AI Medical Compendium Topic

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Tomography, Optical Coherence

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Validation of Soft Labels in Developing Deep Learning Algorithms for Detecting Lesions of Myopic Maculopathy From Optical Coherence Tomographic Images.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
PURPOSE: It is common for physicians to be uncertain when examining some images. Models trained with human uncertainty could be a help for physicians in diagnosing pathologic myopia.

Deep learning algorithms for detection of diabetic macular edema in OCT images: A systematic review and meta-analysis.

European journal of ophthalmology
PURPOSE: Artificial intelligence (AI) can detect diabetic macular edema (DME) from optical coherence tomography (OCT) images. We aimed to evaluate the performance of deep learning neural networks in DME detection.

Towards more efficient ophthalmic disease classification and lesion location via convolution transformer.

Computer methods and programs in biomedicine
OBJECTIVE: A retina optical coherence tomography (OCT) image differs from a traditional image due to its significant speckle noise, irregularity, and inconspicuous features. A conventional deep learning architecture cannot effectively improve the cla...

Artificial intelligence based detection of age-related macular degeneration using optical coherence tomography with unique image preprocessing.

European journal of ophthalmology
PURPOSE: The aim of the study is to improve the accuracy of age related macular degeneration (AMD) disease in its earlier phases with proposed Capsule Network (CapsNet) architecture trained on speckle noise reduced spectral domain optical coherence t...

Recent Advanced Deep Learning Architectures for Retinal Fluid Segmentation on Optical Coherence Tomography Images.

Sensors (Basel, Switzerland)
With non-invasive and high-resolution properties, optical coherence tomography (OCT) has been widely used as a retinal imaging modality for the effective diagnosis of ophthalmic diseases. The retinal fluid is often segmented by medical experts as a p...

Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images.

Computational intelligence and neuroscience
Retinal abnormalities have emerged as a serious public health concern in recent years and can manifest gradually and without warning. These diseases can affect any part of the retina, causing vision impairment and indeed blindness in extreme cases. T...

Cervical optical coherence tomography image classification based on contrastive self-supervised texture learning.

Medical physics
BACKGROUND: Cervical cancer (CC) seriously affects the health of the female reproductive system. Optical coherence tomography (OCT) emerged as a noninvasive, high-resolution imaging technology for cervical disease detection. However, OCT image annota...

Correlation of choroidal thickness with age in healthy subjects: automatic detection and segmentation using a deep learning model.

International ophthalmology
PROPOSE: The proposed deep learning model with a mask region-based convolutional neural network (Mask R-CNN) can predict choroidal thickness automatically. Changes in choroidal thickness with age can be detected with manual measurements. In this stud...

Proof-of-Concept Analysis of a Deep Learning Model to Conduct Automated Segmentation of OCT Images for Macular Hole Volume.

Ophthalmic surgery, lasers & imaging retina
BACKGROUND AND OBJECTIVE: To determine whether an automated artificial intelligence (AI) model could assess macular hole (MH) volume on swept-source optical coherence tomography (OCT) images.

Automatic segmentation of intraocular lens, the retrolental space and Berger's space using deep learning.

Acta ophthalmologica
PURPOSE: To develop and validate a deep learning model to automatically segment three structures using an anterior segment optical coherence tomography (AS-OCT): The intraocular lens (IOL), the retrolental space (IOL to the posterior lens capsule) an...