Faganharding2974

CONCLUSIONS The results indicate that complex synthetic training datasets can be used to specifically guide optical flow estimation. Our proposed algorithm therefore lays the foundation for a robust system which can assist with intra-operative tracking of moving surgical targets even when occluded.PURPOSE Basal cell carcinoma (BCC) is the most commonly diagnosed cancer and the number of diagnosis is growing worldwide due to increased exposure to solar radiation and the aging population. Reduction of positive margin rates when removing BCC leads to fewer revision surgeries and consequently lower health care costs, improved cosmetic outcomes and better patient care. In this study, we propose the first use of a perioperative mass spectrometry technology (iKnife) along with a deep learning framework for detection of BCC signatures from tissue burns. METHODS Resected surgical specimen were collected and inspected by a pathologist. With their guidance, data were collected by burning regions of the specimen labeled as BCC or normal, with the iKnife. Data included 190 scans of which 127 were normal and 63 were BCC. A data augmentation approach was proposed by modifying the location and intensity of the peaks of the original spectra, through noise addition in the time and frequency domains. A symmetric autoencoder was built by simultaneously optimizing the spectral reconstruction error and the classification accuracy. Using t-SNE, the latent space was visualized. RESULTS The autoencoder achieved an accuracy (standard deviation) of 96.62 (1.35%) when classifying BCC and normal scans, a statistically significant improvement over the baseline state-of-the-art approach used in the literature. The t-SNE plot of the latent space distinctly showed the separability between BCC and normal data, not visible with the original data. Augmented data resulted in significant improvements to the classification accuracy of the baseline model. CONCLUSION We demonstrate the utility of a deep learning framework applied to mass spectrometry data for surgical margin detection. We apply the proposed framework to an application with light surgical overhead and high incidence, the removal of BCC. The learnt models can accurately separate BCC from normal tissue.PURPOSE Eye gaze tracking is proving to be beneficial in many biomedical applications. The performance of systems based on eye gaze tracking is very much dependent on how accurate their calibration is. It has been reported that the gaze tracking accuracy deteriorates cumulatively and significantly with usage time. This impedes the wide use of gaze tracking in user interfaces. METHODS Explicit re-calibration, typically requiring the user's active attention, is time-consuming and can interfere with the user's main activity. Therefore, we propose an implicit re-calibration method, which can rectify the deterioration of the gaze tracking accuracy without bringing about the user's deliberate attention. We make use of hand-eye coordination, with the reasonable assumption that the eye gaze follows the pointer during a selection task, to acquire additional calibration points during normal usage of a gaze-contingent system. We construct a statistical model for the calibration and the hand-eye coordination and apply the Gaussian process regression framework to perform the re-calibration. RESULTS To validate our model and method, we performed a user study on ultrasonography tasks on a gaze-contingent interface for ultrasound machines. Results suggest that our method can rectify the tracking accuracy deterioration for [Formula see text] of all cases where deterioration occurs in our user study. With another benchmark dataset, our method can redress tracking accuracy to a level comparable to the initial calibration in more than [Formula see text] of the cases. CONCLUSIONS Our implicit re-calibration method is a practical and convenient fix for tracking accuracy deterioration in gaze-contingent user interfaces, and in particular for gaze-contingent ultrasound machines.PURPOSE Surgical simulations play an increasingly important role in surgeon education and developing algorithms that enable robots to perform surgical subtasks. To model anatomy, finite element method (FEM) simulations have been held as the gold standard for calculating accurate soft tissue deformation. Unfortunately, their accuracy is highly dependent on the simulation parameters, which can be difficult to obtain. METHODS In this work, we investigate how live data acquired during any robotic endoscopic surgical procedure may be used to correct for inaccurate FEM simulation results. Since FEMs are calculated from initial parameters and cannot directly incorporate observations, we propose to add a correction factor that accounts for the discrepancy between simulation and observations. We train a network to predict this correction factor. RESULTS To evaluate our method, we use an open-source da Vinci Surgical System to probe a soft tissue phantom and replay the interaction in simulation. We train the network to correct for the difference between the predicted mesh position and the measured point cloud. This results in 15-30% improvement in the mean distance, demonstrating the effectiveness of our approach across a large range of simulation parameters. CONCLUSION We show a first step towards a framework that synergistically combines the benefits of model-based simulation and real-time observations. It corrects discrepancies between simulation and the scene that results from inaccurate modeling parameters. This can provide a more accurate simulation environment for surgeons and better data with which to train algorithms.PURPOSE Neuronavigation systems making use of augmented reality (AR) have been the focus of much research in the last couple of decades. In recent years, there has been considerable interest in using mobile devices for AR in the operating room (OR). We propose a complete system that performs real-time AR video augmentation