247
Views
1
CrossRef citations to date
0
Altmetric
Retina

Cross-camera Performance of Deep Learning Algorithms to Diagnose Common Ophthalmic Diseases: A Comparative Study Highlighting Feasibility to Portable Fundus Camera Use

, , , , , , , & show all
Pages 857-863 | Received 22 Feb 2023, Accepted 14 May 2023, Published online: 29 May 2023

References

  • Grassmann F, Mengelkamp J, Brandl C, Harsch S, Zimmermann ME, Linkohr B, Peters A, Heid IM, Palm C, Weber BHF. A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology. 2018;125(9):1410–1420. doi:10.1016/j.ophtha.2018.02.037.
  • Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, et al. Development and validation of a deep learning algorithm for the detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410. doi:10.1001/jama.2016.17216.
  • Phene S, Dunn RC, Hammel N, Liu Y, Krause J, Kitade N, Schaekermann M, Sayres R, Wu DJ, Bora A, et al. Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs. Ophthalmology. 2019;126(12):1627–1639. doi:10.1016/j.ophtha.2019.07.024.
  • Liu H, Li L, Wormstone IM, Qiao C, Zhang C, Liu P, Li S, Wang H, Mou D, Pang R, et al. Development and validation of a deep learning system to detect glaucomatous optic neuropathy using fundus photographs. JAMA Ophthalmol. 2019;137(12):1353–1360. doi:10.1001/jamaophthalmol.2019.3501.
  • Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, Tan GSW, Schmetterer L, Keane PA, Wong TY. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167–175. doi:10.1136/bjophthalmol-2018-313173.
  • Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology. 2018;125(8):1199–1206. doi:10.1016/j.ophtha.2018.01.023.
  • Keel S, Li Z, Scheetz J, Robman L, Phung J, Makeyeva G, Aung K, Liu C, Yan X, Meng W, et al. Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs. Clin Exp Ophthalmol. 2019;47(8):1009–1018. doi:10.1111/ceo.13575.
  • He T, Zhou Q, Zou Y. Automatic detection of age-related macular degeneration based on deep learning and local outlier factor algorithm. Diagnostics. 2022;12(2):532. doi:10.3390/diagnostics12020532.
  • Rim TH, Lee AY, Ting DS, Teo K, Betzler BK, Teo ZL, Yoo TK, Lee G, Kim Y, Lin AC, et al. Detection of features associated with neovascular age-related macular degeneration in ethnically distinct data sets by an optical coherence tomography: trained deep learning algorithm. Br J Ophthalmol. 2021;105(8):1133–1139. doi:10.1136/bjophthalmol-2020-316984.
  • Ibrahim MH, Hacibeyoglu M, Agaoglu A, Ucar F. Glaucoma disease diagnosis with an artificial algae-based deep learning algorithm. Med Biol Eng Comput. 2022;60(3):785–796. doi:10.1007/s11517-022-02510-6.
  • Jammal AA, Thompson AC, Mariottoni EB, Berchuck SI, Urata CN, Estrela T, Wakil SM, Costa VP, Medeiros FA. Human versus machine: comparing a deep learning algorithm to human gradings for detecting glaucoma on fundus photographs. Am J Ophthalmol. 2020;211:123–131. doi:10.1016/j.ajo.2019.11.006.
  • Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57(13):5200–5206. doi:10.1167/iovs.16-19964.
  • Keel S, Wu J, Lee PY, Scheetz J, He M. Visualizing deep learning models for the detection of referable diabetic retinopathy and glaucoma. JAMA Ophthalmol. 2019;137(3):288–292. doi:10.1001/jamaophthalmol.2018.6035.
  • Panwar A, Semwal G, Goel S, Gupta S. Stratification of the lesions in color fundus images of diabetic retinopathy patients using deep learning models and machine learning classifiers In Patgiri R, Bandyopadhyay S, Borah MD, Emilia Balas V (Eds). Edge Analytics. Lecture Notes in Electrical Engineering, vol. 869. Springer, Singapore. 2022. doi:10.1007/978-981-19-0019-8_49.
  • Wongchaisuwat N, Trinavarat A, Rodanant N, Thoongsuwan S, Phasukkijwatana N, Prakhunhungsit S, Preechasuk L, Wongchaisuwat P. In-Person verification of deep learning algorithm for diabetic retinopathy screening using different techniques across fundus image devices. Transl Vis Sci Technol. 2021;10(13):17. doi:10.1167/tvst.10.13.17.
