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Joo CW, Choi YJ, Kim HU, Park SP, Na KI. Morphological differences of the neuroretinal rim between temporally tilted and non-tilted optic discs in healthy eyes. Sci Rep 2024; 14:6070. [PMID: 38480784 PMCID: PMC10937920 DOI: 10.1038/s41598-024-54116-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 02/08/2024] [Indexed: 03/17/2024] Open
Abstract
This study aimed to compare morphological differences of the neuroretinal rim between the temporally tilted and non-tilted optic discs in healthy eyes. We prospectively enrolled participants aged 20-40 years with temporally tilted or non-tilted optic discs. The optic nerve head parameters were analyzed using spectral domain-optical coherence tomography. The angle between the Bruch's membrane opening (BMO) plane and BMO-minimum rim width (BMO-MRW) was termed "BMO-MRW angle". Peripapillary retinal nerve fiber layer thickness (pRNFLT) and BMO-based parameters were compared between the temporally tilted and non-tilted disc groups. As a result, 55 temporally tilted disc eyes and 38 non-tilted disc eyes were analyzed. Global pRNFLT, global BMO-MRW, and total BMO-minimum rim area (BMO-MRA) were similar between the two groups (p = 0.138, 0.161, and p = 0.410, respectively). In the sectoral analysis, temporally tilted disc group exhibited thicker BMO-MRW in the temporal sector (p = 0.032) and thinner in the nasal superior and nasal sectors (p = 0.025 and p = 0.002, respectively). Temporally tilted disc group showed larger BMO-MRA in the temporal, temporal superior, and temporal inferior sectors (p < 0.001, p < 0.001, and p < 0.016, respectively), alongside a higher BMO-MRW angle in the temporal sector and lower in the nasal superior and nasal sectors. In conclusion, the neuroretinal rim, represented by BMO-MRW and BMO-MRA, showed morphological differences between temporally tilted and non-tilted optic discs in healthy eyes. BMO-MRW and BMO-MRA showed temporalization in the same manner as pRNFLT in the temporally tilted disc eyes. The BMO-MRW angle showed that in temporally tilted disc eyes, optic nerve fibers met the BMO plane steeply in the nasal sector and gently in the temporal sector than in non-tilted disc eyes, suggesting potential stress region of optic nerve fibers in temporally tilted disc eyes.
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Affiliation(s)
- Chan Woong Joo
- Department of Ophthalmology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, #150 Seongan-ro, Gangdong-gu, Seoul, 05355, South Korea
| | - Youn Joo Choi
- Department of Ophthalmology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, #150 Seongan-ro, Gangdong-gu, Seoul, 05355, South Korea
| | - Han Ul Kim
- Department of Ophthalmology, Armed Forces Seoul District Hospital, Seoul, South Korea
| | - Sung Pyo Park
- Department of Ophthalmology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, #150 Seongan-ro, Gangdong-gu, Seoul, 05355, South Korea
| | - Kyeong Ik Na
- Department of Ophthalmology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, #150 Seongan-ro, Gangdong-gu, Seoul, 05355, South Korea.
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Kisonaite K, Yu Z, Raeme F, Bendazzoli S, Wang C, Söderberg PG. Automatic estimation of the cross-sectional area of the waist of the nerve fibre layer at the optic nerve head. Acta Ophthalmol 2024; 102:91-98. [PMID: 37208926 DOI: 10.1111/aos.15698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 04/12/2023] [Accepted: 04/21/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE Glaucoma leads to pathological loss of axons in the retinal nerve fibre layer at the optic nerve head (ONH). This study aimed to develop a strategy for the estimation of the cross-sectional area of the axons in the ONH. Furthermore, improving the estimation of the thickness of the nerve fibre layer, as compared to a method previously published by us. METHODS In the 3D-OCT image of the ONH, the central limit of the pigment epithelium and the inner limit of the retina, respectively, were identified with deep learning algorithms. The minimal distance was estimated at equidistant angles around the circumference of the ONH. The cross-sectional area was estimated by the computational algorithm. The computational algorithm was applied on 16 non-glaucomatous subjects. RESULTS The mean cross-sectional area of the waist of the nerve fibre layer in the ONH was 1.97 ± 0.19 mm2 . The mean difference in minimal thickness of the waist of the nerve fibre layer between our previous and the current strategies was estimated as CIμ (0.95) 0 ± 1 μm (d.f. = 15). CONCLUSIONS The developed algorithm demonstrated an undulating cross-sectional area of the nerve fibre layer at the ONH. Compared to studies using radial scans, our algorithm resulted in slightly higher values for cross-sectional area, taking the undulations of the nerve fibre layer at the ONH into account. The new algorithm for estimation of the thickness of the waist of the nerve fibre layer in the ONH yielded estimates of the same order as our previous algorithm.
