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We developed a sensor-integrated eye surgical simulator for inner limiting membrane (ILM) peeling, a complicated intraocular surgery. For the quantitative characterization of the retinal surgical skills, we first introduced a quartz crystal resonator (QCR) force sensor that exhibits the high sensitivity, the dynamic range, and the rigidity. The dynamic range of the simulator with this sensor was from 0.087 mN to 7.7 N. We subsequently achieved the uniformity of the force sensitivity on the retinal area of ILM peeling by the parallel mechanical suspension and double ends flexure of the QCR sensor. The error proportion of the sensitivity in that retinal area was suppressed within ±14 %. Finally, we demonstrated the practical evaluation of ILM peeling on the training platform.

To evaluate surgeons’ skills and medical devices, interaction between surgical instruments and a physical surgery simulator can be measured by various sensors. This study proposes sensors for a physical surgery model which accurately replicates mechanical properties of human tissues using synthetic materials, and we named such sensors as ”Bionic Sensors”. As an example, we attempted to integrate a sensor measures the force applying to the fundus retina, into an eye surgery simulator, Bionic-EyE™. We designed a cantilever-type load sensor using a quartz crystal resonator (QCR) with a highly load resolution and wide dynamic range and integrated it in the fundus part of Bionic-EyE™ as a Bionic Sensor. The interaction of a microforceps and the fundus part was measured in a simulated task of the inner limiting membrane (ILM) peeling, and the maximum indentation load was approximately 20 mN when the task was performed by a skilled surgeon. The high sensitivity of the bionic sensor designed to measure the millinewton-order indentation force for the ILM peeling task was successfully demonstrated.

An artificial eye model was developed as an surgical simulator for training novice eye doctors on micro invasive glaucoma surgery (MIGS) to reduce the learning time. The eye model consists of an artificial sclera, trabecular meshwork (TM), and Schlemm’s canal (SC). We succeeded in fabrication of the sclera model with the structure of the SC by molding process using 3D printing models. We fabricated the sclera model with mechanical properties similar to human sclera. Artificial TM was fabricated, by using blowing method, with a thickness and stiffness within the range values of the human tissue. Polyvinyl chloride (PVC) was the main polymer used in fabrication of TM. The thickness of artificial TM was controlled by adjust the concentration of the PVC. A preliminary assessment, by removing the TM with a dual blade device (KAHOOK), for the proposed model made by an experienced eye doctors. Thus, an surgical simulator, for improving skills of novice surgeons on MIGS, was successfully developed.

Vitreoretinal surgery is one of the most difficult surgical operations, even for experienced surgeons. Thus, a master-slave eye surgical robot has been developed to assist the surgeon in safely performing vitreoretinal surgeries; however, in the master-slave control, the robotic positioning accuracy depends on the surgeon's coordination skills. This paper proposes a new method of autonomous robotic positioning using the shadow of the surgical instrument. First, the microscope image is segmented into three regions-namely, a micropipette, its shadow, and the eye ground-using a Gaussian mixture model (GMM). The tips of the micropipette and its shadow are then extracted from the contour lines of the segmented regions. The micropipette is then autonomously moved down to the simulated eye ground until the distance between the tips of micropipette and its shadow in the microscopic image reaches a predefined threshold. To handle possible occlusions, the tip of the shadow is estimated using a Kalman filter. Experiments to evaluate the robotic positioning accuracy in the vertical direction were performed. The results show that the autonomous positioning using the Kalman filter enhanced the accuracy of robotic positioning.