Matsuura Masato

Hashimoto Yohei, Kiwaki Taichi, Sugiura Hiroki, Asano Shotaro, Murata Hiroshi, Fujino Yuri et al.

Deep learning predicting 10-2 visual fields from OCT improved significantly when corrected with 24-2/30-2 visual fields, potentially reducing the need for additional 10-2 tests.

Asaoka Ryo, Xu Linchuan, Murata Hiroshi, Kiwaki Taichi, Matsuura Masato, Fujino Yuri et al.

This study developed an AI model using OCT to predict both current visual field (10°) and future progression (24-2) in glaucoma, showing superior accuracy for earlier detection and monitoring.

Nakahara Kenichi, Asaoka Ryo, Tanito Masaki, Shibata Naoto, Mitsuhashi Keita, Fujino Yuri et al.

This study validated deep learning for glaucoma diagnosis from smartphone fundus photos, showing high accuracy, especially for advanced cases, suggesting potential for accessible screening.

Aoki Shuichiro, Miki Atsuya, Omoto Takashi, Fujino Yuri, Matsuura Masato, Murata Hiroshi et al.

This study found corneal hysteresis (CH), not biomechanical glaucoma factor (BGF), was associated with visual field progression in treated POAG, indicating CH has more clinical utility for monitoring glaucoma.

Omoto Takashi, Murata Hiroshi, Fujino Yuri, Matsuura Masato, Yamashita Takehiro, Miki Atsuya et al.

Sectorwise regression predicted glaucoma visual field progression more accurately than pointwise methods, especially with fewer initial tests, offering a potentially improved monitoring tool.

Murata Hiroshi, Asaoka Ryo, Fujino Yuri, Matsuura Masato, Hirasawa Kazunori, Shimada Satoshi et al.

This study found a new VBLR-VF algorithm measures glaucoma visual fields as accurately as SITA, but significantly faster, potentially improving patient experience and clinic efficiency.

Fujino Yuri, Murata Hiroshi, Matsuura Masato, Nakakura Shunsuke, Shoji Nobuyuki, Nakao Yoshitaka et al.

This study found higher corneal hysteresis (CH) predicts slower glaucoma progression after trabeculectomy, especially centrally and inferiorly, suggesting CH is a valuable prognostic indicator.

Omoto Takashi, Murata Hiroshi, Fujino Yuri, Matsuura Masato, Fujishiro Takashi, Hirasawa Kazunori et al.

This study found that narrower retinal artery angles, indicating more ocular elongation, correlated with significantly thinner macular ganglion cell layers, highlighting a potential structural impact of myopia.

Hashimoto Yohei, Asaoka Ryo, Kiwaki Taichi, Sugiura Hiroki, Asano Shotaro, Murata Hiroshi et al.

A deep learning model accurately predicted central 10° visual fields from SD-OCT scans in glaucoma, offering a potential objective tool for assessing functional loss.

Fujishiro Takashi, Matsuura Masato, Fujino Yuri, Murata Hiroshi, Tokumo Kana, Nakakura Shunsuke et al.

This study found Corvis ST corneal parameters correlate weakly to moderately with ORA's corneal hysteresis. This suggests Corvis ST provides complementary, not redundant, information for glaucoma assessment.

Xu Linchuan, Asaoka Ryo, Kiwaki Taichi, Murata Hiroshi, Fujino Yuri, Matsuura Masato et al.

This study predicted central 10-degree visual fields from OCT using deep learning. CNN-TR models showed superior accuracy, offering a potential tool for glaucoma monitoring and diagnosis by correlating structural and functional damage.

Hirasawa Kazunori, Matsuura Masato, Fujino Yuri, Yanagisawa Mieko, Kanamoto Takashi, Inoue Kenji et al.

This study compared RGC displacement models, finding both Drasdo's and Sjöstrand's improved foveal structure-function relationships, with Drasdo's showing greater improvement. This refines glaucoma assessment near the fovea.

