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Omodaka K, Horie J, Tokairin H, Kato C, Ouchi J, Ninomiya T, Parmanand S, Tsuda S, Nakazawa T. Deep Learning-Based Noise Reduction Improves Optical Coherence Tomography Angiography Imaging of Radial Peripapillary Capillaries in Advanced Glaucoma. Curr Eye Res 2022; 47:1600-1608. [PMID: 36102611 DOI: 10.1080/02713683.2022.2124275] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
PURPOSE We applied deep learning-based noise reduction (NR) to optical coherence tomography-angiography (OCTA) images of the radial peripapillary capillaries (RPCs) in eyes with glaucoma and investigated the usefulness of this method as an objective analysis of glaucoma. METHODS This cross-sectional study included 118 eyes of 94 open-angle glaucoma patients (male/female = 38/56, age: 56.1 ± 10.3 years). We used OCTA (OCT-HS100, Canon) and built-in software (RX software, v. 4.5) to perform NR and calculate RPC vessel area density (VAD) and skeleton vessel length density (VLD). We also examined NR's effect on reproducibility. Finally, we assessed the vascular structure (PRCs)/function relationship at different glaucoma stages with Spearman's correlation. RESULTS Regardless of NR, RPC parameters had excellent coefficients of variation (1.7-4.1%) in glaucoma patients and controls, and mean deviation (MD) was significantly correlated with VAD (NR: r = 0.835, p < 0.001; non-NR: r = 0.871, p < 0.001) and VLD (NR: r = 0.829, p < 0.001; non-NR: r = 0.837, p < 0.001). For mild, moderate, and advanced glaucoma, the correlation coefficients between MD and VLD were 0.366 (p = 0.028) 0.081 (p = 0.689), and 0.427 (p = 0.017) with NR and 0.405 (p = 0.014), 0.184 (p = 0.360), and 0.339 (p = 0.062) without NR, respectively. CONCLUSION Denoised RPC images might have the potential for a closer structural/functional relationship, in which the floor effect of retinal nerve fiber layer thickness affects measurements. Deep learning-based NR promises to improve glaucoma assessment.
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Affiliation(s)
- Kazuko Omodaka
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan.,Department of Ophthalmic Imaging and Information Analytics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | | | - Hikari Tokairin
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Chiho Kato
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Junko Ouchi
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takahiro Ninomiya
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Sharma Parmanand
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Satoru Tsuda
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Toru Nakazawa
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan.,Department of Ophthalmic Imaging and Information Analytics, Tohoku University Graduate School of Medicine, Sendai, Japan
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Omodaka K, Kikawa T, Kabakura S, Himori N, Tsuda S, Ninomiya T, Takahashi N, Pak K, Takeda N, Akiba M, Nakazawa T. Clinical characteristics of glaucoma patients with various risk factors. BMC Ophthalmol 2022; 22:373. [PMID: 36123604 PMCID: PMC9484257 DOI: 10.1186/s12886-022-02587-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 08/29/2022] [Indexed: 11/27/2022] Open
Abstract
Background Glaucoma is multifactorial, but the interrelationship between risk factors and structural changes remains unclear. Here, we adjusted for confounding factors in glaucoma patients with differing risk factors, and compared differences in structure and susceptible areas in the optic disc and macula. Methods In 458 eyes with glaucoma, we determined confounding factors for intraocular pressure (IOP), central corneal thickness (CCT), axial length (AL), LSFG-measured ocular blood flow (OBF), which was assessed with laser speckle flowgraphy-measured mean blur rate in the tissue area (MT) of the optic nerve head, biological antioxidant potential (BAP), and systemic abnormalities in diastolic blood pressure (dBP). To compensate for measurement bias, we also analyzed corrected IOP (cIOP; corrected for CCT) and corrected MT (cMT; corrected for age, weighted retinal ganglion cell count, and AL). Then, we determined the distribution of these parameters in low-, middle-, and high-value subgroups and compared them with the Kruskal–Wallis test. Pairwise comparisons used the Steel–Dwass test. Results The high-cIOP subgroup had significantly worse mean deviation (MD), temporal, superior, and inferior loss of circumpapillary retinal nerve fiber layer thickness (cpRNFLT), and large cupping. The low-CCT subgroup had temporal cpRNFLT loss; the high-CCT subgroup had low cup volume. The high-AL subgroup had macular ganglion cell complex thickness (GCCT) loss; the low-AL subgroup had temporal cpRNFLT loss. The high-systemic-dBP subgroup had worse MD, total, superior, and inferior cpRNFLT loss and macular GCCT loss. The low-BAP subgroup had more male patients, higher dBP, and cpRNFLT loss in the 10 o’clock area. The high-OBF subgroup had higher total, superior and temporal cpRNFLT and macular GCCT. Conclusions Structural changes and local susceptibility to glaucomatous damage show unique variations in patients with different risk factors, which might suggest that specific risk factors induce specific types of pathogenesis and corresponding glaucoma phenotypes. Our study may open new avenues for the development of precision medicine for glaucoma.
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Affiliation(s)
- Kazuko Omodaka
- Department of Ophthalmology, Tohoku University, Graduate School of Medicine, Sendai, Japan.,Department of Ophthalmic Imaging and Information Analytics, Tohoku University, Graduate School of Medicine, Sendai, Japan
| | | | - Sayaka Kabakura
- Department of Ophthalmology, Tohoku University, Graduate School of Medicine, Sendai, Japan
| | - Noriko Himori
- Department of Ophthalmology, Tohoku University, Graduate School of Medicine, Sendai, Japan.,Department of Aging Vision Healthcare, Tohoku University Graduate School of Biomedical Engineering, Sendai, Japan
| | - Satoru Tsuda
- Department of Ophthalmology, Tohoku University, Graduate School of Medicine, Sendai, Japan
| | - Takahiro Ninomiya
- Department of Ophthalmology, Tohoku University, Graduate School of Medicine, Sendai, Japan
| | - Naoki Takahashi
- Department of Ophthalmology, Tohoku University, Graduate School of Medicine, Sendai, Japan
| | - Kyongsun Pak
- Division of Biostatistics, Center for Clinical Research, National Center for Child Health and Development, Tokyo, Japan
| | | | - Masahiro Akiba
- R and D Division, Topcon Corporation, Tokyo, Japan.,Cloud-Based Eye Disease Diagnosis Joint Research Team, Riken, Wako, Japan
| | - Toru Nakazawa
- Department of Ophthalmology, Tohoku University, Graduate School of Medicine, Sendai, Japan. .,Department of Ophthalmic Imaging and Information Analytics, Tohoku University, Graduate School of Medicine, Sendai, Japan.
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