Prediction of postoperative visual acuity after vitrectomy for macular hole using deep learning-based artificial intelligence.
Artificial intelligence
Deep learning
Macular hole
Prediction
Visual acuity
Journal
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
ISSN: 1435-702X
Titre abrégé: Graefes Arch Clin Exp Ophthalmol
Pays: Germany
ID NLM: 8205248
Informations de publication
Date de publication:
Apr 2022
Apr 2022
Historique:
received:
19
05
2021
accepted:
19
09
2021
revised:
20
08
2021
pubmed:
13
10
2021
medline:
15
3
2022
entrez:
12
10
2021
Statut:
ppublish
Résumé
To create a model for prediction of postoperative visual acuity (VA) after vitrectomy for macular hole (MH) treatment using preoperative optical coherence tomography (OCT) images, using deep learning (DL)-based artificial intelligence. This was a retrospective single-center study. We evaluated 259 eyes that underwent vitrectomy for MHs. We divided the eyes into four groups, based on their 6-month postoperative Snellen VA values: (A) ≥ 20/20; (B) 20/25-20/32; (C) 20/32-20/63; and (D) ≤ 20/100. Training data were randomly selected, comprising 20 eyes in each group. Test data were also randomly selected, comprising 52 total eyes in the same proportions as those of each group in the total database. Preoperative OCT images with corresponding postoperative VA values were used to train the original DL network. The final prediction of postoperative VA was subjected to regression analysis based on inferences made with DL network output. We created a model for predicting postoperative VA from preoperative VA, MH size, and age using multivariate linear regression. Precision values were determined, and correlation coefficients between predicted and actual postoperative VA values were calculated in two models. The DL and multivariate models had precision values of 46% and 40%, respectively. The predicted postoperative VA values on the basis of DL and on preoperative VA and MH size were correlated with actual postoperative VA at 6 months postoperatively (P < .0001 and P < .0001, r = .62 and r = .55, respectively). Postoperative VA after MH treatment could be predicted via DL using preoperative OCT images with greater accuracy than multivariate linear regression using preoperative VA, MH size, and age.
Identifiants
pubmed: 34636995
doi: 10.1007/s00417-021-05427-2
pii: 10.1007/s00417-021-05427-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1113-1123Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Gass JD (1988) Idiopathic senile macular hole. Its early stages and pathogenesis. Arch Ophthalmol 106:629–639. https://doi.org/10.1001/archopht.1988.01060130683026
doi: 10.1001/archopht.1988.01060130683026
pubmed: 3358729
Kelly NE, Wendel RT (1991) Vitreous surgery for idiopathic macular holes. Results of a pilot study. Arch Ophthalmol 109:654–659. https://doi.org/10.1001/archopht.1991.01080050068031
doi: 10.1001/archopht.1991.01080050068031
pubmed: 2025167
Park DW, Sipperley JO, Sneed SR, Dugel PU, Jacobsen J (1999) Macular hole surgery with internal-limiting membrane peeling and intravitreous air. Ophthalmology 106:1392–1398. https://doi.org/10.1016/s0161-6420(99)00730-7
doi: 10.1016/s0161-6420(99)00730-7
pubmed: 10406628
Brooks HL (2000) Macular hole surgery with and without internal limiting membrane peeling. Ophthalmology 107:1939–1948. https://doi.org/10.1016/s0161-6420(00)00331-6
doi: 10.1016/s0161-6420(00)00331-6
pubmed: 11013203
