| Texto completo | |
| Autor(es): |
Shigueoka, Leonardo Seidi
[1]
;
Cabral de Vasconcellos, Jose Paulo
[1]
;
Schimiti, Rui Barroso
[1]
;
Castro Reis, Alexandre Soares
[1]
;
de Oliveira, Gabriel Ozeas
[2]
;
Gomi, Edson Satoshi
[2]
;
Rocha Vianna, Jayme Augusto
[3]
;
dos Reis Lisboa, Renato Dichetti
[4]
;
Medeiros, Felipe Andrade
[4]
;
Costa, Vital Paulino
[1]
Número total de Autores: 10
|
| Afiliação do(s) autor(es): | [1] Univ Estadual Campinas, Dept Ophthalmol, Glaucoma Serv, Campinas, SP - Brazil
[2] Univ Sao Paulo, Polytech Sch, Dept Comp Engn, Sao Paulo, SP - Brazil
[3] Dalhousie Univ, Dept Ophthalmol & Visual Sci, Halifax, NS - Canada
[4] Duke Univ, Ctr Eye, Dept Ophthalmol, Sch Med, Durham, NC 27710 - USA
Número total de Afiliações: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PLoS One; v. 13, n. 12 DEC 5 2018. |
| Citações Web of Science: | 1 |
| Resumo | |
Purpose To test the ability of machine learning classifiers (MLCs) using optical coherence tomography (OCT) and standard automated perimetry (SAP) parameters to discriminate between healthy and glaucomatous individuals, and to compare it to the diagnostic ability of the combined structure-function index (CSFI), general ophthalmologists and glaucoma specialists. Design Cross-sectional prospective study. Methods Fifty eight eyes of 58 patients with early to moderate glaucoma (median value of the mean deviation = -3.44 dB; interquartile range, -6.0 to -2.4 dB) and 66 eyes of 66 healthy individuals underwent OCT and SAP tests. The diagnostic accuracy (area under the ROC curve-AUC) of 10 MLCs was compared to those obtained with the CSFI, 3 general ophthalmologists and 3 glaucoma specialists exposed to the same OCT and SAP data. Results The AUCs obtained with MLCs ranged from 0.805 (Classification Tree) to 0.931 (Radial Basis Function Network, RBF). The sensitivity at 90% specificity ranged from 51.6% (Classification Tree) to 82.8% (Bagging, Multilayer Perceptron and Support Vector Machine Gaussian). The CSFI had a sensitivity of 79.3% at 90% specificity, and the highest AUC (0.948). General ophthalmologists and glaucoma specialists' grading had sensitivities of 66.2% and 83.8% at 90% specificity, and AUCs of 0.879 and 0.921, respectively. RBF (the best MLC), the CSFI, and glaucoma specialists showed significantly higher AUCs than that obtained by general ophthalmologists (P<0.05). However, there were no significant differences between the AUCs obtained by RBF, the CSFI, and glaucoma specialists (P>0.25). Conclusion Our findings suggest that both MLCs and the CSFI can be helpful in clinical practice and effectively improve glaucoma diagnosis in the primary eye care setting, when there is no glaucoma specialist available. (AU) | |
| Processo FAPESP: | 07/51281-9 - Diagnóstico precoce e de progressão do glaucoma baseados em sistemas de aprendizagem de classificadores híbridos |
| Beneficiário: | Vital Paulino Costa |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |