Forces and trauma associated with minimally invasive image-guided cochlear implantation

Objective. Minimally invasive image-guided cochlear implantation (CI) utilizes a patient-customized microstereotactic frame to access the cochlea via a single drill-pass. We investigate the average force and trauma associated with the insertion of lateral wall CI electrodes using this technique. Study Design. Assessment using cadaveric temporal bones. Setting. Laboratory setup. Subjects and Methods. Microstereotactic frames for 6 fresh cadaveric temporal bones were built using CT scans to determine an optimal drill path following which drilling was performed. CI electrodes were inserted using surgical forceps to manually advance the CI electrode array, via the drilled tunnel, into the cochlea. Forces were recorded using a 6-axis load sensor placed under the temporal bone during the insertion of lateral wall electrode arrays (2 each of Nucleus CI422, MED-EL standard, and modified MED-EL electrodes with stiffeners). Tissue histology was performed by microdissection of the otic capsule and apical photo documentation of electrode position and intracochlear tissue. Results. After drilling, CT scanning demonstrated successful access to cochlea in all 6 bones. Average insertion forces ranged from 0.009 to 0.078 N. Peak forces were in the range of 0.056 to 0.469 N. Tissue histology showed complete scala tympani insertion in 5 specimens and scala vestibuli insertion in the remaining specimen with depth of insertion ranging from 360° to 600°. No intracochlear trauma was identified. Conclusion. The use of lateral wall electrodes with the minimally invasive image-guided CI approach was associated with insertion forces comparable to traditional CI surgery. Deep insertions were obtained without identifiable trauma. © American Academy of Otolaryngology-Head and Neck Surgery Foundation 2014

Wire-Actuated Parallel Robots for Cochlear Implantation with in-vivo Sensory Feedback

Robot-assisted cochlear implant (CI) surgery is a new research area that emerged in the last decade. The goal of robotic assistance is to improve patient hearing outcomes through improved surgical access and the minimization of intracochlear trauma during implantation. This thesis presents several research efforts converging on a system for robotic atraumatic CI insertion. The work begins with the characterization of fundamental aspects of CI implantation. This leads to the synthesis of a robot design for the implantation task. Lastly, intelligent control through in-vivo sensory feedback is investigated for improvement in CI insertion and final placement. The motivation behind this research stems from fundamental knowledge gaps in both characterization of the CI surgical domain and in robot design and control. Current solutions for robot-assisted CI surgery do not exhibit adaptability to changes from nominal CI insertion plans. This lead to exploration of a new domain of in-vivo sensory guided robotic insertion of CI electrode arrays. The contributions of this work include a system architecture derived from the clinical specifications of CI surgery while simultaneously exploring theoretical gaps in the areas of mechanism design and static balancing of serial and parallel mechanisms. Methodical derivation of specifications for surgical access during CI implantation are presented and include available workspace, kinematic behavior of under-actuated implants, and baseline expectations of insertion forces. From this, the synthesis of a robotic wire-driven insertion platform for CI is presented. Lastly, both force and intra-cochlear impedance data collected by the proposed robot are used to add intelligent correction to the implantation procedure. These corrections include physical misalignment of the robotic system to the patient anatomy and incorrect models of the non-visible intra-cochlear geometry. Novel algorithms utilizing in-vivo sensory feedback for robot-assisted CI insertion guidance and fault detection are proposed and experimentally demonstrated using several robotic platforms. The implications of this research extend to providing new methods of CI insertion and also design of compact parallel robots with remote actuation

On Not Being Able to Build: Thinking Space, Boundaries and the Other with Lacan’s Discourse of the Capitalist

Jacques Lacan’s fifth Discourse, or Discourse of the Capitalist, suggests that relations of difference are being immobilized or rendered redundant. What individuals are left with is a variety of strategies with which they try to cope, upholding a modicum of consistence and reality. Drawing on cultural examples and Bernard Stiegler’s use of psychoanalysis, this paper examines the relationship between experiences of space, the feeling of nonbeing and the encounter with the Other. If controlling space and enforcing spatial boundaries is the last strategy for keeping vestiges of the Other in working order, a radical re-thinking of space/milieu and objects/designs is necessary for individuals to starting imagining a future beyond capitalism’s creative stagnation and catastrophe

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers