Subretinal injection is a delicate and complex microsurgery. The main surgical difficulties come from the surgeon's hand tremor, dexterous motion, and insufficient visual feedback. In order to begin addressing these challenges, this paper presents a robot system for subretinal insertion integrated with intraoperative optical coherence tomography (OCT). The surgical workflow using this system consists of two main parts: the first part is the manual robot control, which aims the target before approaching the retinal surface, while considering the remote center of motion (RCM) constraint. When the injection area has been located precisely, needle is inserted into retina. To ensure \revise{surgical safety}, needle insertion depth is estimated using OCT images on a continuous basis. A soft RCM control method is designed and integrated for the controller of our hybrid parallel-serial surgical robot. Safety and accuracy performance evaluation with a 15 ms control loop shows that the worst case RCM deviation error is within 1 mm. Experimental results demonstrated that the proposed system has the ability to improve surgical outcomes by surgeons overcome their physical limitations in order to enable a better dexterous motion, and furthermore enhancing their visual feedback for a better intraocular perception.

This paper introduces the design and development of a new robotic system to assist surgeons performing ophthalmic surgeries. The robot itself is very compact and similar to an average human hand in size. Its primary application is intraocular micromanipulation in order to overcome the existing challenges in treatment of diseases like Retinal Vein Occlusion (RVO). The novel hybrid mechanism designed for this robot allows microscale motions and is stable in the presence of vibrations common in operation room (OR). The robotic system can be easily integrated into standard operation rooms and does not require modification of conventional surgical tools. This compact microsurgical system is suitable for mounting on the patient's head and thereby, solves the problem of patient motion. The compatibility of the robotic system with a real world surgical setup was evaluated and confirmed in this work.

Machine learning and especially deep learning techniques are dominating medical image and data analysis. This article reviews machine learning approaches proposed for diagnosing ophthalmic diseases during the last four years. Three diseases are addressed in this survey, namely diabetic retinopathy, age-related macular degeneration, and glaucoma. The review covers over 60 publications and 25 public datasets and challenges related to the detection, grading, and lesion segmentation of the three considered diseases. Each section provides a summary of the public datasets and challenges related to each pathology and the current methods that have been applied to the problem. Furthermore, the recent machine learning approaches used for retinal vessels segmentation, and methods of retinal layers and fluid segmentation are reviewed. Two main imaging modalities are considered in this survey, namely color fundus imaging, and optical coherence tomography. Machine learning approaches that use eye measurements and visual field data for glaucoma detection are also included in the survey. Finally, the authors provide their views, expectations and the limitations of the future of these techniques in the clinical practice.

Ophthalmic microsurgery is known to be a challenging operation, which requires very precise and dexterous manipulation. Image guided robot-assisted surgery (RAS) is a promising solution that brings significant improvements in outcomes and reduces the physical limitations of human surgeons. However, this technology must be further developed before it can be routinely used in clinics. One of the problems is the lack of proper calibration between the robotic manipulator and appropriate imaging device. In this work, we developed a flexible framework for hand-eye calibration of an ophthalmic robot with a microscope-integrated Optical Coherence Tomography (MI-OCT) without any markers. The proposed method consists of three main steps: a) we estimate the OCT calibration parameters; b) with micro-scale displacements controlled by the robot, we detect and segment the needle tip in 3D-OCT volume; c) we find the transformation between the coordinate system of the OCT camera and the coordinate system of the robot. We verified the capability of our framework in ex-vivo pig eye experiments and compared the results with a reference method (marker-based). In all experiments, our method showed a small difference from the marker based method, with a mean calibration error of 9.2 μm and 7.0 μm, respectively. Additionally, the noise test shows the \Revise{robustness} of the proposed method.

This paper presents the kinematic and dynamic analysis of a robot for 6 Degrees Of Freedom (DOF) micromanipulation. This robot is designed for performing intraocular manipulation but its application is not limited to ophthalmic surgery. The novel hybrid parallel-serial mechanism designed for this robot enables microscale motions with high stiffness and sufficient output forces. This portable robot can be easily integrated into standard biomedical environments and does not require any modification of conventional surgical tools. The contribution of this work is a novel design of a miniature micromanipulator comprising piezo actuator based parallel coupled joints which allow adjustable Remote Center of Motion (RCM). The advantages of the introduced mechanism compared to similar mechanisms are compactness, stiffness and simplicity of mathematical computation.