behind scattering media is a challenging issue with applications in biomedical imaging, non-distractive testing, computer-assisted surgery, and autonomous vehicular systems. Such systems’ main challenge is the multiple scattering of the photons in the angular and spatial domain, which results in a blurred image. Previous works try to improve the reconstructing ability using deep learning algorithms, with some success. We enhance these methods by illuminating the set-up using several modes of vortex beams obtaining a series of time-gated images corresponding to each mode. The images are accurately reconstructed using a deep learning algorithm by analyzing the pattern captured in the camera. This study shows that using vortex beams instead of Gaussian beams enhances the deep learning algorithm’s image reconstruction ability in terms of the peak signal-to-noise ratio (PSNR) by ~ 2.5 dB and ~1 dB when low and high scattering scatterers are used respectively.

scholarly journals Representation Learning for Motor Imagery Recognition with Deep Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 112
Author(s):  
Fangzhou Xu ◽  
Fenqi Rong ◽  
Yunjing Miao ◽  
Yanan Sun ◽  
Gege Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Motor Imagery ◽  
Learning Algorithm ◽  
Representation Learning ◽  
Training Data ◽  
Gradient Boosting ◽  
Future Research ◽  
Deep Learning Algorithm ◽  
The Brain

This study describes a method for classifying electrocorticograms (ECoGs) based on motor imagery (MI) on the brain–computer interface (BCI) system. This method is different from the traditional feature extraction and classification method. In this paper, the proposed method employs the deep learning algorithm for extracting features and the traditional algorithm for classification. Specifically, we mainly use the convolution neural network (CNN) to extract the features from the training data and then classify those features by combing with the gradient boosting (GB) algorithm. The comprehensive study with CNN and GB algorithms will profoundly help us to obtain more feature information from brain activities, enabling us to obtain the classification results from human body actions. The performance of the proposed framework has been evaluated on the dataset I of BCI Competition III. Furthermore, the combination of deep learning and traditional algorithms provides some ideas for future research with the BCI systems.

scholarly journals Deep Learning Prediction of Biomarkers from Echocardiogram Videos

2021 ◽  
Author(s):  
J Weston Hughes ◽  
Neal Yuan ◽  
Bryan He ◽  
Jiahong Ouyang ◽  
Joseph Ebinger ◽  
...  
Keyword(s):  
Deep Learning ◽  
Troponin I ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Clinical Value ◽  
Disease States ◽  
Deep Learning Algorithm ◽  
Phenotypic Information ◽  
Additional Value ◽  
Physiologic State

AbstractLaboratory blood testing is routinely used to assay biomarkers to provide information on physiologic state beyond what clinicians can evaluate from interpreting medical imaging. We hypothesized that deep learning interpretation of echocardiogram videos can provide additional value in understanding disease states and can predict common biomarkers results. Using 70,066 echocardiograms and associated biomarker results from 39,460 patients, we developed EchoNet-Labs, a video-based deep learning algorithm to predict anemia, elevated B-type natriuretic peptide (BNP), troponin I, and blood urea nitrogen (BUN), and abnormal levels in ten additional lab tests. On held-out test data across different healthcare systems, EchoNet-Labs achieved an area under the curve (AUC) of 0.80 in predicting anemia, 0.82 in predicting elevated BNP, 0.75 in predicting elevated troponin I, and 0.69 in predicting elevated BUN. We further demonstrate the utility of the model in predicting abnormalities in 10 additional lab tests. We investigate the features necessary for EchoNet-Labs to make successful predictions and identify potential prediction mechanisms for each biomarker using well-known and novel explainability techniques. These results show that deep learning applied to diagnostic imaging can provide additional clinical value and identify phenotypic information beyond current imaging interpretation methods.

scholarly journals Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hao Gu ◽  
Youwen Guo ◽  
Lei Gu ◽  
Anji Wei ◽  
Shirong Xie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ocular Surface ◽  
Learning Algorithm ◽  
Corneal Dystrophy ◽  
Disease Diagnosis ◽  
Eye Diseases ◽  
Eye Disease ◽  
Computer Assisted ◽  
Infectious Keratitis ◽  
Corneal Disease

Abstract To demonstrate the identification of corneal diseases using a novel deep learning algorithm. A novel hierarchical deep learning network, which is composed of a family of multi-task multi-label learning classifiers representing different levels of eye diseases derived from a predefined hierarchical eye disease taxonomy was designed. Next, we proposed a multi-level eye disease-guided loss function to learn the fine-grained variability of eye diseases features. The proposed algorithm was trained end-to-end directly using 5,325 ocular surface images from a retrospective dataset. Finally, the algorithm’s performance was tested against 10 ophthalmologists in a prospective clinic-based dataset with 510 outpatients newly enrolled with diseases of infectious keratitis, non-infectious keratitis, corneal dystrophy or degeneration, and corneal neoplasm. The area under the ROC curve of the algorithm for each corneal disease type was over 0.910 and in general it had sensitivity and specificity similar to or better than the average values of all ophthalmologists. Confusion matrices revealed similarities in misclassification between human experts and the algorithm. In addition, our algorithm outperformed over all four previous reported methods in identified corneal diseases. The proposed algorithm may be useful for computer-assisted corneal disease diagnosis.

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