Itoh Y, Inoue M, Rii T, Hiraoka T, Hirakata A (2012) Correlation between length of foveal cone outer segment tips line defect and visual acuity after macular hole closure. Ophthalmology 119:1438–1446. https://doi.org/10.1016/j.ophtha.2012.01.023
doi: 10.1016/j.ophtha.2012.01.023
pubmed: 22424577
Baba T, Kakisu M, Nizawa T, Oshitari T, Yamamoto S (2017) Superficial foveal avascular zone determined by optical coherence tomography angiography before and after macular hole surgery. Retina 37:444–450. https://doi.org/10.1097/IAE.0000000000001205
doi: 10.1097/IAE.0000000000001205
pubmed: 28225721
Amram AL, Mandviwala MM, Ou WC, Wykoff CC, Shah AR (2018) Predictors of visual acuity outcomes following vitrectomy for idiopathic macular hole. Ophthalmic Surg Lasers Imaging Retina 49:566–570. https://doi.org/10.3928/23258160-20180803-03
doi: 10.3928/23258160-20180803-03
pubmed: 30114300
Kim SH, Kim HK, Yang JY, Lee SC, Kim SS (2018) Visual recovery after macular hole surgery and related prognostic factors. Korean J Ophthalmol 32:140–146. https://doi.org/10.3341/kjo.2017.0085
doi: 10.3341/kjo.2017.0085
pubmed: 29611371
pmcid: 5906399
Kumagai K, Ogino N, Demizu S, Atsumi K, Kurihara H, Iwaki M, Ishigooka H, Tachi N (2000) Factors related to initial success in macular hole surgery. Nippon Ganka Gakkai Zasshi 104:792–796
pubmed: 11530369
Kokame GT, de Bustros S (1995) Visual acuity as a prognostic indicator in stage I macular holes. Am J Ophthalmol 120:112–114. https://doi.org/10.1016/s0002-9394(14)73768-7
doi: 10.1016/s0002-9394(14)73768-7
pubmed: 7611316
Ullrich S, Haritoglou C, Gass C, Schaumberger M, Ulbig MW, Kampik A (2002) Macular hole size as a prognostic factor in macular hole surgery. Br J Ophthalmol 86:390–393. https://doi.org/10.1136/bjo.86.4.390
doi: 10.1136/bjo.86.4.390
pubmed: 11914205
pmcid: 1771090
Scott RA, Ezra E, West JF, Gregor ZJ (2000) Visual and anatomical results of surgery for long standing macular holes. Br J Ophthalmol 84:150–153. https://doi.org/10.1136/bjo.84.2.150
doi: 10.1136/bjo.84.2.150
pubmed: 10655189
pmcid: 1723387
Jaycock PD, Bunce C, Xing W, Thomas D, Poon W, Gazzard G, Williamson TH, Laidlaw DA (2005) Outcomes of macular hole surgery: implications for surgical management and clinical governance. Eye (Lond) 19:879–884. https://doi.org/10.1038/sj.eye.6701679
doi: 10.1038/sj.eye.6701679
Gupta B, Laidlaw DA, Williamson TH, Shah SP, Wong R, Wren S (2009) Predicting visual success in macular hole surgery. Br J Ophthalmol 93:1488–1491. https://doi.org/10.1136/bjo.2008.153189
doi: 10.1136/bjo.2008.153189
pubmed: 19635721
Larsson J, Holm K, Lovestam-Adrian M (2006) The presence of an operculum verified by optical coherence tomography and other prognostic factors in macular hole surgery. Acta Ophthalmol Scand 84:301–304. https://doi.org/10.1111/j.1600-0420.2006.00672.x
doi: 10.1111/j.1600-0420.2006.00672.x
pubmed: 16704687
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539
doi: 10.1038/nature14539
Lee CS, Tyring AJ, Deruyter NP, Wu Y, Rokem A, Lee AY (2017) Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed Opt Express 8:3440–3448. https://doi.org/10.1364/BOE.8.003440
doi: 10.1364/BOE.8.003440
pubmed: 28717579
pmcid: 5508840
Lu D, Heisler M, Lee S, Ding GW, Navajas E, Sarunic MV, Beg MF (2019) Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network. Med Image Anal 54:100–110. https://doi.org/10.1016/j.media.2019.02.011
doi: 10.1016/j.media.2019.02.011
pubmed: 30856455
Alsaih K, Yusoff MZ, Tang TB, Faye I, Meriaudeau F (2020) Deep learning architectures analysis for age-related macular degeneration segmentation on optical coherence tomography scans. Comput Methods Programs Biomed 195:105566. https://doi.org/10.1016/j.cmpb.2020.105566
doi: 10.1016/j.cmpb.2020.105566
pubmed: 32504911
Asaoka R, Murata H, Hirasawa K, Fujino Y, Matsuura M, Miki A, Kanamoto T, Ikeda Y, Mori K, Iwase A, Shoji N, Inoue K, Yamagami J, Araie M (2019) Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images. Am J Ophthalmol 198:136–145. https://doi.org/10.1016/j.ajo.2018.10.007
doi: 10.1016/j.ajo.2018.10.007
pubmed: 30316669
Russakoff DB, Mannil SS, Oakley JD, Ran AR, Cheung CY, Dasari S, Riyazzuddin M, Nagaraj S, Rao HL, Chang D, Chang RT (2020) A 3D deep learning system for detecting referable glaucoma using full OCT macular cube scans. Transl Vis Sci Technol 9:12. https://doi.org/10.1167/tvst.9.2.12
doi: 10.1167/tvst.9.2.12
pubmed: 32704418
pmcid: 7347026
Nagasato D, Tabuchi H, Masumoto H, Enno H, Ishitobi N, Kameoka M, Niki M, Mitamura Y (2019) Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning. PLoS ONE 14:e0223965. https://doi.org/10.1371/journal.pone.0223965
doi: 10.1371/journal.pone.0223965
pubmed: 31697697
pmcid: 6837754
Sonobe T, Tabuchi H, Ohsugi H, Masumoto H, Ishitobi N, Morita S, Enno H, Nagasato D (2019) Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT. Int Ophthalmol 39:1871–1877. https://doi.org/10.1007/s10792-018-1016-x
doi: 10.1007/s10792-018-1016-x
pubmed: 30218173
De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, Askham H, Glorot X, O’Donoghue B, Visentin D, van den Driessche G, Lakshminarayanan B, Meyer C, Mackinder F, Bouton S, Ayoub K, Chopra R, King D, Karthikesalingam A, Hughes CO, Raine R, Hughes J, Sim DA, Egan C, Tufail A, Montgomery H, Hassabis D, Rees G, Back T, Khaw PT, Suleyman M, Cornebise J, Keane PA, Ronneberger O (2018) Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 24:1342–1350. https://doi.org/10.1038/s41591-018-0107-6
doi: 10.1038/s41591-018-0107-6
pubmed: 30104768
Kawczynski MG, Bengtsson T, Dai J, Hopkins JJ, Gao SS, Willis JR (2020) Development of deep learning models to predict best-corrected visual acuity from optical coherence tomography. Transl Vis Sci Technol 9:51. https://doi.org/10.1167/tvst.9.2.51
doi: 10.1167/tvst.9.2.51
pubmed: 32974088
pmcid: 7488630
Kingma D BJ Adam: (2014) A method for stochastic optimization. arXiv preprint arXiv:14126980
Lovie-Kitchin JE, Whittaker SG (1999) Prescribing near magnification for low vision patients. Clin Exp Optom 82:214–224. https://doi.org/10.1111/j.1444-0938.1999.tb06651.x
doi: 10.1111/j.1444-0938.1999.tb06651.x
pubmed: 12482267
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 2818–2826, https://doi.org/10.1109/CVPR.2016.308 .) Rethinking the Inception Architecture for Computer Vision,
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2019:618e626) Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis
Chawla NV, Japkowicz N, Kotcz A (2004) Editorial ACM SIGKDD Explorations Newsletter 6:1–6. https://doi.org/10.1145/1007730.1007733
doi: 10.1145/1007730.1007733
Kanda Y (2013) Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant 48:452–458. https://doi.org/10.1038/bmt.2012.244
doi: 10.1038/bmt.2012.244
pubmed: 23208313
pmcid: 23208313
Wendel RT, Patel AC, Kelly NE, Salzano TC, Wells JW, Novack GD (1993) Vitreous surgery for macular holes. Ophthalmology 100:1671–1676. https://doi.org/10.1016/s0161-6420(93)31419-3
doi: 10.1016/s0161-6420(93)31419-3
pubmed: 8233393