Asaoka Ryo, Murata Hiroshi, Matsuura Masato, Fujino Yuri, Miki Atsuya, Tanito Masaki et al.

Studying glaucoma progression, data augmentation significantly improved visual field prediction, especially with short series. This enhances early detection and monitoring of glaucoma progression.

Aoki Shuichiro, Kiuchi Yoshiaki, Tokumo Kana, Fujino Yuri, Matsuura Masato, Murata Hiroshi et al.

This study found that corneal biomechanical properties, measured by Corvis ST, are associated with superior-inferior optic nerve head asymmetry in glaucoma, suggesting a link between corneal stiffness and ONH damage patterns.

Aoki Shuichiro, Murata Hiroshi, Nakakura Shunsuke, Nakao Yoshitaka, Matsuura Masato, Hirasawa Kazunori et al.

Matsuura Masato, Murata Hiroshi, Fujino Yuri, Yanagisawa Mieko, Nakao Yoshitaka, Tokumo Kana et al.

Corvis ST's bIOP measurement was independent of central corneal thickness but still influenced by corneal hysteresis. This suggests bIOP offers a more refined, but not fully independent, IOP assessment.

Asano Shotaro, Murata Hiroshi, Matsuura Masato, Fujino Yuri, Miki Atsuya, Tanito Masaki et al.

This study validated binomial PLR for detecting glaucoma visual field progression. It found binomial PLR detects progression significantly earlier than other methods, improving clinical management and trial efficacy.

Yousefi Siamak, Sakai Hiroshi, Murata Hiroshi, Fujino Yuri, Matsuura Masato, Garway-Heath David et al.

This study found POAG eyes lose superior visual field faster than inferior, unlike PACG eyes, suggesting different progression patterns for these glaucoma types.

Asano Shotaro, Murata Hiroshi, Matsuura Masato, Fujino Yuri, Asaoka Ryo

This study found binomial pointwise linear regression detected central 10-degree glaucoma progression significantly earlier than other methods. This allows for quicker clinical intervention to preserve vision.

Asaoka Ryo, Murata Hiroshi, Hirasawa Kazunori, Fujino Yuri, Matsuura Masato, Miki Atsuya et al.

Deep learning with macular OCT images accurately diagnosed early glaucoma (93.7% AROC), significantly outperforming other methods. This offers improved early detection for patients.

Hirasawa Kazunori, Nakakura Shunsuke, Nakao Yoshitaka, Fujino Yuri, Matsuura Masato, Murata Hiroshi et al.

Cataract surgery consistently lowered IOP. Corneal biomechanics also changed, showing reduced stiffness (e.g., lower SP A1, higher DA max) up to three months post-op, impacting IOP measurement accuracy.

Fujino Yuri, Murata Hiroshi, Matsuura Masato, Yanagisawa Mieko, Shoji Nobuyuki, Inoue Kenji et al.

This study mapped central visual field defects to optic nerve damage, confirming previous findings but identifying areas with higher prediction error, which is crucial for interpreting glaucoma progression.

Murata Hiroshi, Zangwill Linda M, Fujino Yuri, Matsuura Masato, Miki Atsuya, Hirasawa Kazunori et al.

This study found variational Bayes linear regression (VBLR) more accurately predicted future visual field progression in glaucoma than standard methods, suggesting VBLR could be a useful clinical tool.

Yanagisawa Mieko, Yamashita Takehiro, Matsuura Masato, Fujino Yuri, Murata Hiroshi, Asaoka Ryo

This study found that axial length elongation in glaucoma patients was significantly associated with slower visual field progression, particularly in the inferior hemifield.

Matsuura Masato, Murata Hiroshi, Fujino Yuri, Hirasawa Kazunori, Yanagisawa Mieko, Asaoka Ryo

MP-3 microperimetry in glaucoma patients showed similar test-retest reproducibility to HFA, but a stronger structure-function relationship, suggesting its potential for earlier disease detection.