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.

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 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 Deep learning in the field of disease diagnosis

2018 ◽  
Vol 7 (2.25) ◽  
pp. 37
Author(s):  
K S. Harish Kumar ◽  
Dijo Micheal Jerald ◽  
A Emmanuel
Keyword(s):  
Deep Learning ◽  
Medical Errors ◽  
Learning Algorithm ◽  
Disease Diagnosis ◽  
Good Treatment ◽  
Test Results ◽  
Medical Field ◽  
Deep Learning Algorithm ◽  
Medical Reports ◽  
Severity Of The Disease

A good treatment is dependent on the accuracy of the diagnosis. The cure for the disease starts with the process of diagnosis. All these years, the grade and standard of the medical field has been increasing exponentially, yet there has been no significant downfall in the rate of unintentional medical errors. These errors can be avoided using Deep learning algorithm to predict the disease. The Deep Learning algorithm scans analyses and compares the patient's report with its dataset and predicts the nature and severity of the disease. The test results from the patient’s report are extracted by using PDF processing. More the medical reports analyzed, more will be the intelligence gained by the algorithm. This will be of great assistance to the doctors as they can interpret their diagnosis with the results predicted by the algorithm.  

scholarly journals A Novel Hierarchical Deep Learning Framework for Diagnosing Multiple Visual Impairment Diseases in the Clinical Environment

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiaxu Hong ◽  
Xiaoqing Liu ◽  
Youwen Guo ◽  
Hao Gu ◽  
Lei Gu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Visual Impairment ◽  
Ocular Surface ◽  
Eye Disease ◽  
Clinical Images ◽  
Learning Framework ◽  
Disease Identification ◽  
Ocular Surface Diseases ◽  
Retinal Fundus ◽  
Level 2

Early detection and treatment of visual impairment diseases are critical and integral to combating avoidable blindness. To enable this, artificial intelligence–based disease identification approaches are vital for visual impairment diseases, especially for people living in areas with a few ophthalmologists. In this study, we demonstrated the identification of a large variety of visual impairment diseases using a coarse-to-fine approach. We designed a 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. A multi-level disease–guided loss function was proposed to learn the fine-grained variability of eye disease features. The proposed framework was trained for both ocular surface and retinal images, independently. The training dataset comprised 7,100 clinical images from 1,600 patients with 100 diseases. To show the feasibility of the proposed framework, we demonstrated eye disease identification on the first two levels of the eye disease taxonomy, namely 7 ocular diseases with 4 ocular surface diseases and 3 retinal fundus diseases in level 1 and 17 subclasses with 9 ocular surface diseases and 8 retinal fundus diseases in level 2. The proposed framework is flexible and extensible, which can be inherently trained on more levels with sufficient training data for each subtype diseases (e.g., the 17 classes of level 2 include 100 subtype diseases defined as level 3 diseases). The performance of the proposed framework was evaluated against 40 board-certified ophthalmologists on clinical cases with various visual impairment diseases and showed that the proposed framework had high sensitivity and specificity with the area under the receiver operating characteristic curve ranging from 0.743 to 0.989 in identifying all identified major causes of blindness. Further assessment of 4,670 cases in a tertiary eye center also demonstrated that the proposed framework achieved a high identification accuracy rate for different visual impairment diseases compared with that of human graders in a clinical setting. The proposed hierarchical deep learning framework would improve clinical practice in ophthalmology and broaden the scope of service available, especially for people living in areas with a few ophthalmologists.

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 Comparative Analysis of Deep Learning Methods for Detection of Keratoconus

2021 ◽  
Vol 23 (06) ◽  
pp. 1546-1553
Author(s):  
Impana N ◽  
◽  
K J Bhoomika ◽  
Suraksha S S ◽  
Karan Sawhney ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Analysis ◽  
Diagnostic Errors ◽  
Detection Algorithm ◽  
Eye Disease ◽  
Test Collection ◽  
Corneal Disease ◽  
Detection Model ◽  
Percent Accuracy ◽  
Deep Learning Model

Keratoconus eye disease is not an inflammatory corneal disease that is caused by progress in thinning of the cornea, scarring, and deformation in the shape of the cornea. In India, there is a significant increase in the number of cases of keratoconus, and several research centers have been paying attention to this disease in recent years. In this situation, there is an immediate need for tools that simplify both diagnosis and treatment[1]. The algorithm developed can decide whether the eye is a normal eye or keratoconus eye with stages. The K-net model analyzes the pentagram images of the eye using a convolutional neural network(CNN) a deep learning model and pre-trained ResNet-50 and InceptionV3 pre-trained models and does the comparative analysis of the accuracies of these models. The results show that the Keratoconus Detection algorithm leads to a good job, with a 93.75 percent accuracy on the data test collection. Keratoconus Detection model is a program that can help ophthalmologists test their patients faster, therefore reducing diagnostic errors and facilitating treatment.

scholarly journals Comparison of Autonomous AS-OCT Deep Learning Algorithm and Clinical Dry Eye Tests in Diagnosis of Dry Eye Disease

2021 ◽  
Vol Volume 15 ◽  
pp. 4281-4289
Author(s):  
Collin Chase ◽  
Amr Elsawy ◽  
Taher Eleiwa ◽  
Eyup Ozcan ◽  
Mohamed Tolba ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dry Eye ◽  
Learning Algorithm ◽  
Dry Eye Disease ◽  
Eye Disease ◽  
Deep Learning Algorithm

scholarly journals A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

2021 ◽  
Vol 7 ◽  
pp. e432
Author(s):  
Bifta Sama Bari ◽  
Md Nahidul Islam ◽  
Mamunur Rashid ◽  
Md Jahid Hasan ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Disease Diagnosis ◽  
Brown Spot ◽  
Real Field ◽  
Computer Assisted ◽  
Identification System ◽  
Rice Leaf ◽  
Leaf Disease ◽  
Leaf Infection

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.

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