PURPOSE: To compare the performance of standard automated perimetry (SAP), frequency-doubling technology (FDT) perimetry, and short-wavelength automated perimetry (SWAP) in detecting glaucoma. METHODS: One hundred thirty-two eyes of 95 glaucoma patients and 37 normal subjects had retinal nerve fiber layer (RNFL) imaging and visual field testing by SAP, Matrix FDT perimetry, and Swedish interactive thresholding algorithm (SITA) SWAP at the same visit (all perimeters by Carl Zeiss Meditec, Inc., Dublin, CA). Visual field defects were confirmed with two or more consecutive examinations by the same types of perimetry. Glaucoma was defined with the reference to the RNFL thickness deviation map score (≥ 4, glaucomatous; ≤ 2, normal). The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of MD (mean deviation) and PSD (pattern standard deviation) of the perimetries were compared. RESULTS: Taking all glaucoma patients into consideration, the sensitivity was highest for Matrix FDT perimetry (69%), followed by SAP (68%), and then SITA SWAP (59%). When the analysis included only patients with early glaucoma, the sensitivity decreased to 52%, 46%, and 34%, respectively, with a significant difference detected between Matrix FDT perimetry and SITA SWAP (P = 0.034). The specificity was ≥ 97% for all perimetries. The AUCs of MD and PSD followed a similar order, with Matrix FDT perimetry having the greatest AUC (0.89-0.94), followed by SAP (0.87-0.94), and then SITA SWAP (0.69-0.90). There were significant differences in sensitivities at 90% specificity between Matrix FDT perimetry and SITA SWAP (P ≤ 0.005 for MD; P ≤ 0.039 for PSD). CONCLUSIONS: The performance for glaucoma detection was comparable between FDT perimetry and SAP. FDT perimetry had a higher sensitivity for detecting glaucoma than did SWAP at a comparable level of specificity.
PURPOSE: To compare the performance of standard automated perimetry (SAP), frequency-doubling technology (FDT) perimetry, and short-wavelength automated perimetry (SWAP) in detecting glaucoma. METHODS: One hundred thirty-two eyes of 95 glaucomapatients and 37 normal subjects had retinal nerve fiber layer (RNFL) imaging and visual field testing by SAP, Matrix FDT perimetry, and Swedish interactive thresholding algorithm (SITA) SWAP at the same visit (all perimeters by Carl Zeiss Meditec, Inc., Dublin, CA). Visual field defects were confirmed with two or more consecutive examinations by the same types of perimetry. Glaucoma was defined with the reference to the RNFL thickness deviation map score (≥ 4, glaucomatous; ≤ 2, normal). The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of MD (mean deviation) and PSD (pattern standard deviation) of the perimetries were compared. RESULTS: Taking all glaucomapatients into consideration, the sensitivity was highest for Matrix FDT perimetry (69%), followed by SAP (68%), and then SITA SWAP (59%). When the analysis included only patients with early glaucoma, the sensitivity decreased to 52%, 46%, and 34%, respectively, with a significant difference detected between Matrix FDT perimetry and SITA SWAP (P = 0.034). The specificity was ≥ 97% for all perimetries. The AUCs of MD and PSD followed a similar order, with Matrix FDT perimetry having the greatest AUC (0.89-0.94), followed by SAP (0.87-0.94), and then SITA SWAP (0.69-0.90). There were significant differences in sensitivities at 90% specificity between Matrix FDT perimetry and SITA SWAP (P ≤ 0.005 for MD; P ≤ 0.039 for PSD). CONCLUSIONS: The performance for glaucoma detection was comparable between FDT perimetry and SAP. FDT perimetry had a higher sensitivity for detecting glaucoma than did SWAP at a comparable level of specificity.
Authors: Daniel Meira-Freitas; Andrew J Tatham; Renato Lisboa; Tung-Mei Kuang; Linda M Zangwill; Robert N Weinreb; Christopher A Girkin; Jeffrey M Liebmann; Felipe A Medeiros Journal: Ophthalmology Date: 2013-11-26 Impact factor: 12.079
Authors: Kenman Gan; Yao Liu; Brian Stagg; Siddarth Rathi; Louis R Pasquale; Karim Damji Journal: Telemed J E Health Date: 2020-03-25 Impact factor: 3.536
Authors: Mark B Horton; Christopher J Brady; Jerry Cavallerano; Michael Abramoff; Gail Barker; Michael F Chiang; Charlene H Crockett; Seema Garg; Peter Karth; Yao Liu; Clark D Newman; Siddarth Rathi; Veeral Sheth; Paolo Silva; Kristen Stebbins; Ingrid Zimmer-Galler Journal: Telemed J E Health Date: 2020-03-25 Impact factor: 3.536
Authors: Eugene A Lowry; Jing Hou; Lauren Hennein; Robert T Chang; Shan Lin; Jeremy Keenan; Sean K Wang; Sean Ianchulev; Louis R Pasquale; Ying Han Journal: Transl Vis Sci Technol Date: 2016-07-19 Impact factor: 3.283