{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T07:51:16Z","timestamp":1772956276752,"version":"3.50.1"},"reference-count":59,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001348","name":"Agency for Science, Technology and Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001348","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013898","name":"Republic of Turkey Ministry of National Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013898","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1016\/j.compmedimag.2024.102461","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T08:57:10Z","timestamp":1732611430000},"page":"102461","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Uncertainty-aware regression model to predict post-operative visual acuity in patients with macular holes"],"prefix":"10.1016","volume":"119","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5589-4430","authenticated-orcid":false,"given":"Burak","family":"Kucukgoz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Declan C.","family":"Murphy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David H.","family":"Steel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4084-7778","authenticated-orcid":false,"given":"Boguslaw","family":"Obara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2024.102461_b1","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","article-title":"A review of uncertainty quantification in deep learning: Techniques, applications and challenges","volume":"76","author":"Abdar","year":"2021","journal-title":"Inf. Fusion"},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102461_b2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1001\/jamaophthalmol.2016.5870","article-title":"Incidence of and risk factors for developing idiopathic macular hole among a diverse group of patients throughout the United States","volume":"135","author":"Ali","year":"2017","journal-title":"JAMA Ophthalmol."},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102461_b3","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s11517-019-02066-y","article-title":"AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images","volume":"58","author":"Alqudah","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.compmedimag.2024.102461_b4","first-page":"14927","article-title":"Deep evidential regression","volume":"33","author":"Amini","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2024.102461_b5","series-title":"2022 5th Information Technology for Education and Development","first-page":"1","article-title":"Machine learning model for classifying free space optics channel impairments","author":"Babatunde","year":"2022"},{"key":"10.1016\/j.compmedimag.2024.102461_b6","first-page":"10","article-title":"Brightness calculation in digital image processing","volume":"vol. 2007","author":"Bezryadin","year":"2007"},{"key":"10.1016\/j.compmedimag.2024.102461_b7","first-page":"93","article-title":"LOF: identifying density-based local outliers","volume":"vol. 29","author":"Breunig","year":"2000"},{"key":"10.1016\/j.compmedimag.2024.102461_b8","article-title":"Semi-supervised and unsupervised deep visual learning: A survey","author":"Chen","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compmedimag.2024.102461_b9","article-title":"Macular hole morphology and measurement using an automated three dimensional image segmentation algorithm","author":"Chen","year":"2020","journal-title":"Br. Med. J. Open Ophthalmol."},{"issue":"9","key":"10.1016\/j.compmedimag.2024.102461_b10","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1111\/ceo.12801","article-title":"Tasmanian macular hole study: whole population-based incidence of full thickness macular hole","volume":"44","author":"Darian-Smith","year":"2016","journal-title":"Clin. Exp. Ophthalmol."},{"key":"10.1016\/j.compmedimag.2024.102461_b11","series-title":"NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications","article-title":"A closer look at distribution shifts and out-of-distribution generalization on graphs","author":"Ding","year":"2021"},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102461_b12","doi-asserted-by":"crossref","first-page":"6572","DOI":"10.1038\/s41467-022-34025-x","article-title":"Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology","volume":"13","author":"Dolezal","year":"2022","journal-title":"Nature Commun."},{"issue":"3","key":"10.1016\/j.compmedimag.2024.102461_b13","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1093\/biomet\/81.3.425","article-title":"Ideal spatial adaptation by wavelet shrinkage","volume":"81","author":"Donoho","year":"1994","journal-title":"Biometrika"},{"key":"10.1016\/j.compmedimag.2024.102461_b14","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N., 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In: International Conference on Learning Representations."