Otolaryngology - Research Publications
Permanent URI for this collection
Search Results
1 - 6 of 6
-
ItemAutomatic analysis of cochlear response using electrocochleography signals during cochlear implant surgery(PUBLIC LIBRARY SCIENCE, 2022-07-14)Cochlear implants (CIs) provide an opportunity for the hearing impaired to perceive sound through electrical stimulation of the hearing (cochlear) nerve. However, there is a high risk of losing a patient's natural hearing during CI surgery, which has been shown to reduce speech perception in noisy environments as well as music appreciation. This is a major barrier to the adoption of CIs by the hearing impaired. Electrocochleography (ECochG) has been used to detect intra-operative trauma that may lead to loss of natural hearing. There is early evidence that ECochG can enable early intervention to save natural hearing of the patient. However, detection of trauma by observing changes in the ECochG response is typically carried out by a human expert. Here, we discuss a method of automating the analysis of cochlear responses during CI surgery. We establish, using historical patient data, that the proposed method is highly accurate (∼94% and ∼95% for sensitivity and specificity respectively) when compared to a human expert. The automation of real-time cochlear response analysis is expected to improve the scalability of ECochG and improve patient safety.
-
ItemA Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images(MDPI, 2022-01)Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone can provide, integrating such information to be used in segmentation is a challenging task. Numerous methods have been introduced to solve the problem of multi-modal medical image segmentation in recent years. In this paper, we propose a solution for the task of brain tumor segmentation. To this end, we first introduce a method of enhancing an existing magnetic resonance imaging (MRI) dataset by generating synthetic computed tomography (CT) images. Then, we discuss a process of systematic optimization of a convolutional neural network (CNN) architecture that uses this enhanced dataset, in order to customize it for our task. Using publicly available datasets, we show that the proposed method outperforms similar existing methods.
-
ItemThe Melbourne Mastoidectomy Scale: Validation of an end-product dissection scale for cortical mastoidectomy(WILEY, 2020-09)Introduction Cortical mastoidectomy is a common otolaryngology procedure and represents a compulsory part of otolaryngology training. As such, a specific validated assessment score is needed for the progression of competency‐based training in this procedure. Although multiple temporal bone dissection scales have been developed, they have all been validated for advanced temporal bone dissection including posterior tympanotomy, rather than the task of cortical mastoidectomy. Methods The Melbourne Mastoidectomy Scale, a 20‐item end‐product dissection scale to assess cortical mastoidectomy, was developed. The scale was validated using dissections by 30 participants (10 novice, 10 intermediate and 10 expert) on a virtual reality temporal bone simulator. All dissections were assessed independently by three blinded graders. Additionally, all procedures were graded with an abbreviated Welling Scale by one grader. Results There was high inter‐rater reliability between the three graders (r = .9210, P < .0001). There was a significant difference in scores between the three groups (P < .0001). Additionally, there was a large effect size between all three groups: the differences between the novice group and both the intermediate group (P = .0119, η2 = 0.2482) and expert group (P < .001, η2 = 0.6356) were significant. The difference between the intermediate group and expert group again had a large effect size (η2 = 0.3217), but was not significant. The Melbourne Mastoidectomy Scale correlated well with an abbreviated Welling Scale (r = .8485, P < .0001). Conclusion The Melbourne Mastoidectomy Scale offers a validated score for use in the assessment of cortical mastoidectomy.
-
ItemA deep learning based framework for the registration of three dimensional multi-modal medical images of the head(NATURE PORTFOLIO, 2021-01-21)Image registration is a fundamental task in image analysis in which the transform that moves the coordinate system of one image to another is calculated. Registration of multi-modal medical images has important implications for clinical diagnosis, treatment planning, and image-guided surgery as it provides the means of bringing together complimentary information obtained from different image modalities. However, since different image modalities have different properties due to their different acquisition methods, it remains a challenging task to find a fast and accurate match between multi-modal images. Furthermore, due to reasons such as ethical issues and need for human expert intervention, it is difficult to collect a large database of labelled multi-modal medical images. In addition, manual input is required to determine the fixed and moving images as input to registration algorithms. In this paper, we address these issues and introduce a registration framework that (1) creates synthetic data to augment existing datasets, (2) generates ground truth data to be used in the training and testing of algorithms, (3) registers (using a combination of deep learning and conventional machine learning methods) multi-modal images in an accurate and fast manner, and (4) automatically classifies the image modality so that the process of registration can be fully automated. We validate the performance of the proposed framework on CT and MRI images of the head obtained from a publicly available registration database.
-
ItemA Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication(MDPI, 2020-06)Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.
-
ItemAutomated assessment of cortical mastoidectomy performance in virtual reality(WILEY, 2021-09)INTRODUCTION: Cortical mastoidectomy is a core skill that Otolaryngology trainees must gain competency in. Automated competency assessments have the potential to reduce assessment subjectivity and bias, as well as reducing the workload for surgical trainers. OBJECTIVES: This study aimed to develop and validate an automated competency assessment system for cortical mastoidectomy. PARTICIPANTS: Data from 60 participants (Group 1) were used to develop and validate an automated competency assessment system for cortical mastoidectomy. Data from 14 other participants (Group 2) were used to test the generalisability of the automated assessment. DESIGN: Participants drilled cortical mastoidectomies on a virtual reality temporal bone simulator. Procedures were graded by a blinded expert using the previously validated Melbourne Mastoidectomy Scale: a different expert assessed procedures by Groups 1 and 2. Using data from Group 1, simulator metrics were developed to map directly to the individual items of this scale. Metric value thresholds were calculated by comparing automated simulator metric values to expert scores. Binary scores per item were allocated using these thresholds. Validation was performed using random sub-sampling. The generalisability of the method was investigated by performing the automated assessment on mastoidectomies performed by Group 2, and correlating these with scores of a second blinded expert. RESULTS: The automated binary score compared with the expert score per item had an accuracy, sensitivity and specificity of 0.9450, 0.9547 and 0.9343, respectively, for Group 1; and 0.8614, 0.8579 and 0.8654, respectively, for Group 2. There was a strong correlation between the total scores per participant assigned by the expert and calculated by the automatic assessment method for both Group 1 (r = .9144, P < .0001) and Group 2 (r = .7224, P < .0001). CONCLUSION: This study outlines a virtual reality-based method of automated assessment of competency in cortical mastoidectomy, which proved comparable to the assessment provided by human experts.