Project Summary I am an assistant professor in the department of Otolaryngology-Head and Neck Surgery at the Johns Hopkins School of Medicine, where my practice is focused on neurotology and lateral skull base surgery. I am applying for a mentored surgeon-scientist career development award (CDA) to obtain further training in robotics, deep learning and computer vision. This will further my long-term career goals of improving neurotologic surgical outcomes through novel applications of engineering methods and ultimately to investigate semi-autonomous, robotic interventions in the inner ear and skull base which are beyond the limits of the human hand alone. Operating in the temporal bone and lateral skull base is technically demanding due to complex three- dimensional anatomy, small working spaces and delicate neurovascular structures. Many of these challenges are ideally suited to semi-autonomous surgical platforms to augment a surgeon’s skills with robotic and image- guided assistance. Despite the widespread implementation of robotic surgery and image guidance in other areas of the body, the field of neurotology has had relatively little adoption of this technology. We believe one reason for this is the precise registration needed in this field, where millimeter differences differentiate a successful from a catastrophic result. This CDA proposes expanding on my prior work investigating cooperative control robotics and virtual safety barriers by using computer vision and deep learning networks to develop highly accurate surgical image registration. This CDA aims to provide me with multi-disciplinary training in the departments of Otolaryngology, Biomedical Engineering and Computer Science. Specific training goals include: (1) Training in robotics, statistical shape modeling and computer tomography landmark segmentation, (2) Training in deep learning networks and computer vision video image registration, (3) Integrating this training, with my knowledge of temporal bone and skull base surgery to develop into an independent investigator (4) Pursue additional training in the ethical and responsible conduct of research. The research plan addresses the hypothesis that virtual safety barriers can be accurately enforced by a cooperative control robot, and computer vision methods can be used to automate accurate placement and registration of these safety barriers. I believe that the integration of these techniques will allow for semi- autonomous surgical methods resulting in improved surgical safety and efficiency. The specific aims of the proposal are to: (1) Develop and Validate Cooperative Control Robot Enforced Virtual Safety Barriers for Cortical Mastoidectomy (2) Develop and Test Autonomous Segmentation of Lateral Skull Base Anatomy (3) Develop Video-Based, Fiducial-less, Surgical Image Registration to Detect and Update the 3-D Position of Temporal Bone Anatomy from Intraoperative Stereoscopic Microscope Video.