Menu
×
Main Library
9 a.m. - 8 p.m.
Phone: (843) 805-6930
West Ashley Library
9 a.m. – 7 p.m.
Phone: (843) 766-6635
Folly Beach Library
Closed for renovations
Phone: (843) 588-2001
John L. Dart Library
9 a.m. – 7 p.m.
Phone: (843) 722-7550
St. Paul's/Hollywood Library
9 a.m. - 8 p.m.
Phone: (843) 889-3300
Mt. Pleasant Library
9 a.m. – 8 p.m.
Phone: (843) 849-6161
Dorchester Road Library
9 a.m. - 8 p.m.
Phone: (843) 552-6466
Edgar Allan Poe/Sullivan's Island Library
9 a.m. - 6 p.m.
Phone: (843) 883-3914
John's Island Library
9 a.m. – 8 p.m.
Phone: (843) 559-1945
McClellanville Library
Closed for renovations
Phone: (843) 887-3699
Edisto Library
9 a.m. - 6 p.m.
Phone: (843) 869-2355
Wando Mount Pleasant Library
9 a.m. - 8 p.m.
Phone: (843) 805-6888
Otranto Road Library
9 a.m. - 8 p.m.
Phone: (843) 572-4094
Hurd/St. Andrews Library
9 a.m. - 8 p.m.
Phone: (843) 766-2546
Baxter-Patrick James Island
9 p.m. - 8 p.m.
Phone: (843) 795-6679
Bees Ferry West Ashley Library
9 a.m. - 8 p.m.
Phone: (843) 805-6892
Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
Keith Summey North Charleston Library
9 a.m. – 8 p.m.
Phone: (843) 744-2489
Mobile Library
9 a.m. - 5 p.m.
Phone: (843) 805-6909
Today's Hours
Main Library
9 a.m. - 8 p.m.
Phone: (843) 805-6930
West Ashley Library
9 a.m. – 7 p.m.
Phone: (843) 766-6635
Folly Beach Library
Closed for renovations
Phone: (843) 588-2001
John L. Dart Library
9 a.m. – 7 p.m.
Phone: (843) 722-7550
St. Paul's/Hollywood Library
9 a.m. - 8 p.m.
Phone: (843) 889-3300
Mt. Pleasant Library
9 a.m. – 8 p.m.
Phone: (843) 849-6161
Dorchester Road Library
9 a.m. - 8 p.m.
Phone: (843) 552-6466
Edgar Allan Poe/Sullivan's Island Library
9 a.m. - 6 p.m.
Phone: (843) 883-3914
John's Island Library
9 a.m. – 8 p.m.
Phone: (843) 559-1945
McClellanville Library
Closed for renovations
Phone: (843) 887-3699
Edisto Library
9 a.m. - 6 p.m.
Phone: (843) 869-2355
Wando Mount Pleasant Library
9 a.m. - 8 p.m.
Phone: (843) 805-6888
Otranto Road Library
9 a.m. - 8 p.m.
Phone: (843) 572-4094
Hurd/St. Andrews Library
9 a.m. - 8 p.m.
Phone: (843) 766-2546
Baxter-Patrick James Island
9 p.m. - 8 p.m.
Phone: (843) 795-6679
Bees Ferry West Ashley Library
9 a.m. - 8 p.m.
Phone: (843) 805-6892
Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
Keith Summey North Charleston Library
9 a.m. – 8 p.m.
Phone: (843) 744-2489
Mobile Library
9 a.m. - 5 p.m.
Phone: (843) 805-6909
Patron Login
menu
Item request has been placed!
×
Item request cannot be made.
×
Processing Request
Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques.
Item request has been placed!
×
Item request cannot be made.
×
Processing Request
- Author(s): Pérez del Palomar, Amaya; Cegoñino, José; Montolío, Alberto; Orduna, Elvira; Vilades, Elisa; Sebastián, Berta; Pablo, Luis E.; Garcia-Martin, Elena
- Source:
PLoS ONE; 5/6/2019, Vol. 14 Issue 5, p1-18, 18p- Subject Terms:
- Source:
- Additional Information
- Abstract: Objective: To compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different machine learning techniques. To analyze the capability of machine learning techniques to improve the detection of retinal nerve fiber layer (RNFL) and the complex Ganglion Cell Layer–Inner plexiform layer (GCL+) damage in patients with multiple sclerosis and to use the SS-OCT as a biomarker to early predict this disease. Methods: Patients with relapsing-remitting MS (n = 80) and age-matched healthy controls (n = 180) were enrolled. Different protocols from the DRI SS-OCT Triton system were used to obtain the RNFL and GCL+ thicknesses in both eyes. Macular and peripapilar areas were analyzed to detect the zones with higher thickness decrease. The performance of different machine learning techniques (decision trees, multilayer perceptron and support vector machine) for identifying RNFL and GCL+ thickness loss in patients with MS were evaluated. Receiver-operating characteristic (ROC) curves were used to display the ability of the different tests to discriminate between MS and healthy eyes in our population. Results: Machine learning techniques provided an excellent tool to predict MS disease using SS-OCT data. In particular, the decision trees obtained the best prediction (97.24%) using RNFL data in macular area and the area under the ROC curve was 0.995, while the wide protocol which covers an extended area between macula and papilla gave an accuracy of 95.3% with a ROC of 0.998. Moreover, it was obtained that the most significant area of the RNFL to predict MS is the macula just surrounding the fovea. On the other hand, in our study, GCL+ did not contribute to predict MS and the different machine learning techniques performed worse in this layer than in RNFL. Conclusions: Measurements of RNFL thickness obtained with SS-OCT have an excellent ability to differentiate between healthy controls and patients with MS. Thus, the use of machine learning techniques based on these measures can be a reliable tool to help in MS diagnosis. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
Contact CCPL
Copyright 2022 Charleston County Public Library Powered By EBSCO Stacks 3.3.0 [350.3] | Staff Login
No Comments.