  • He M, Li Z, Liu C, Shi D, Tan Z. Deployment of artificial intelligence in real-world practice: opportunity and challenge. Asia Pac J Ophthalmol. 2020;9(4):299–307. doi:10.1097/APO.0000000000000301.
  • Tsai M-J, Hsieh Y-T, Tsai C-H, Chen M, Hsieh A-T, Tsai C-W, Chen M-L. Cross-camera external validation for artificial intelligence software in diagnosis of diabetic retinopathy. J Diabetes Res. 2022;2022:5779276. doi:10.1155/2022/5779276.
  • Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, Lee PY, Shaw J, Ting D, Wong TY, et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care. 2018;41(12):2509–2516. doi:10.2337/dc18-0147.
  • Wang Y, Shi D, Tan Z, Niu Y, Jiang Y, Xiong R, Peng G, He M. Screening referable diabetic retinopathy using a semi-automated deep learning algorithm assisted approach. Front Med. 2021;8:740987. doi:10.3389/fmed.2021.740987.
  • Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–174. doi:10.2307/2529310.
  • Ting DSW, Peng L, Varadarajan AV, Keane PA, Burlina PM, Chiang MF, Schmetterer L, Pasquale LR, Bressler NM, Webster DR, et al. Deep learning in ophthalmology: the technical and clinical considerations. Prog Retin Eye Res. 2019;72:100759. doi:10.1016/j.preteyeres.2019.04.003.
  • Raumviboonsuk P, Krause J, Chotcomwongse P, Sayres R, Raman R, Widner K, Campana BJL, Phene S, Hemarat K, Tadarati M, et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ Digit Med. 2019;2:25. doi:10.1038/s41746-019-0099-8.
  • Tufail A, Rudisill C, Egan C, Kapetanakis VV, Salas-Vega S, Owen CG, Lee A, Louw V, Anderson J, Liew G, et al. Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology. 2017;124(3):343–351. doi:10.1016/j.ophtha.2016.11.014.
  • Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39. doi:10.1038/s41746-018-0040-6.
  • Li T, Bo W, Hu C, Kang H, Liu H, Wang K, Fu H. Applications of deep learning in fundus images: a review. Med Image Anal [Internet]. 2021;69:101971. doi:10.1016/j.media.2021.101971.
  • Aurangzeb K, Aslam S, Alhussein M, Naqvi RA, Arsalan M, Haider SI. Contrast enhancement of fundus images by employing modified PSO for improving the performance of deep learning models. IEEE Access. 2021;9(99):47930–47945. doi:10.1109/ACCESS.2021.3068477.
  • Tky A, Ihrb C, Jin K, Isl B, Jsk B, Hong K, Jyc E. Deep learning can generate traditional retinal fundus photographs using ultra-widefield images via generative adversarial networks. Comput Methods Programs Biomed. 2020;197:105761. doi:10.1016/j.cmpb.2020.105761.
  • Tahghighi P, Zoroofi RA, Saffi S, Ramezani A. Heightmap reconstruction of macula on color fundus images using conditional generative adversarial networks. In 2021 26th International Computer Conference, Computer Society of Iran (CSICC) (pp. 1–6), Tehran, Iran. 2021, doi:10.48550/arXiv.2009.01601.
  • Kamran SA, Hossain KF, Tavakkoli A, Zuckerbrod SL, Baker SA, Sanders KM. Fundus2Angio: a conditional GAN architecture for generating fluorescein angiography images from retinal fundus photography. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science, vol. 12510. Springer, Cham. 2020. doi:10.1007/978-3-030-64559-5_10.
  • Yi X, Walia E, Babyn P. Generative adversarial network in medical imaging: a review. Med Image Anal. 2019;58:101552. doi:10.1016/j.media.2019.101552.
  • Zhong Z, Zheng L, Zheng Z, Li S, Yang Y. Camera style adaptation for person re-identification. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5157–5166), Salt Lake City, UT, USA. 2018. doi:10.1109/CVPR.2018.00541.
  • Liu J, Liu H, Gong S, Tang Z, Xie Y, Yin H, Niyoyita JP. Automated cardiac segmentation of cross-modal medical images using unsupervised multi-domain adaptation and spatial neural attention structure. Med Image Anal. 2021;72:102135. doi:10.1016/j.media.2021.102135.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.