| Accuracy of deep learning, a machine-learning technology, using ultra–wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment H Ohsugi, H Tabuchi, H Enno, N Ishitobi Scientific reports 7 (1), 9425, 2017 | 130 | 2017 |
| Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration S Matsuba, H Tabuchi, H Ohsugi, H Enno, N Ishitobi, H Masumoto, ... International ophthalmology 39, 1269-1275, 2019 | 96 | 2019 |
| Deep neural network‐based method for detecting central retinal vein occlusion using ultrawide‐field fundus ophthalmoscopy D Nagasato, H Tabuchi, H Ohsugi, H Masumoto, H Enno, N Ishitobi, ... Journal of ophthalmology 2018 (1), 1875431, 2018 | 67 | 2018 |
| Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy T Nagasawa, H Tabuchi, H Masumoto, H Enno, M Niki, Z Ohara, ... International ophthalmology 39, 2153-2159, 2019 | 65 | 2019 |
| Deep-learning classifier with an ultrawide-field scanning laser ophthalmoscope detects glaucoma visual field severity H Masumoto, H Tabuchi, S Nakakura, N Ishitobi, M Miki, H Enno Journal of glaucoma 27 (7), 647-652, 2018 | 58 | 2018 |
| Deep-learning classifier with ultrawide-field fundus ophthalmoscopy for detecting branch retinal vein occlusion D Nagasato, H Tabuchi, H Ohsugi, H Masumoto, H Enno, N Ishitobi, ... International Journal of Ophthalmology 12 (1), 94, 2019 | 56 | 2019 |
| Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning D Nagasato, H Tabuchi, H Masumoto, H Enno, N Ishitobi, M Kameoka, ... PloS one 14 (11), e0223965, 2019 | 54 | 2019 |
| Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT T Sonobe, H Tabuchi, H Ohsugi, H Masumoto, N Ishitobi, S Morita, ... International ophthalmology 39, 1871-1877, 2019 | 42 | 2019 |
| Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images H Masumoto, H Tabuchi, S Nakakura, H Ohsugi, H Enno, N Ishitobi, ... PeerJ 7, e6900, 2019 | 36 | 2019 |
| Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes T Nagasawa, H Tabuchi, H Masumoto, H Enno, M Niki, H Ohsugi, ... PeerJ 6, e5696, 2018 | 36 | 2018 |
| Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study S Tsuiki, T Nagaoka, T Fukuda, Y Sakamoto, FR Almeida, H Nakayama, ... Sleep and Breathing, 1-9, 2021 | 30 | 2021 |
| Dibenzo[a,f]perylene Bisimide: Effects of Introducing Two Fused Rings Chaolumen, H Enno, M Murata, A Wakamiya, Y Murata Chemistry–An Asian Journal 9 (11), 3136-3140, 2014 | 9 | 2014 |
| Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawidefield images. PeerJ 7: e6900 H Masumoto, H Tabuchi, S Nakakura, H Ohsugi, H Enno, N Ishitobi, ... | 4 | 2019 |
| 0594 Can a Deep Convolutional Neural Network Extract Diagnostic Information on Obstructive Sleep Apnea from Images? S Tsuiki, T Nagaoka, T Fukuda, Y Sakamoto, FR Almeida, H Nakayama, ... Sleep 43, A227, 2020 | | 2020 |
| Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naıve proliferative diabetic retinopathy TNHTH Masumoto, H Enno, MNZOY Yoshizumi, H Ohsugi, Y Mitamura | | 2019 |
