Accuracy of image guided robotic assistance in cochlear implant surgery

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Electrophysiology and Auditory Performance of Children with Profound Sensoryneural Hearing loss after Cochlear Implant Surgery

INTRODUCTION: Cochlear implantation is a powerful tool to gain hearing ability and to achieve age appropriate communication skills in children with severe to profound sensorineural hearing loss. OBJECTIVE: To compare the intraoperative and postoperative telemetry of the children with Cochlear implants and to assess the auditory performance of children with sensorineural hearing loss after surgery. METHODOLOGY: A prospective study was done involving 63 children operated for cochlear implant at Upgraded Institute of Otorhinolaryngology, Madras Medical College, Chennai. Intraoperative and postoperative electrode impedance as well as telemetry measurements were done. CAP score was used to assess the auditory performance preoperatively and at follow up. RESULTS: Majority (41.3%) of the children was in 13 – 24 months age group and 57% were males. Around 45% of children reached CAP score of 3 by 6 months, 38% achieved 4 and 14.3% reached score of 5 by 12 months with a significant increase in follow up. Surgery before 3 years of age had a significant relationship with performance. The electrode impedance and telemetry measurements were found to be predictors of device function. CONCLUSION: Our results show that, early implantation leads to better auditory performance compared to implantation at later ages. Electrode impedance and telemetry measures provide valuable information regarding the output and response of the auditory system

Three-dimensional models of cochlear implants : a review of their development and how they could support management and maintenance of cochlear implant performance

Three-dimensional (3D) computational modelling of the auditory periphery forms an integral part of modern-day research in cochlear implants (CIs). These models consist of a volume conduction description of implanted stimulation electrodes and the current distribution around these, coupled to auditory nerve fibre models. Cochlear neural activation patterns can then be predicted for a given input stimulus. The objective of this article is to present the context of 3D modelling within the field of CIs, the different models and approaches to models that have been developed over the years, as well as the applications and potential applications of these models. The process of development of 3D models is discussed, and the article places specific emphasis on the complementary roles of generic models and user-specific models, as the latter is important for translation of these models into clinical application.http://tandfonline.com/toc/inet202017-05-31hb2016Electrical, Electronic and Computer Engineerin

AUGMENTED REALITY AND INTRAOPERATIVE C-ARM CONE-BEAM COMPUTED TOMOGRAPHY FOR IMAGE-GUIDED ROBOTIC SURGERY

Minimally-invasive robotic-assisted surgery is a rapidly-growing alternative to traditionally open and laparoscopic procedures; nevertheless, challenges remain. Standard of care derives surgical strategies from preoperative volumetric data (i.e., computed tomography (CT) and magnetic resonance (MR) images) that benefit from the ability of multiple modalities to delineate different anatomical boundaries. However, preoperative images may not reflect a possibly highly deformed perioperative setup or intraoperative deformation. Additionally, in current clinical practice, the correspondence of preoperative plans to the surgical scene is conducted as a mental exercise; thus, the accuracy of this practice is highly dependent on the surgeon’s experience and therefore subject to inconsistencies. In order to address these fundamental limitations in minimally-invasive robotic surgery, this dissertation combines a high-end robotic C-arm imaging system and a modern robotic surgical platform as an integrated intraoperative image-guided system. We performed deformable registration of preoperative plans to a perioperative cone-beam computed tomography (CBCT), acquired after the patient is positioned for intervention. From the registered surgical plans, we overlaid critical information onto the primary intraoperative visual source, the robotic endoscope, by using augmented reality. Guidance afforded by this system not only uses augmented reality to fuse virtual medical information, but also provides tool localization and other dynamic intraoperative updated behavior in order to present enhanced depth feedback and information to the surgeon. These techniques in guided robotic surgery required a streamlined approach to creating intuitive and effective human-machine interferences, especially in visualization. Our software design principles create an inherently information-driven modular architecture incorporating robotics and intraoperative imaging through augmented reality. The system's performance is evaluated using phantoms and preclinical in-vivo experiments for multiple applications, including transoral robotic surgery, robot-assisted thoracic interventions, and cocheostomy for cochlear implantation. The resulting functionality, proposed architecture, and implemented methodologies can be further generalized to other C-arm-based image guidance for additional extensions in robotic surgery