Feature Tracking and Segmentation in Real Time via Deep Learning in Vitreoretinal Surgery

Nespolo, Rogerio Garcia; Yi, Darvin; Cole, Emily; Warren, Alexis K.; Wang, Daniel; Leiderman, Yannek I.

doi:10.1016/j.oret.2022.10.002

Cited by 10 publications

(2 citation statements)

References 31 publications

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“…In vitreoretinal surgery, Nespolo et al [126] utilized 606 surgical image frames to train a model known as YOLACT++, which is an instance of a fully convolutional neural network specifically designed for segmentation tasks. They have also built a platform where this AI system could locate, classify, and segment tissue and instruments in real time.…”

Section: The Role Of Ai In Improving Glaucoma Outcomesmentioning

confidence: 99%

Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection

Zhu,

Salowe,

Chow

et al. 2024

Bioengineering

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Glaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care. This review summarizes current capabilities, future outlooks, and practical translation considerations. For enhanced screening, algorithms analyzing retinal photographs and machine learning models synthesizing risk factors can identify high-risk patients needing diagnostic workup and close follow-up. To augment definitive diagnosis, deep learning techniques detect characteristic glaucomatous patterns by interpreting results from optical coherence tomography, visual field testing, fundus photography, and other ocular imaging. AI-powered platforms also enable continuous monitoring, with algorithms that analyze longitudinal data alerting physicians about rapid disease progression. By integrating predictive analytics with patient-specific parameters, AI can also guide precision medicine for individualized glaucoma treatment selections. Advances in robotic surgery and computer-based guidance demonstrate AI’s potential to improve surgical outcomes and surgical training. Beyond the clinic, AI chatbots and reminder systems could provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications remain critical to successfully implementing these emerging technologies. This review highlights AI’s vast capabilities to transform glaucoma care while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside.

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Section: The Role Of Ai In Improving Glaucoma Outcomesmentioning

confidence: 99%

Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection

Zhu,

Salowe,

Chow

et al. 2024

Bioengineering

View full text Add to dashboard Cite

show abstract

“…This advancement relies on AI models utilizing sophisticated algorithms integrating various data sources, such as imaging technology, EHR, and demographics. While research on AI applications in glaucoma surgery and training is ongoing, Nespolo and colleagues [32] have demonstrated the successful use of an AI CNN in vitreoretinal surgery. This AI system has the potential to offer real-time intraoperative guidance and analyze instrument movements postsurgery, showcasing the promising prospects of AI in reducing intraocular surgical errors and enhancing training processes.…”

Section: Ai Guided Management Of Glaucomamentioning

confidence: 99%

Transforming Glaucoma Care with Artificial Intelligence: A Comprehensive Review of Diagnosis, Monitoring, and Treatment Algorithms

Amberg,

Espaillat

2024

Preprint

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This review article explores the transformative impact of Artificial Intelligence (AI) on glaucoma care, focusing on advancements in diagnosis, monitoring, and treatment algorithms. AI technologies have revolutionized the field of ophthalmology by enhancing the accuracy and efficiency of diagnosing glaucoma. Furthermore, AI-powered monitoring systems enable continuous and personalized patient care, facilitating early detection of disease progression. Integrating AI algorithms in treatment protocols promises individualized and optimized therapeutic strategies for better management of glaucoma. This comprehensive review elucidates AI applications' current state and future directions in transforming glaucoma care, emphasizing the potential benefits of improving patient outcomes.

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Efficient Spatiotemporal Learning of Microscopic Video for Augmented Reality-Guided Phacoemulsification Cataract Surgery

Tu,

Ye,

Young

et al. 2023

Lecture Notes in Computer Science

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Feature Tracking and Segmentation in Real Time via Deep Learning in Vitreoretinal Surgery

Cited by 10 publications

References 31 publications

Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection

Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection

Transforming Glaucoma Care with Artificial Intelligence: A Comprehensive Review of Diagnosis, Monitoring, and Treatment Algorithms

Efficient Spatiotemporal Learning of Microscopic Video for Augmented Reality-Guided Phacoemulsification Cataract Surgery

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