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Poh SSJ, Sia JT, Yip MYT, Tsai ASH, Lee SY, Tan GSW, Weng CY, Kadonosono K, Kim M, Yonekawa Y, Ho AC, Toth CA, Ting DSW. Artificial Intelligence, Digital Imaging, and Robotics Technologies for Surgical Vitreoretinal Diseases. Ophthalmol Retina 2024; 8:633-645. [PMID: 38280425 DOI: 10.1016/j.oret.2024.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/14/2024] [Accepted: 01/19/2024] [Indexed: 01/29/2024]
Abstract
OBJECTIVE To review recent technological advancement in imaging, surgical visualization, robotics technology, and the use of artificial intelligence in surgical vitreoretinal (VR) diseases. BACKGROUND Technological advancements in imaging enhance both preoperative and intraoperative management of surgical VR diseases. Widefield imaging in fundal photography and OCT can improve assessment of peripheral retinal disorders such as retinal detachments, degeneration, and tumors. OCT angiography provides a rapid and noninvasive imaging of the retinal and choroidal vasculature. Surgical visualization has also improved with intraoperative OCT providing a detailed real-time assessment of retinal layers to guide surgical decisions. Heads-up display and head-mounted display utilize 3-dimensional technology to provide surgeons with enhanced visual guidance and improved ergonomics during surgery. Intraocular robotics technology allows for greater surgical precision and is shown to be useful in retinal vein cannulation and subretinal drug delivery. In addition, deep learning techniques leverage on diverse data including widefield retinal photography and OCT for better predictive accuracy in classification, segmentation, and prognostication of many surgical VR diseases. CONCLUSION This review article summarized the latest updates in these areas and highlights the importance of continuous innovation and improvement in technology within the field. These advancements have the potential to reshape management of surgical VR diseases in the very near future and to ultimately improve patient care. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Stanley S J Poh
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Josh T Sia
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | - Michelle Y T Yip
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | - Andrew S H Tsai
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Shu Yen Lee
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Gavin S W Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Christina Y Weng
- Department of Ophthalmology, Baylor College of Medicine, Houston, Texas
| | | | - Min Kim
- Department of Ophthalmology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Yoshihiro Yonekawa
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Allen C Ho
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Cynthia A Toth
- Departments of Ophthalmology and Biomedical Engineering, Duke University, Durham, North Carolina
| | - Daniel S W Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore; Byers Eye Institute, Stanford University, Palo Alto, California.
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Effects of image-sharpening algorithm on surgical field visibility during 3D heads-up surgery for vitreoretinal diseases. Sci Rep 2023; 13:2758. [PMID: 36797311 PMCID: PMC9935873 DOI: 10.1038/s41598-023-29882-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 02/11/2023] [Indexed: 02/18/2023] Open
Abstract
We conducted clinical and experimental studies to investigate the effects of image-sharpening algorithms and color adjustments, which enabled real-time processing of live surgical images with a delay of 0.004 s. The images were processed with image-sharpening intensities of 0%, 12.5%, 25%, and 50% during cataract surgery, vitrectomy, peeling of epiretinal membrane, and peeling of internal limiting membrane (ILM) with the Ngenuity 3D visualization system. In addition, the images obtained with a yellow filter during the ILM peeling were processed with color adjustments. Five vitreoretinal surgeons scored the clarity of the images on a 10-point scale. The images of a 1951 United States Air Force grating target placed in no fluid (control), saline, and 0.1% and 1% milk solution were evaluated. The results showed that the mean visibility score increased significantly from 5.0 ± 0.6 at 0% to 6.4 ± 0.6 at 12.5%, 7.3 ± 0.7 at 25%, and 7.5 ± 0.9 at 50% (P < 0.001). The visibility scores during ILM peeling improved significantly with color adjustments (P = 0.005). In the experimental study, the contrast of the grating targets blurred by the 0.1% and 1% milk solution increased significantly by the image-sharpening procedure. We conclude that the image-sharpening algorithms and color adjustments improved the intraoperative visibility of 3D heads-up surgery.
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