Abstract
Retinal detachment (RD) is a serious ocular disease that can lead to permanent vision loss. In cases with fundus-obscuring vitreous hemorrhage (VH), it is difficult to detect RD even using ocular ultrasonography. We developed a convolutional neural network (CNN) based on the You Only Look Once version 5 (YOLOv5) architecture to detect RD and VH on B-scan ultrasound images. The model was trained using 2,188 images and validated using 1,042 images. We applied image enhancement techniques, including unsharp masking (UM), to improve the detection accuracy. The final model (Incorporating fivefold cross-validation along with previous techniques) achieved overall accuracies of 96.6%, 99.2%, and 98.0% for RD, VH, and RD with VH, respectively. Our deep-learning algorithm showed high accuracy in detecting RD and VH on ocular ultrasound images. In cases with fundus-obscuring VH, our deep-learning algorithm might be useful for detecting RD as a supportive tool on ocular ultrasound images.
Similar content being viewed by others
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Mitry, D., Charteris, D. G., Fleck, B. W., Campbell, H. & Singh, J. The epidemiology of rhegmatogenous retinal detachment: Geographical variation and clinical associations. Br. J. Ophthalmol. 94, 678–684. https://doi.org/10.1136/bjo.2009.157727 (2010).
Spraul, C. W. & Grossniklaus, H. E. Vitreous hemorrhage. Surv. Ophthalmol. 42, 3–39. https://doi.org/10.1016/S0039-6257(97)84041-6 (1997).
Kendall, C. J. et al. Diagnostic ophthalmic ultrasound for radiologists. Neuroimaging Clin. N. Am. 25, 327–365. https://doi.org/10.1016/j.nic.2015.05.001 (2015).
Parchand, S., Singh, R. & Bhalekar, S. Reliability of ocular ultrasonography findings for pre-surgical evaluation in various vitreo-retinal disorders. Semin. Ophthalmol. 29, 236–241. https://doi.org/10.3109/08820538.2013.821506 (2014).
Lahham, S. et al. Point-of-care ultrasonography in the diagnosis of retinal detachment, vitreous Hemorrhage, and vitreous detachment in the emergency department. JAMA Netw. Open 2, e192162. https://doi.org/10.1001/jamanetworkopen.2019.2162 (2019).
Ting, D. S. W. et al. Artificial intelligence and deep learning in ophthalmology. Br. J. Ophthalmol. 103, 167–175. https://doi.org/10.1136/bjophthalmol-2018-313173 (2019).
Schmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M. & Bogunović, H. Artificial intelligence in retina. Prog. Retin. Eye Res. 67, 1–29. https://doi.org/10.1016/j.preteyeres.2018.07.004 (2018).
Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410. https://doi.org/10.1001/jama.2016.17216 (2016).
De Fauw, J. et al. 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 (2018).
Adithya, V. K. et al. Development and validation of an offline deep learning algorithm to detect vitreoretinal abnormalities on ocular ultrasound. Indian J. Ophthalmol. 70, 1145–1149. https://doi.org/10.4103/ijo.IJO_2119_21 (2022).
Chen, D. et al. A deep learning model for screening multiple abnormal findings in ophthalmic ultrasonography (with video). Transl. Vis. Sci. Technol. 10, 22. https://doi.org/10.1167/tvst.10.4.22 (2021).
Singh, L. K., Garg, H., Pooja, N. A. & Khanna, M. Performance analysis of machine learning techniques for glaucoma detection based on textural and intensity features. Int. J. Innovative Comput. Appl. 11, 216–230. https://doi.org/10.1504/IJICA.2020.111230 (2020).
Singh, L. K. et al. A three-stage novel framework for efficient and automatic glaucoma classification from retinal fundus images. Multimed. Tools Appl. 83, 85421–85481. https://doi.org/10.1007/s11042-024-19603-z (2024).
Singh, L. K. & Khanna, M. Introduction to artificial intelligence and current trends. Innov. Artif. Intell. Hum. Comput. Interact. Digit. Era. https://doi.org/10.1016/B978-0-323-99891-8.00001-2 (2023).
Sorenson, J. A., Niklason, L. T. & Nelson, J. A. Photographic unsharp masking in chest radiography. Invest. Radiol. 16, 281–288. https://doi.org/10.1097/00004424-198107000-00007 (1981).
Panetta, K., Zhou, Y., Agaian, S. & Jia, H. Nonlinear unsharp masking for mammogram enhancement. IEEE Trans. Inf. Technol. Biomed. 15, 918–928. https://doi.org/10.1109/TITB.2011.2164259 (2011).
Edla, D. R., Simi, V. R. & Joseph, J. A noise-robust and overshoot-free alternative to unsharp masking for enhancing the acuity of MR images. J. Digit. Imaging 35, 1041–1060. https://doi.org/10.1007/s10278-022-00585-z (2022).
Wei, Q., Chen, Q., Zhao, C. & Jiang, R. Performance of automated machine learning in detecting fundus diseases based on ophthalmologic B-scan ultrasound images. BMJ Open Ophthalmol. https://doi.org/10.1136/bmjophth-2024-001873 (2024).
Ye, X. et al. Ocular disease detection with deep learning (fine-grained image categorization) applied to ocular B-scan ultrasound images. Ophthalmol. Ther. 13, 2645–2659. https://doi.org/10.1007/s40123-024-01009-7 (2024).
Caki, O. et al. Automated detection of retinal detachment using deep learning-based segmentation on ocular ultrasonography images. Transl. Vis. Sci. Technol. 14, 26. https://doi.org/10.1167/tvst.14.2.26doi:10.1167/tvst.14.2.26 (2025).
Acknowledgements
We would like to express our gratitude to the following undergraduate students from the Faculty of Engineering at the University of Miyazaki for their valuable contribution to the data analysis in this research: Yuichiro Uchida, Ruon Kanda, Taiyo Nagayama. Their assistance in analyzing the data was crucial to the success of this study.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Contributions
N.T. collected the data, prepared the figures, and wrote the main manuscript text. N.T., T.H., and Y.I. designed the study. H.T. performed the data analysis. All authors reviewed the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethics approval
This study was approved by the Institutional Review Board of University of Miyazaki (approval number: O-1065).
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Toyama, N., Hidaka, T., Tamura, H. et al. Deep learning-based detection of retinal detachment with vitreous hemorrhage in ocular ultrasound images. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38272-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-38272-6
