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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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Sáiz-Manzanares MC, Solórzano Mulas A, Escolar-Llamazares MC, Alcantud Marín F, Rodríguez-Arribas S, Velasco-Saiz R. Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis. CHILDREN (BASEL, SWITZERLAND) 2024; 11:381. [PMID: 38671598 PMCID: PMC11048911 DOI: 10.3390/children11040381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 03/15/2024] [Accepted: 03/20/2024] [Indexed: 04/28/2024]
Abstract
Advances in technology and artificial intelligence (smart healthcare) open up a range of possibilities for precision intervention in the field of health sciences. The objectives of this study were to analyse the functionality of using supervised (prediction and classification) and unsupervised (clustering) machine learning techniques to analyse results related to the development of functional skills in patients at developmental ages of 0-6 years. We worked with a sample of 113 patients, of whom 49 were cared for in a specific centre for people with motor impairments (Group 1) and 64 were cared for in a specific early care programme for patients with different impairments (Group 2). The results indicated that in Group 1, chronological age predicted the development of functional skills at 85% and in Group 2 at 65%. The classification variable detected was functional development in the upper extremities. Two clusters were detected within each group that allowed us to determine the patterns of functional development in each patient with respect to functional skills. The use of smart healthcare resources has a promising future in the field of early care. However, data recording in web applications needs to be planned, and the automation of results through machine learning techniques is required.
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Affiliation(s)
- María Consuelo Sáiz-Manzanares
- DATAHES Research Group, Consolidated Research Unit Nº. 348, Departamento de Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, 09001 Burgos, Spain;
| | | | - María Camino Escolar-Llamazares
- DATAHES Research Group, Consolidated Research Unit Nº. 348, Departamento de Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, 09001 Burgos, Spain;
| | - Francisco Alcantud Marín
- Department of Developmental and Educational Psychology, Universitat de València, 46010 València, Spain;
| | - Sandra Rodríguez-Arribas
- BEST-AI Research Group, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, 09006 Burgos, Spain;
| | - Rut Velasco-Saiz
- Facultad de Ciencias de la Salud, Universidad de Burgos, 09001 Burgos, Spain;
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