Research Topic

Robotics and Artificial Intelligence for Retinal Imaging and Surgery

About this Research Topic

In recent years, there has been a surge of opportunities for the development of retinal imaging, analysis, and surgery. These developments have been achieved through advances in image processing and artificial intelligence such as computer vision-based techniques, traditional machine learning, and deep learning. These techniques have been applied rapidly and widely in the field of medical image analysis and are becoming a better way to advance ophthalmology in practice.

As well as advances in imaging and analysis, autonomous systems have also contributed towards developments in ophthalmology practices and surgical operations. The aim is to work towards overcoming biological limitations, increasing the safety of retinal procedures, and reducing surgeon training times. Examples of these developments include robot-assisted cataract surgery and microrobots for detecting oxygen levels.

This Research Topic aims to gather high-quality research papers on newly proposed work as well as state-of-the-art developments in retinal imaging and surgical operations. We aim to explore the advances in image processing, artificial intelligence, computer vision-based techniques, traditional machine learning and deep learning techniques for retinal image analysis, robotic applications for retinal imaging and surgery, as well as research papers surrounding retinal disease diagnosis such AI techniques for diagnostic of Retinopathy and other eye diseases.

We hope the research collected in this Research Topic will provide an overview of the most recent advances in retinal imaging and surgery, working towards safer and more efficient processes and techniques for treatments and diagnoses.

We welcome all types of contributions including theoretical, engineering and applied. Topics of interest include, but are not limited to:

• Retinal Image Analysis
• Optic Imaging Techniques
• Retinal Image Segmentations
• Retinal Image Enhancement
• Deep Learning Techniques for Retinal Image
• Robust Training Processing for Retinal Image Database
• Analysis of Retinal Images Vessels Based on AI Techniques.
• Retinal Image Understanding
• Image Techniques Comparison with Deep Learning Techniques: Retinal Images Case.
• Detail Review on Analysis Process Of Retinal Images.
• Robot Vision for Retinal Imaging and Surgery
• Robot-Assisted Retinal Imaging
• Robot-Assisted Retinal Surgery


Keywords: Retinal images, Machine learning, Image processing, Robot-Assisted Eye Surgery, Retinal Surgery


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

In recent years, there has been a surge of opportunities for the development of retinal imaging, analysis, and surgery. These developments have been achieved through advances in image processing and artificial intelligence such as computer vision-based techniques, traditional machine learning, and deep learning. These techniques have been applied rapidly and widely in the field of medical image analysis and are becoming a better way to advance ophthalmology in practice.

As well as advances in imaging and analysis, autonomous systems have also contributed towards developments in ophthalmology practices and surgical operations. The aim is to work towards overcoming biological limitations, increasing the safety of retinal procedures, and reducing surgeon training times. Examples of these developments include robot-assisted cataract surgery and microrobots for detecting oxygen levels.

This Research Topic aims to gather high-quality research papers on newly proposed work as well as state-of-the-art developments in retinal imaging and surgical operations. We aim to explore the advances in image processing, artificial intelligence, computer vision-based techniques, traditional machine learning and deep learning techniques for retinal image analysis, robotic applications for retinal imaging and surgery, as well as research papers surrounding retinal disease diagnosis such AI techniques for diagnostic of Retinopathy and other eye diseases.

We hope the research collected in this Research Topic will provide an overview of the most recent advances in retinal imaging and surgery, working towards safer and more efficient processes and techniques for treatments and diagnoses.

We welcome all types of contributions including theoretical, engineering and applied. Topics of interest include, but are not limited to:

• Retinal Image Analysis
• Optic Imaging Techniques
• Retinal Image Segmentations
• Retinal Image Enhancement
• Deep Learning Techniques for Retinal Image
• Robust Training Processing for Retinal Image Database
• Analysis of Retinal Images Vessels Based on AI Techniques.
• Retinal Image Understanding
• Image Techniques Comparison with Deep Learning Techniques: Retinal Images Case.
• Detail Review on Analysis Process Of Retinal Images.
• Robot Vision for Retinal Imaging and Surgery
• Robot-Assisted Retinal Imaging
• Robot-Assisted Retinal Surgery


Keywords: Retinal images, Machine learning, Image processing, Robot-Assisted Eye Surgery, Retinal Surgery


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

25 June 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

25 June 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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