Cargando…

Suspect glaucoma detection from corneal densitometry supported by machine learning

PURPOSE: To discriminate suspect glaucomatous from control eyes using corneal densitometry based on the statistical modeling of the pixel intensity distribution of Scheimpflug images. METHODS: Twenty-four participants (10 suspect glaucomatous and 14 control eyes) were included in this retrospective...

Descripción completa

Detalles Bibliográficos
Autores principales:	García-Jiménez, Andrés, Consejo, Alejandra
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Elsevier 2022
Materias:	Artificial Intelligence
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732483/ https://www.ncbi.nlm.nih.gov/pubmed/36210294 http://dx.doi.org/10.1016/j.optom.2022.09.002

Ejemplares similares

Can Glaucoma Suspect Data Help to Improve the Performance of Glaucoma Diagnosis?
por: Abbasi, Ashkan, et al.
Publicado: (2023)

Influence of eye tilt on corneal densitometry
por: Consejo, Alejandra, et al.
Publicado: (2022)

Detection of Subclinical Keratoconus With a Validated Alternative Method to Corneal Densitometry
por: Consejo, Alejandra, et al.
Publicado: (2021)

Anomaly detection for blueberry data using sparse autoencoder-support vector machine
por: Wei, Dianwen, et al.
Publicado: (2023)

Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma
por: Thiéry, Alexandre H., et al.
Publicado: (2023)

Classification model for accuracy and intrusion detection using machine learning approach
por: Agarwal, Arushi, et al.
Publicado: (2021)

Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences
por: Horst, Alexander, et al.
Publicado: (2021)

Python code smells detection using conventional machine learning models
por: Sandouka, Rana, et al.
Publicado: (2023)

Deep Learning Model for the Detection of Corneal Edema Before Descemet Membrane Endothelial Keratoplasty on Optical Coherence Tomography Images
por: Bitton, Karen, et al.
Publicado: (2022)

Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning
por: Hemelings, Ruben, et al.
Publicado: (2022)

A field-based recommender system for crop disease detection using machine learning
por: Omara, Jonathan, et al.
Publicado: (2023)

Topology Applied to Machine Learning: From Global to Local
por: Adams, Henry, et al.
Publicado: (2021)

Machine learning and deep learning approaches in IoT
por: Javed, Abqa, et al.
Publicado: (2023)

Early breast cancer detection and differentiation tool based on tissue impedance characteristics and machine learning
por: Salem, Soumaya Ben, et al.
Publicado: (2023)

A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images
por: Ali, Abder-Rahman, et al.
Publicado: (2020)

Editorial: Machine Learning for Water Resources
por: Mariethoz, Gregoire, et al.
Publicado: (2021)

Opportunities for human factors in machine learning
por: Baweja, Jessica A., et al.
Publicado: (2023)

Artificial Intelligence–Based Diagnostic Model for Detecting Keratoconus Using Videos of Corneal Force Deformation
por: Tan, Zuoping, et al.
Publicado: (2022)

GDP Forecasting: Machine Learning, Linear or Autoregression?
por: Maccarrone, Giovanni, et al.
Publicado: (2021)

A Survey of Topological Machine Learning Methods
por: Hensel, Felix, et al.
Publicado: (2021)

Integration of AI and Machine Learning in Radiotherapy QA
por: Chan, Maria F., et al.
Publicado: (2020)

Leveraging explanations in interactive machine learning: An overview
por: Teso, Stefano, et al.
Publicado: (2023)

Using machine learning for healthcare treatment planning
por: Dubey, Snigdha, et al.
Publicado: (2023)

Predicting ward transfer mortality with machine learning
por: Lezama, Jose L., et al.
Publicado: (2023)

Corrigendum: Opportunities for human factors in machine learning
por: Baweja, Jessica A., et al.
Publicado: (2023)

A systematic review on machine learning models for online learning and examination systems
por: Kaddoura, Sanaa, et al.
Publicado: (2022)

Detecting racism and xenophobia using deep learning models on Twitter data: CNN, LSTM and BERT
por: Benítez-Andrades, José Alberto, et al.
Publicado: (2022)

Identification of Scams in Initial Coin Offerings With Machine Learning
por: Karimov, Bedil, et al.
Publicado: (2021)

Impact of Box-Cox Transformation on Machine-Learning Algorithms
por: Blum, Luca, et al.
Publicado: (2022)

Applied Machine Learning for Stochastic Local Volatility Calibration
por: Hakala, Jürgen
Publicado: (2019)

Roof type classification with innovative machine learning approaches
por: Ölçer, Naim, et al.
Publicado: (2023)

Supervised machine learning models for depression sentiment analysis
por: Obagbuwa, Ibidun Christiana, et al.
Publicado: (2023)

Optical character recognition system for Baybayin scripts using support vector machine
por: Pino, Rodney, et al.
Publicado: (2021)

DPWSS: differentially private working set selection for training support vector machines
por: Sun, Zhenlong, et al.
Publicado: (2021)

Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning
por: de Lacy, Nina, et al.
Publicado: (2022)

Artifact Correction in Retinal Nerve Fiber Layer Thickness Maps Using Deep Learning and Its Clinical Utility in Glaucoma
por: Shi, Min, et al.
Publicado: (2023)

Predictions of bitcoin prices through machine learning based frameworks
por: Cocco, Luisanna, et al.
Publicado: (2021)

Generalizable Machine Learning in Neuroscience Using Graph Neural Networks
por: Wang, Paul Y., et al.
Publicado: (2021)

Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces
por: Hernández-Orozco, Santiago, et al.
Publicado: (2021)

Survey on graph embeddings and their applications to machine learning problems on graphs
por: Makarov, Ilya, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_8rnem15ce1d3n526m99ktbi28v