[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Li et al., 2020 - Google Patents

Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images

Li et al., 2020

View HTML @Full View
Document ID
6357110306098336823
Author
Li Z
Guo C
Nie D
Lin D
Zhu Y
Chen C
Wu X
Xu F
Jin C
Zhang X
Xiao H
Zhang K
Zhao L
Yan P
Lai W
Li J
Feng W
Li Y
Wei Ting D
Lin H
Publication year
Publication venue
Communications biology

External Links

Snippet

Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachment screening is time …
Continue reading at www.nature.com (HTML) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G06F19/322Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • G06T2207/30041Eye; Retina; Ophthalmic
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/10Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
    • A61B3/12Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/10Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
    • A61B3/102Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for optical coherence tomography [OCT]
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/0016Operational features thereof
    • A61B3/0025Operational features thereof characterised by electronic signal processing, e.g. eye models
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/0016Operational features thereof
    • A61B3/0033Operational features thereof characterised by user input arrangements
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00597Acquiring or recognising eyes, e.g. iris verification

Similar Documents

Publication Publication Date Title
Li et al. Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images
Christopher et al. Performance of deep learning architectures and transfer learning for detecting glaucomatous optic neuropathy in fundus photographs
Shibata et al. Development of a deep residual learning algorithm to screen for glaucoma from fundus photography
Fenner et al. Advances in retinal imaging and applications in diabetic retinopathy screening: a review
Keel et al. Development and validation of a deep‐learning algorithm for the detection of neovascular age‐related macular degeneration from colour fundus photographs
Kuo et al. A deep learning approach in diagnosing fungal keratitis based on corneal photographs
Nagasawa et al. Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy
Li et al. A deep learning system for identifying lattice degeneration and retinal breaks using ultra-widefield fundus images
JP2021507428A (en) Diagnosis and referral based on deep learning of ophthalmic diseases and disorders
Sarao et al. Automated diabetic retinopathy detection with two different retinal imaging devices using artificial intelligence: a comparison study
Lu et al. Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images
Zhang et al. Development of a deep-learning system for detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus images: a pilot study
Tham et al. Detecting visually significant cataract using retinal photograph-based deep learning
Nikolaidou et al. Teleophthalmology and artificial intelligence as game changers in ophthalmic care after the COVID-19 pandemic
Jammal et al. Detecting retinal nerve fibre layer segmentation errors on spectral domain-optical coherence tomography with a deep learning algorithm
Hwang et al. Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography
Sun et al. Deep learning for the detection of multiple fundus diseases using ultra-widefield images
Liu et al. Application of artificial intelligence-based dual-modality analysis combining fundus photography and optical coherence tomography in diabetic retinopathy screening in a community hospital
Cho et al. Computer-aided recognition of myopic tilted optic disc using deep learning algorithms in fundus photography
Rim et al. Deep learning for automated sorting of retinal photographs
Mao et al. An artificial intelligence platform for the diagnosis and surgical planning of strabismus using corneal light-reflection photos
Lupidi et al. Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting
Lemij et al. Characteristics of a large, labeled data set for the training of artificial intelligence for glaucoma screening with fundus photographs
Kim et al. Development of a deep learning system to detect glaucoma using macular vertical optical coherence tomography scans of myopic eyes
Zhou et al. Deep learning for automatic detection of recurrent retinal detachment after surgery using ultra‐widefield fundus images: a single‐center study