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Affiliation(s)
| | - Zhaohua Yu
- Ophthalmology, Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Faisal Raeme
- Ophthalmology, Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Simone Bendazzoli
- Biomedical Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Chunliang Wang
- Biomedical Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Per G Söderberg
- Ophthalmology, Surgical Sciences, Uppsala University, Uppsala, Sweden
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Gonzalez-Hernandez M, Betancor-Caro N, Mesa-Lugo F, Rodriguez-Talavera I, Pareja-Rios A, Guedes-Guedes I, Estevez-Jorge B, Trujillo-Blanco M, Cordova-Villegas R, Espinoza-Gonzalez J, Siguero-Martin L, Goya-Gonzalez C, Rodriguez-Dominguez M, Gonzalez-Hernandez D, Gonzalez de la Rosa M. Identification of Glaucoma in Diabetics Using the Laguna-ONhE Colourimetric Method and OCT Spectralis. J Clin Med 2023; 12:5876. [PMID: 37762816 PMCID: PMC10531930 DOI: 10.3390/jcm12185876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/01/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Previous retrospective results are evaluated prospectively and blinded. METHODS A total of 221 eyes previously classified as normal (G1), 279 as moderate risk of glaucoma (G2) and 217 as high risk (G3) according to the Globin Discriminant Function (GDF) Laguna-ONhE index were examined with OCT Spectralis- Results: In G1, the Bruch's Membrane Opening Minimum Rim Width (BMO-MRW) was 332 ± 55 microns; in G2, it was 252 ± 47 (p < 0.0001); and in G3, 231 ± 44 (p < 0.0001). In G1, the 1% and 5% percentiles were 233 and 248, respectively; in G2, they were lower in 28.80% and 42.29% of cases, respectively; and in G3, in 50.23% and 63.59% of cases, respectively. Most of the cases were normal-tension glaucomas. Laguna-ONhE indices showed a curvilinear correlation with BMO-MRW results. The Retinal Nerve Fibre Layer (RNFL) showed a poor relationship with BMO. Assuming G1 to be truly normal, BMO-MRW would have a Receiver operating characteristic (ROC) curve area of 0.901 for G2 and G3 and 0.651 for RNFL. A significant reduction in pixels corresponding to vessels was found in G2 and G3 vs. G1 (p < 0.0001). CONCLUSIONS In some cases, these defects appear to be mainly glaucomatous, and in others, they are associated with diabetic microangiopathy. In normal tension glaucoma, RNFL defects may be less severe than those inside the nerve.
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Affiliation(s)
- Marta Gonzalez-Hernandez
- Instrumentacion y Oftalmologia, INSOFT S.L., 38004 Santa Cruz de Tenerife, Spain; (D.G.-H.); (M.G.d.l.R.)
| | - Nisamar Betancor-Caro
- Hospital Universitario de Canarias, 38320 La Laguna, Spain; (N.B.-C.); (F.M.-L.); (I.R.-T.); (A.P.-R.)
| | - Fatima Mesa-Lugo
- Hospital Universitario de Canarias, 38320 La Laguna, Spain; (N.B.-C.); (F.M.-L.); (I.R.-T.); (A.P.-R.)
| | - Ivan Rodriguez-Talavera
- Hospital Universitario de Canarias, 38320 La Laguna, Spain; (N.B.-C.); (F.M.-L.); (I.R.-T.); (A.P.-R.)
| | - Alicia Pareja-Rios
- Hospital Universitario de Canarias, 38320 La Laguna, Spain; (N.B.-C.); (F.M.-L.); (I.R.-T.); (A.P.-R.)
| | - Isabel Guedes-Guedes
- Hospital Universitario Insular de Gran Canaria, 35016 Las Palmas de Gran Canarias, Spain; (I.G.-G.); (B.E.-J.); (M.T.-B.); (J.E.-G.); (L.S.-M.); (C.G.-G.); (M.R.-D.)
- Facultad de Medicina, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran, Spain
| | - Beatriz Estevez-Jorge
- Hospital Universitario Insular de Gran Canaria, 35016 Las Palmas de Gran Canarias, Spain; (I.G.-G.); (B.E.-J.); (M.T.-B.); (J.E.-G.); (L.S.-M.); (C.G.-G.); (M.R.-D.)
| | - Maricela Trujillo-Blanco
- Hospital Universitario Insular de Gran Canaria, 35016 Las Palmas de Gran Canarias, Spain; (I.G.-G.); (B.E.-J.); (M.T.-B.); (J.E.-G.); (L.S.-M.); (C.G.-G.); (M.R.-D.)
| | - Roberto Cordova-Villegas
- Hospital Universitario Insular de Gran Canaria, 35016 Las Palmas de Gran Canarias, Spain; (I.G.-G.); (B.E.-J.); (M.T.-B.); (J.E.-G.); (L.S.-M.); (C.G.-G.); (M.R.-D.)
| | - Juan Espinoza-Gonzalez
- Hospital Universitario Insular de Gran Canaria, 35016 Las Palmas de Gran Canarias, Spain; (I.G.-G.); (B.E.-J.); (M.T.-B.); (J.E.-G.); (L.S.-M.); (C.G.-G.); (M.R.-D.)
| | - Leticia Siguero-Martin
- Hospital Universitario Insular de Gran Canaria, 35016 Las Palmas de Gran Canarias, Spain; (I.G.-G.); (B.E.-J.); (M.T.-B.); (J.E.-G.); (L.S.-M.); (C.G.-G.); (M.R.-D.)
| | - Carolina Goya-Gonzalez
- Hospital Universitario Insular de Gran Canaria, 35016 Las Palmas de Gran Canarias, Spain; (I.G.-G.); (B.E.-J.); (M.T.-B.); (J.E.-G.); (L.S.-M.); (C.G.-G.); (M.R.-D.)
| | - Maria Rodriguez-Dominguez
- Hospital Universitario Insular de Gran Canaria, 35016 Las Palmas de Gran Canarias, Spain; (I.G.-G.); (B.E.-J.); (M.T.-B.); (J.E.-G.); (L.S.-M.); (C.G.-G.); (M.R.-D.)
| | - Daniel Gonzalez-Hernandez
- Instrumentacion y Oftalmologia, INSOFT S.L., 38004 Santa Cruz de Tenerife, Spain; (D.G.-H.); (M.G.d.l.R.)
| | - Manuel Gonzalez de la Rosa
- Instrumentacion y Oftalmologia, INSOFT S.L., 38004 Santa Cruz de Tenerife, Spain; (D.G.-H.); (M.G.d.l.R.)
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Zangalli CS, Jammal AA, Reis ASC, Ayub G, Diniz-Filho A, Paranhos A, Paula JS, Costa VP. Minimum Rim Width and Peripapillary Retinal Nerve Fiber Layer Thickness for Diagnosing Early to Moderate Glaucoma. J Glaucoma 2023; 32:526-532. [PMID: 36730041 DOI: 10.1097/ijg.0000000000002156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 11/16/2022] [Indexed: 02/03/2023]
Abstract
PRCIS In a cross-sectional study from a Brazilian multiracial population, minimum rim width (MRW) and peripapillary retinal nerve fiber layer thickness measurements from OCT showed comparable diagnostic performance in discriminating early to moderate glaucoma from healthy eyes. PURPOSE The purpose of this study is to compare the ability of MRW and peripapillary retinal nerve fiber layer thickness (RNFLT) measurements in discriminating early to moderate glaucoma from healthy eyes in a Brazilian population. METHODS A total of 155 healthy controls and 118 patients with mild to moderate glaucoma (mean deviation >-12 dB) underwent MRW and RNFLT measurements with optical coherence tomography. Only 1 eye per patient was included in the analysis. A receiver operating characteristic (ROC) regression model was used to evaluate the diagnostic accuracy of MRW and RNFLT, whereas adjusting for age and Bruch membrane opening area. Sensitivities at fixed specificities of 95% were calculated for each parameter. RESULTS Global RNFLT and MRW showed comparable area under the ROC curves [0.93 (0.91-0.96) and 0.93 (0.89-0.96), respectively; P =0.973]. Both parameters had similar sensitivities (75% vs. 74%, respectively; P =0.852) at a fixed specificity of 95%. The best sector for diagnosing glaucoma for both parameters was the temporal inferior sector, which showed an area under the ROC curve of 0.93 (0.87-0.96) for RNFLT and 0.91 (0.86-0.95) for MRW ( P =0.320). The temporal inferior sector showed similar sensitivities for RNFLT and MRW measurements (83% vs. 77%, respectively) at a fixed specificity of 95% (P =0.230). CONCLUSIONS MRW and RNFLT measurements showed comparable diagnostic performance in discriminating early to moderate glaucoma from healthy eyes in a Brazilian multiracial population.
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Diaz-Torres S, He W, Thorp J, Seddighi S, Mullany S, Hammond CJ, Hysi PG, Pasquale LR, Khawaja AP, Hewitt AW, Craig JE, Mackey DA, Wiggs JL, van Duijn C, Lupton MK, Ong JS, MacGregor S, Gharahkhani P. Disentangling the genetic overlap and causal relationships between primary open-angle glaucoma, brain morphology and four major neurodegenerative disorders. EBioMedicine 2023; 92:104615. [PMID: 37201334 PMCID: PMC10206164 DOI: 10.1016/j.ebiom.2023.104615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/21/2023] [Accepted: 04/28/2023] [Indexed: 05/20/2023] Open
Abstract
BACKGROUND Primary open-angle glaucoma (POAG) is an optic neuropathy characterized by progressive degeneration of the optic nerve that leads to irreversible visual impairment. Multiple epidemiological studies suggest an association between POAG and major neurodegenerative disorders (Alzheimer's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and Parkinson's disease). However, the nature of the overlap between neurodegenerative disorders, brain morphology and glaucoma remains inconclusive. METHOD In this study, we performed a comprehensive assessment of the genetic and causal relationship between POAG and neurodegenerative disorders, leveraging genome-wide association data from studies of magnetic resonance imaging of the brain, POAG, and four major neurodegenerative disorders. FINDINGS This study found a genetic overlap and causal relationship between POAG and its related phenotypes (i.e., intraocular pressure and optic nerve morphology traits) and brain morphology in 19 regions. We also identified 11 loci with a significant local genetic correlation and a high probability of sharing the same causal variant between neurodegenerative disorders and POAG or its related phenotypes. Of interest, a region on chromosome 17 corresponding to MAPT, a well-known risk locus for Alzheimer's and Parkinson's disease, was shared between POAG, optic nerve degeneration traits, and Alzheimer's and Parkinson's diseases. Despite these local genetic overlaps, we did not identify strong evidence of a causal association between these neurodegenerative disorders and glaucoma. INTERPRETATION Our findings indicate a distinctive and likely independent neurodegenerative process for POAG involving several brain regions although several POAG or optic nerve degeneration risk loci are shared with neurodegenerative disorders, consistent with a pleiotropic effect rather than a causal relationship between these traits. FUNDING PG was supported by an NHMRC Investigator Grant (#1173390), SM by an NHMRC Senior Research Fellowship and an NHMRC Program Grant (APP1150144), DM by an NHMRC Fellowship, LP is funded by the NEIEY015473 and EY032559 grants, SS is supported by an NIH-Oxford Cambridge Fellowship and NIH T32 grant (GM136577), APK is supported by a UK Research and Innovation Future Leaders Fellowship, an Alcon Research Institute Young Investigator Award and a Lister Institute for Preventive Medicine Award.
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Affiliation(s)
- Santiago Diaz-Torres
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; Faculty of Medicine, University of Queensland (UQ), Brisbane, QLD, Australia.
| | - Weixiong He
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jackson Thorp
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sahba Seddighi
- Nuffield Department of Population Health, Oxford University, Oxford, UK; Medical Scientist Training Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sean Mullany
- Department of Ophthalmology, Flinders University, Flinders Medical Centre, Bedford Park, Australia
| | - Christopher J Hammond
- Departments of Ophthalmology & Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Pirro G Hysi
- Departments of Ophthalmology & Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Anthony P Khawaja
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Alex W Hewitt
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | - Jamie E Craig
- Department of Ophthalmology, Flinders University, Flinders Medical Centre, Bedford Park, Australia
| | - David A Mackey
- Centre for Ophthalmology and Visual Science, University of Western Australia, Lions Eye Institute, Australia
| | - Janey L Wiggs
- Department of Ophthalmology, Harvard Medical School, Boston, 02114, MA, USA
| | | | - Michelle K Lupton
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jue-Sheng Ong
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Stuart MacGregor
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; Faculty of Medicine, University of Queensland (UQ), Brisbane, QLD, Australia
| | - Puya Gharahkhani
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; Faculty of Medicine, University of Queensland (UQ), Brisbane, QLD, Australia; School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, Australia.
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Ozcelik-Kose A, Yıldız MB, Imamoglu S. Diagnostic Performance of Optical Coherence Tomography for Pseudoexfoliation Glaucoma. J Glaucoma 2022; 31:651-658. [PMID: 35474292 DOI: 10.1097/ijg.0000000000002042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 04/15/2022] [Indexed: 11/25/2022]
Abstract
PRECIS LC thickness and LCCI had comparable diagnostic performances with RNFL thickness in distinguishing eyes with PXG from those with PXS. BMO-MRW showed the lowest diagnostic performance among all geometric parameters derived from OCT scans we evaluated. OBJECTIVE To compare the diagnostic performance of different geometric parameters derived from optical coherence tomography (OCT) scans (retinal nerve fiber layer [RNFL] thickness, lamina cribrosa [LC] thickness, LC curvature index [LCCI] and Bruch's membrane opening-minimum rim width [BMO-MRW]) for distinguishing eyes with pseudoexfoliation glaucoma (PXG) from pseudoexfoliation syndrome (PXS) and healthy eyes. METHODS Fifty-five eyes of 55 patients with PXG, 55 eyes of 55 patients with PXS, and 50 healthy subjects were enrolled in this cross-sectional study. The areas under the receiver operating characteristic curves (AUCs) of RNFL thickness, LC thickness, LCCI and BMO-MRW were calculated and compared. RESULTS In discriminating between eyes with PXG from those with PXS, LC thickness (0.930 [95% CI: 0.883-0.978]) and global RNFL thickness (0.974 [95% CI: 0.947-0.992]) presented comparable AUCs (P=0.244). In distinguishing subjects wiht PXG from healthy controls, both LC thickness (0.972 [95% CI: 0.948-0.997]) and LCCI (0.983 [95% CI: 0.968-0.998]) had comparable AUCs with global RNFL thickness (0.988 [95% CI: 0.974-1.000]) (P=0.094 andP=0.239, respectively). Global BMO-MRW had lower AUCs than RNFL thickness (0.839 [95% CI: 0.759-0.920] and 0.897 [95% CI: 0.836-0.958], respectively) in distinguishing PXG from both PXS and healthy controls (P=0.001 andP=0.002, respectively). BMO-MRW also had significantly lower AUCs than both LC thickness and LCCI in distinguishing PXG from healthy controls (P=0.034 andP=0.001, respectively). CONCLUSION LC thickness and LCCI had better diagnostic performance than BMO-MRW in distinguishing PXG from PXS and healthy controls, which were comparable to RNFL thickness.
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Affiliation(s)
- Alev Ozcelik-Kose
- University of Health Sciences Haydarpasa Education and Research Hospital, Department of Ophthalmology, Istanbul, Turkey
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Camara J, Neto A, Pires IM, Villasana MV, Zdravevski E, Cunha A. Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification. J Imaging 2022; 8:jimaging8020019. [PMID: 35200722 PMCID: PMC8878383 DOI: 10.3390/jimaging8020019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 12/20/2022] Open
Abstract
Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images. The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease’s progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements related to glaucoma, which may promote patient empowerment and help medical doctors better monitor patients.
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Affiliation(s)
- José Camara
- R. Escola Politécnica, Universidade Aberta, 1250-100 Lisboa, Portugal;
- Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, Portugal;
| | - Alexandre Neto
- Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, Portugal;
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal;
| | - Ivan Miguel Pires
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal;
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
| | - María Vanessa Villasana
- Centro Hospitalar Universitário Cova da Beira, 6200-251 Covilhã, Portugal;
- UICISA:E Research Centre, School of Health, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia;
| | - António Cunha
- Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, Portugal;
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal;
- Correspondence: ; Tel.: +351-931-636-373
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Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT. Diagnostics (Basel) 2021; 11:diagnostics11091718. [PMID: 34574059 PMCID: PMC8471622 DOI: 10.3390/diagnostics11091718] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/17/2021] [Accepted: 09/18/2021] [Indexed: 11/17/2022] Open
Abstract
Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating reliable diagnoses in glaucoma practice. Here we aim to compare different MLCs based on Spectralis OCT parameters, including circumpapillary retinal nerve fiber layer (cRNFL) thickness, Bruch’s membrane opening-minimum rim width (BMO-MRW), Early Treatment Diabetes Retinopathy Study (ETDRS) macular thickness, and posterior pole asymmetry analysis (PPAA), in discriminating normal from glaucomatous eyes. Five MLCs were proposed, namely conditional inference trees (CIT), logistic model tree (LMT), C5.0 decision tree, random forest (RF), and extreme gradient boosting (XGBoost). Logistic regression (LGR) was used as a benchmark for comparison. RF was shown to be the best model. Ganglion cell layer measurements were the most important predictors in early glaucoma detection and cRNFL measurements were more important as the glaucoma severity increased. The global, temporal, inferior, superotemporal, and inferotemporal sites were relatively influential locations among all parameters. Clinicians should cautiously integrate the Spectralis OCT results into the entire clinical picture when diagnosing glaucoma.
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