},{"issue":"12","key":"10.1016\/j.compmedimag.2024.102461_b15","doi-asserted-by":"crossref","first-page":"2611","DOI":"10.1016\/j.ophtha.2013.07.042","article-title":"The international vitreomacular traction study group classification of vitreomacular adhesion, traction, and macular hole","volume":"120","author":"Duker","year":"2013","journal-title":"Ophthalmology"},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102461_b16","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1097\/IAE.0000000000002013","article-title":"Macular hole hydrodissection: surgical technique for the treatment of persistent, chronic, and large macular holes","volume":"39","author":"Felfeli","year":"2019","journal-title":"Retina"},{"issue":"12","key":"10.1016\/j.compmedimag.2024.102461_b17","doi-asserted-by":"crossref","first-page":"e1221","DOI":"10.1016\/S2214-109X(17)30393-5","article-title":"Global causes of blindness and distance vision impairment 1990\u20132020: a systematic review and meta-analysis","volume":"5","author":"Flaxman","year":"2017","journal-title":"Lancet Glob. Health"},{"key":"10.1016\/j.compmedimag.2024.102461_b18","doi-asserted-by":"crossref","unstructured":"Francis, L., Sreenath, N., 2019. Pre-processing techniques for detection of blurred images. In: International Conference on Computational Intelligence and Data Engineering. pp. 59\u201366.","DOI":"10.1007\/978-981-13-6459-4_7"},{"key":"10.1016\/j.compmedimag.2024.102461_b19","series-title":"The 33rd International Conference on Machine Learning","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","volume":"vol. 48","author":"Gal","year":"2016"},{"issue":"2","key":"10.1016\/j.compmedimag.2024.102461_b20","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","article-title":"Res2net: A new multi-scale backbone architecture","volume":"43","author":"Gao","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compmedimag.2024.102461_b21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep Residual Learning for Image Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.compmedimag.2024.102461_b22","first-page":"319","article-title":"Determining optical flow","volume":"vol. 281","author":"Horn","year":"1981"},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102461_b23","doi-asserted-by":"crossref","DOI":"10.21037\/atm-20-1789","article-title":"A multi-center study of prediction of macular hole status after vitrectomy and internal limiting membrane peeling by a deep learning model","volume":"9","author":"Hu","year":"2021","journal-title":"Ann. Transl. Med."},{"key":"10.1016\/j.compmedimag.2024.102461_b24","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., 2017. Densely Connected Convolutional Networks. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 2261\u20132269.","DOI":"10.1109\/CVPR.2017.243"},{"issue":"5","key":"10.1016\/j.compmedimag.2024.102461_b25","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1038\/eye.2013.12","article-title":"United Kingdom national ophthalmology database study of vitreoretinal surgery: report 1; case mix, complications, and cataract","volume":"27","author":"Jackson","year":"2013","journal-title":"Eye"},{"issue":"3","key":"10.1016\/j.compmedimag.2024.102461_b26","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/j.ophtha.2012.09.003","article-title":"United Kingdom national ophthalmology database study of vitreoretinal surgery: report 2, macular hole","volume":"120","author":"Jackson","year":"2013","journal-title":"Ophthalmology"},{"key":"10.1016\/j.compmedimag.2024.102461_b27","series-title":"The exponential family: Conjugate priors","author":"Jordan","year":"2009"},{"issue":"2","key":"10.1016\/j.compmedimag.2024.102461_b28","doi-asserted-by":"crossref","DOI":"10.1167\/tvst.9.2.51","article-title":"Development of deep learning models to predict best-corrected visual acuity from optical coherence tomography","volume":"9","author":"Kawczynski","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"issue":"5","key":"10.1016\/j.compmedimag.2024.102461_b29","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1001\/archopht.1991.01080050068031","article-title":"Vitreous surgery for idiopathic macular holes: results of a pilot study","volume":"109","author":"Kelly","year":"1991","journal-title":"Arch. Ophthalmol."},{"key":"10.1016\/j.compmedimag.2024.102461_b30","doi-asserted-by":"crossref","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","article-title":"Deep learning applications in medical image analysis","volume":"6","author":"Ker","year":"2018","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102461_b31","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s004170050234","article-title":"Correlation of quantitative three-dimensional measurements of macular hole size with visual acuity after vitrectomy","volume":"237","author":"Kobayashi","year":"1999","journal-title":"Graefe\u2019s Arch. Clin. Exp. Ophthalmol."},{"key":"10.1016\/j.compmedimag.2024.102461_b32","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2024.3369676","article-title":"Deep learning using preoperative optical coherence tomography images to predict visual acuity following surgery for idiopathic full-thickness macular holes","author":"Kucukgoz","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.compmedimag.2024.102461_b33","doi-asserted-by":"crossref","unstructured":"Kucukgoz, B., Yapici, M.M., Steel, D.H., Obara, B., 2023. Evaluation of 2D and 3D Deep Learning Approaches For Predicting Visual Acuity Following Surgery for Idiopathic Full-Thickness Macular Holes In Spectral Domain Optical Coherence Tomography Images. In: 2023 International Symposium on Image and Signal Processing and Analysis. pp. 1\u20136.","DOI":"10.1109\/ISPA58351.2023.10279422"},{"issue":"3","key":"10.1016\/j.compmedimag.2024.102461_b34","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1159\/000355492","article-title":"Predicting visual outcome following surgery for idiopathic macular holes","volume":"231","author":"Kusuhara","year":"2014","journal-title":"Ophthalmologica"},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102461_b35","doi-asserted-by":"crossref","DOI":"10.1167\/tvst.11.4.6","article-title":"Predicting visual improvement after macular hole surgery: a combined model using deep learning and clinical features","volume":"11","author":"Lachance","year":"2022","journal-title":"Transl. Vis. Sci. Technol."},{"key":"10.1016\/j.compmedimag.2024.102461_b36","first-page":"6405","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume":"vol. 30","author":"Lakshminarayanan","year":"2017"},{"key":"10.1016\/j.compmedimag.2024.102461_b37","doi-asserted-by":"crossref","unstructured":"Li, B., Han, Z., Li, H., Fu, H., Zhang, C., 2022. Trustworthy long-tailed classification. In: The IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 6970\u20136979.","DOI":"10.1109\/CVPR52688.2022.00684"},{"key":"10.1016\/j.compmedimag.2024.102461_b38","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S., 2022. A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 11976\u201311986.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"10.1016\/j.compmedimag.2024.102461_b39","first-page":"97","article-title":"Optimal management of idiopathic macular holes","author":"Madi","year":"2016","journal-title":"Clin. Ophthalmol."},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102461_b40","doi-asserted-by":"crossref","DOI":"10.1136\/bmjophth-2016-000057","article-title":"Vitreoretinal interface abnormalities in middle-aged adults with visual impairment in the UK Biobank study: prevalence, impact on visual acuity and associations","volume":"1","author":"McKibbin","year":"2017","journal-title":"BMJ Open Ophthalmol."},{"key":"10.1016\/j.compmedimag.2024.102461_b41","doi-asserted-by":"crossref","first-page":"151","DOI":"10.5201\/ipol.2013.20","article-title":"Horn-schunck optical flow with a multi-scale strategy","volume":"3","author":"Meinhardt-Llopis","year":"2013","journal-title":"Image Process. Line"},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102461_b42","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/j.ophtha.2014.10.014","article-title":"The epidemiology of vitreoretinal interface abnormalities as detected by spectral-domain optical coherence tomography: the beaver dam eye study","volume":"122","author":"Meuer","year":"2015","journal-title":"Ophthalmology"},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102461_b43","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s40123-019-00207-y","article-title":"Optical coherence tomography-based deep-learning models for classifying normal and age-related macular degeneration and exudative and non-exudative age-related macular degeneration changes","volume":"8","author":"Motozawa","year":"2019","journal-title":"Ophthalmol. Ther."},{"key":"10.1016\/j.compmedimag.2024.102461_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.oret.2020.06.005","article-title":"Predicting post-operative vision for macular hole with automated image analysis","author":"Murphy","year":"2020","journal-title":"Ophthalmol. Retina"},{"issue":"2","key":"10.1016\/j.compmedimag.2024.102461_b45","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/TMI.2017.2767908","article-title":"Multi-scale segmentation and surface fitting for measuring 3D macular holes","volume":"37","author":"Nasrulloh","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2024.102461_b46","first-page":"1","article-title":"Prediction of postoperative visual acuity after vitrectomy for macular hole using deep learning\u2013based artificial intelligence","author":"Obata","year":"2021","journal-title":"Graefe\u2019s Arch. Clin. Exp. Ophthalmol."