Geometric Morphometrics Can Predict Postoperative Visual Acuity Changes in Patients With Epiretinal Membrane: A Retrospective Study

被引:1
作者
Miyagi, Sugao [1 ]
Oishi, Akio [1 ,2 ]
Tsuiki, Eiko [1 ]
Kitaoka, Takashi [1 ]
机构
[1] Nagasaki Univ, Grad Sch Biomed Sci, Dept Ophthalmol & Visual Sci, 1-7-1 Sakamoto, Nagasaki, Japan
[2] Nagasaki Univ, Grad Sch Biomed Sci, Dept Ophthalmol & Visual Sci, 1-7-1 Sakamoto, Nagasaki 8528501, Japan
来源
TRANSLATIONAL VISION SCIENCE & TECHNOLOGY | 2023年 / 12卷 / 01期
关键词
retina; epiretinal membrane; geometric morphometrics; optical coherence tomography; bending energy; MULTI-LABEL CLASSIFICATION; DIABETIC-RETINOPATHY; GLOBAL PREVALENCE; RETINAL LESIONS; FUNDUS IMAGES; BURDEN;
D O I
10.1167/tvst.12.1.24
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
Purpose: To investigate the efficacy of the geometric morphometrics method for the evaluation of retinal deformation in patients with epiretinal membrane (ERM) and deter-mine whether the degree of deformation can serve as a predictive factor for postopera-tive visual outcome.Methods: We retrospectively evaluated data from 29 eyes of 29 patients with primary ERM. Preoperative optical coherence tomography images were compared with images of their normal fellow eyes using the geometric morphometrics thin-plate spline technique. Conventional parameters such as retinal layer thickness and previously reported indices were also measured. The correlation between the preoperative param-eters and visual acuity was evaluated. Statistical comparisons were performed using a paired t-test, and associations between the optical coherence tomography image parameters and visual acuity were determined using Spearman's rank correlation coeffi-cient.Results: Bending energy, which was calculated using geometric morphometrics, was significantly associated with visual acuity as well as conventional optical coherence tomography parameters and previously reported indices. Multiple regression analysis showed that bending energy was an independent predictive factor for postoperative visual acuity changes.Conclusions: The geometric morphometrics method is an effective approach for evalu-ating the severity of ERM and predicting the efficacy of surgery.Translational Relevance: Geometric morphometrics can effectively evaluate retinal deformation in eyes with epiretinal membrane.
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共 35 条
  • [1] Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features
    Abbas, Qaisar
    Fondon, Irene
    Sarmiento, Auxiliadora
    Jimenez, Soledad
    Alemany, Pedro
    [J]. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2017, 55 (11) : 1959 - 1974
  • [2] Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions
    Abdelmaksoud, Eman
    El-Sappagh, Shaker
    Barakat, Sherif
    Abuhmed, Tamer
    Elmogy, Mohammed
    [J]. IEEE ACCESS, 2021, 9 : 15939 - 15960
  • [3] A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection
    AbdelMaksoud, Eman
    Barakat, Sherif
    Elmogy, Mohammed
    [J]. COMPUTERS IN BIOLOGY AND MEDICINE, 2020, 126 (126)
  • [4] To Combat Multi-Class Imbalanced Problems by Means of Over-Sampling Techniques
    Abdi, Lida
    Hashemi, Sattar
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2016, 28 (01) : 238 - 251
  • [5] Application of deep learning for retinal image analysis: A review
    Badar, Maryam
    Haris, Muhammad
    Fatima, Anam
    [J]. COMPUTER SCIENCE REVIEW, 2020, 35
  • [6] Barandela R, 2004, LECT NOTES COMPUT SC, V3138, P806
  • [7] Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases
    Burlina, Philippe
    Paul, William
    Mathew, Philip
    Joshi, Neil
    Pacheco, Katia D.
    Bressler, Neil M.
    [J]. JAMA OPHTHALMOLOGY, 2020, 138 (10) : 1070 - 1077
  • [8] Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks
    Cen, Ling-Ping
    Ji, Jie
    Lin, Jian-Wei
    Ju, Si-Tong
    Lin, Hong-Jie
    Li, Tai-Ping
    Wang, Yun
    Yang, Jian-Feng
    Liu, Yu-Fen
    Tan, Shaoying
    Tan, Li
    Li, Dongjie
    Wang, Yifan
    Zheng, Dezhi
    Xiong, Yongqun
    Wu, Hanfu
    Jiang, Jingjing
    Wu, Zhenggen
    Huang, Dingguo
    Shi, Tingkun
    Chen, Binyao
    Yang, Jianling
    Zhang, Xiaoling
    Luo, Li
    Huang, Chukai
    Zhang, Guihua
    Huang, Yuqiang
    Ng, Tsz Kin
    Chen, Haoyu
    Chen, Weiqi
    Pang, Chi Pui
    Zhang, Mingzhi
    [J]. NATURE COMMUNICATIONS, 2021, 12 (01)
  • [9] Classification of Fundus Images Based on Deep Learning for Detecting Eye Diseases
    Chea, Nakhim
    Nam, Yunyoung
    [J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2021, 67 (01): : 411 - 426
  • [10] Clinically applicable deep learning for diagnosis and referral in retinal disease
    De Fauw, Jeffrey
    Ledsam, Joseph R.
    Romera-Paredes, Bernardino
    Nikolov, Stanislav
    Tomasev, Nenad
    Blackwell, Sam
    Askham, Harry
    Glorot, Xavier
    O'Donoghue, Brendan
    Visentin, Daniel
    van den Driessche, George
    Lakshminarayanan, Balaji
    Meyer, Clemens
    Mackinder, Faith
    Bouton, Simon
    Ayoub, Kareem
    Chopra, Reena
    King, Dominic
    Karthikesalingam, Alan
    Hughes, Cian O.
    Raine, Rosalind
    Hughes, Julian
    Sim, Dawn A.
    Egan, Catherine
    Tufail, Adnan
    Montgomery, Hugh
    Hassabis, Demis
    Rees, Geraint
    Back, Trevor
    Khaw, Peng T.
    Suleyman, Mustafa
    Cornebise, Julien
    Keane, Pearse A.
    Ronneberger, Olaf
    [J]. NATURE MEDICINE, 2018, 24 (09) : 1342 - +