Ophthalmic microsurgery is technically difficult because the scale of required surgical tool manipulations challenge the limits of the surgeon's visual acuity, sensory perception, and physical dexterity. Intraoperative optical coherence tomography (OCT) imaging with micrometer-scale resolution is increasingly being used to monitor and provide enhanced real-time visualization of ophthalmic surgical maneuvers, but surgeons still face physical limitations when manipulating instruments inside the eye. Autonomously controlled robots are one avenue for overcoming these physical limitations. We demonstrate the feasibility of using learning from demonstration and reinforcement learning with an industrial robot to perform OCT-guided corneal needle insertions in an ex vivo model of deep anterior lamellar keratoplasty (DALK) surgery. Our reinforcement learning agent trained on ex vivo human corneas, then outperformed surgical fellows in reaching a target needle insertion depth in mock corneal surgery trials. This work shows the combination of learning from demonstration and reinforcement learning is a viable option for performing OCT guided robotic ophthalmic surgery.

Optical coherence tomography (OCT) has found great success in ophthalmology where it plays a key role in screening and diagnostics. Clinical ophthalmic OCT systems are typically deployed as tabletop instruments that require chinrest stabilization and trained ophthalmic photographers to operate. These requirements preclude OCT diagnostics in bedbound or unconscious patients who cannot use a chinrest, and restrict OCT screening to ophthalmology offices. We present a robotically-aligned OCT scanner capable of automatic eye imaging without chinrests. The scanner features eye tracking from fixed-base RGB-D cameras for coarse and stereo pupil cameras for fine alignment, as well as galvanometer aiming for fast lateral tracking, reference arm adjustment for fast axial tracking, and a commercial robot arm for slow lateral and axial tracking. We demonstrate the system's performance autonomously aligning with stationary eyes, pursuing moving eyes, and tracking eyes undergoing physiologic motion. The system demonstrates sub-millimeter eye tracking accuracy, 12 μm lateral pupil tracking accuracy, 83.2 ms stabilization time following step disturbance, and 9.7 Hz tracking bandwidth.

Deep anterior lamellar keratoplasty (DALK) is a promising technique for corneal transplantation that avoids the chronic immunosuppression comorbidities and graft rejection risk associated with penetrating keratoplasty (PKP), the standard procedure. In DALK, surgeons must insert a needle 90% through the 500 μm cornea without penetrating its underlying membrane. This pushes surgeons to their manipulation and visualization limits such that 59% of DALK attempts fail due to corneal perforation or inadequate needle depth. We propose a robot-assisted solution to jointly solve the manipulation and visualization challenges using a cooperatively-controlled, precise robot arm and live optical coherence tomography (OCT) imaging, respectively. Our system features an interface handle, with which the surgeon and robot cooperatively hold the tool, and a posterior corneal boundary virtual fixture driven by real-time OCT segmentation. A study in which three operators performed DALK needle insertions manually and cooperatively in ex vivo human corneas demonstrated an 84% improvement in perforation-free needle depth without an increased perforation rate.

Intraoperative 3D imaging has great potential for enhancing surgical visualization. This is especially so in ophthalmic surgery where live volumetric imaging from optical coherence tomography systems recently incorporated into surgical microscopes has freed surgeons from the otherwise universal top-down viewpoint. New viewpoints, however, disorient surgeons when directions of their hand motions and viewed tool motions do not align. We propose introducing a robotic surgery system to decouple surgeons' hands from their tools and ensure that viewed tool motions align in arbitrary viewpoints. We present a framework entitled Arbitrary Viewpoint Robotic Manipulation (AVRM) which governs how hand and tool motions should interact to minimize disorientation and thereby enable operations from desirable but previously untenable viewpoints. A crossover study in which 20 subjects completed mock surgical scenarios with an AVRM testbed system demonstrated that arbitrary viewpoints do not improve task performance unless automatic hand-tool misalignment correction is provided. When provided together with arbitrary viewpoints, automatic hand-tool misalignment correction reduces task completion time by 50% on average compared to a fixed top-down viewpoint.