},{"issue":"12","key":"10.1016\/j.compmedimag.2024.102461_b47","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.3390\/diagnostics11122319","article-title":"Artificial intelligence and OCT angiography in full thickness macular hole. New developments for personalized medicine","volume":"11","author":"Rizzo","year":"2021","journal-title":"Diagnostics"},{"issue":"3","key":"10.1016\/j.compmedimag.2024.102461_b48","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.jvcir.2012.01.008","article-title":"Measuring perceptual contrast in digital images","volume":"23","author":"Simone","year":"2012","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102461_b49","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1038\/s41433-020-0844-x","article-title":"Factors affecting anatomical and visual outcome after macular hole surgery: findings from a large prospective UK cohort","volume":"35","author":"Steel","year":"2021","journal-title":"Eye"},{"key":"10.1016\/j.compmedimag.2024.102461_b50","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., 2016. Rethinking the Inception Architecture for Computer Vision. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 2818\u20132826.","DOI":"10.1109\/CVPR.2016.308"},{"key":"10.1016\/j.compmedimag.2024.102461_b51","series-title":"International Conference on Machine Learning","first-page":"10096","article-title":"Efficientnetv2: Smaller models and faster training","author":"Tan","year":"2021"},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102461_b52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12886-020-01382-4","article-title":"Classification of optical coherence tomography images using a capsule network","volume":"20","author":"Tsuji","year":"2020","journal-title":"BMC Ophthalmol."},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102461_b53","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1136\/bjo.86.4.390","article-title":"Macular hole size as a prognostic factor in macular hole surgery","volume":"86","author":"Ullrich","year":"2002","journal-title":"Br. J. Ophthalmol."},{"key":"10.1016\/j.compmedimag.2024.102461_b54","series-title":"European Conference on Computer Vision","first-page":"299","article-title":"Bayescap: Bayesian identity cap for calibrated uncertainty in frozen neural networks","author":"Upadhyay","year":"2022"},{"issue":"5","key":"10.1016\/j.compmedimag.2024.102461_b55","doi-asserted-by":"crossref","first-page":"e240","DOI":"10.1016\/S2589-7500(20)30060-1","article-title":"Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study","volume":"2","author":"Xie","year":"2020","journal-title":"Lancet Digital Health"},{"key":"10.1016\/j.compmedimag.2024.102461_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.101985","article-title":"A survey on incorporating domain knowledge into deep learning for medical image analysis","volume":"69","author":"Xie","year":"2021","journal-title":"Med. Image Anal."},{"issue":"2","key":"10.1016\/j.compmedimag.2024.102461_b57","doi-asserted-by":"crossref","DOI":"10.1167\/tvst.9.2.11","article-title":"Methodological challenges of deep learning in optical coherence tomography for retinal diseases: a review","volume":"9","author":"Yanagihara","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102461_b58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1687-6180-2014-17","article-title":"One-class kernel subspace ensemble for medical image classification","volume":"2014","author":"Zhang","year":"2014","journal-title":"EURASIP J. Adv. Signal Process."},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102461_b59","doi-asserted-by":"crossref","DOI":"10.1016\/j.metrad.2023.100003","article-title":"A review of uncertainty estimation and its application in medical imaging","volume":"1","author":"Zou","year":"2023","journal-title":"Meta-Radiol."}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611124001381?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611124001381?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T03:11:41Z","timestamp":1736219501000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611124001381"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":59,"alternative-id":["S0895611124001381"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102461","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Uncertainty-aware regression model to predict post-operative visual acuity in patients with macular holes","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102461","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"102461"}}