on a mobile device in the context of image-guided neurosurgery. METHODS MARIN (mobile augmented reality interactive neuronavigation system) improves upon the state of the art in terms of performance, allowing real-time augmentation, and interactivity by allowing users to interact with the displayed data. The system was tested in a user study with 17 subjects for qualitative and quantitative evaluation in the context of target localization and brought into the OR for preliminary feasibility tests, where qualitative feedback from surgeons was obtained. RESULTS The results of the user study showed that MARIN performs significantly better in terms of both time ([Formula see text]) and accuracy ([Formula see text]) for the task of target localization in comparison with a traditional image-guided neurosurgery (IGNS) navigation system. Further, MARIN AR visualization was found to be more intuitive and allowed users to estimate target depth more easily. CONCLUSION MARIN improves upon previously proposed mobile AR neuronavigation systems with its real-time performance, higher accuracy, full integration in the normal workflow and greater interactivity and customizability of the displayed information. selleckchem The improvement in efficiency and usability over previous systems will facilitate bringing AR into the OR.Sirtuin-2 (Sirt2) is a member of the NAD (+)-dependent protein deacetylase family involved in neuroprotection, cellular metabolism, homeostasis, and stress responses after injury of the nervous system. So far, no data have been published describing the role of SIRT2 in motor functional recovery after damage. We found that SIRT2 expression and deacetylase activity were increased within motoneurons after axotomy. To shed light onto the biological relevance of this change, we combined in vitro and in vivo models with pharmacological and genetic ablation approaches. We found that SIRT2 KO (knockout) mice exhibited improved functional recovery after sciatic nerve crush. SIRT2 activity blockage, using AK7, increased neurite outgrowth and length in organotypic spinal cord cultures and human cell line models. SIRT2 blockage enhanced the acetyltransferase activity of p300, which in turn increased the levels of an acetylated form of p53 (Ac-p53 k373), histone 3 (Ac-H3K9), and expression of GAP43, a downstream marker of regeneration. Lastly, we verified that p300 acetyltransferase activity is essential for these effects. Our results suggest that bolstering an epigenetic shift that promotes SIRT2 inhibition can be an effective therapy to increase functional recovery after peripheral nerve injury.Cortical spreading depolarizations (SD) are strongly associated with worse tissue injury and clinical outcomes in the setting of aneurysmal subarachnoid hemorrhage (SAH). Animal studies have suggested a causal relationship, and new therapies to target SDs are starting to be tested in clinical studies. A recent set of single-center randomized trials assessed the effect of the phosphodiesterase inhibitor cilostazol in patients with SAH. Cilostazol led to improved functional outcomes and SD-related metrics in treated patients through a putative mechanism of improved cerebral blood flow. Another promising therapeutic approach includes attempts to block SDs with, for example, the NMDA receptor antagonist ketamine. SDs have emerged not only as a therapeutic target but also as a potentially useful biomarker for brain injury following SAH. Additional clinical and preclinical experimental work is greatly needed to assess the generalizability of existing therapeutic trials and to better delineate the relationship between SDs, SAH, and functional outcome.Abnormal neural activity, particularly in the rostrodorsal anterior cingulate cortex (rdACC), appears to be responsible for intense alcohol craving. Neuromodulation of the rdACC using cortical implants may be an option for individuals with treatment-resistant alcohol dependence. This study assessed the effectiveness and feasibility of suppressing alcohol craving using cortical implants of the rdACC using a controlled one-group pre- and post-test study design. Eight intractable alcohol-dependent participants (four males and four females) were implanted with two Lamitrode 44 electrodes over the rdACC bilaterally connected to an internal pulse generator (IPG). The primary endpoint, self-reported alcohol craving reduced by 60.7% (p = 0.004) post- compared to pre-stimulation. Adverse events occurred in four out of the eight participants. Electrophysiology findings showed that among responders, there was a post-stimulation decrease (p = 0.026) in current density at the rdACC for beta 1 band (13-18 Hz). Results suggest that rdACC stimulation using implanted electrodes may potentially be a feasible method for supressing alcohol craving in individuals with severe alcohol use disorder. However, to further establish safety and efficacy, larger controlled clinical trials are needed.PURPOSE To determine the association between coronal Cobb's angle and Nash-Moe index in patients with adolescent idiopathic scoliosis. We also attempted to determine whether apical vertebral derotation depended upon the curve flexibility. OVERVIEW OF LITERATURE The three-dimensional nature of adolescent idiopathic scoliosis (AIS) is well established. Knowledge of all components of this complex deformity is essential to formulate effective treatment strategies. Though the importance of quantifying all the components of the deformity, in AIS, has been analysed in detail, very few studies have been done to ascertain the relationship between the coronal plane deformity and apical vertebral rotation. METHODS Digitalised standing and supine stretch anteroposterior (AP) radiographs of 158 patients with AIS were analysed. The standing and supine stretch AP radiographs were compared to calculate the percentage reduction of Cobb's angle to determine curve flexibility. The derotation of the apical vertebra on application of traction was also noted.