Otolaryngology - Research Publications
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ItemStreet Sign Recognition Using Histogram of Oriented Gradients and Artificial Neural Networks(MDPI, 2019-04)Street sign identification is an important problem in applications such as autonomous vehicle navigation and aids for individuals with vision impairments. It can be especially useful in instances where navigation techniques such as global positioning system (GPS) are not available. In this paper, we present a method of detection and interpretation of Malaysian street signs using image processing and machine learning techniques. First, we eliminate the background from an image to segment the region of interest (i.e., the street sign). Then, we extract the text from the segmented image and classify it. Finally, we present the identified text to the user as a voice notification. We also show through experimental results that the system performs well in real-time with a high level of accuracy. To this end, we use a database of Malaysian street sign images captured through an on-board camera.
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ItemRegion-Specific Automated Feedback in Temporal Bone Surgery Simulation(IEEE, 2015)The use of virtual reality simulators for surgical training has gained popularity in recent years, with an ever increasing body of evidence supporting the benefits and validity of simulation-based training. However, a crucial component of effective skill acquisition has not been adequately addressed, namely the provision of timely performance feedback. The utility of a surgical simulator is limited if it still requires the presence of experts to guide trainees. Automated feedback that emulates the advise provided by experts is necessary to facilitate independent learning. We propose an automated system that provides region-specific feedback on surgical technique within a temporal bone surgery simulator. The design of this system allows easy transfer of feedback models to multiple temporal bone specimens in the simulator. The system was validated by an expert otologist and was found to provide highly accurate and timely feedback.
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ItemPresentation of automated procedural guidance in surgical simulation: results of two randomised controlled trials(Cambridge University Press, 2018-03)OBJECTIVE: To investigate the effectiveness and usability of automated procedural guidance during virtual temporal bone surgery. METHODS: Two randomised controlled trials were performed to evaluate the effectiveness, for medical students, of two presentation modalities of automated real-time procedural guidance in virtual reality simulation: full and step-by-step visual presentation of drillable areas. Presentation modality effectiveness was determined through a comparison of participants' dissection quality, evaluated by a blinded otologist, using a validated assessment scale. RESULTS: While the provision of automated guidance on procedure improved performance (full presentation, p = 0.03; step-by-step presentation, p < 0.001), usage of the two different presentation modalities was vastly different (full presentation, 3.73 per cent; step-by-step presentation, 60.40 per cent). CONCLUSION: Automated procedural guidance in virtual temporal bone surgery is effective in improving trainee performance. Step-by-step presentation of procedural guidance was engaging, and therefore more likely to be used by the participants.
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ItemA rotation and translation invariant method for 3D organ image classification using deep convolutional neural networks(PEERJ INC, 2019-03-04)Three-dimensional (3D) medical image classification is useful in applications such as disease diagnosis and content-based medical image retrieval. It is a challenging task due to several reasons. First, image intensity values are vastly different depending on the image modality. Second, intensity values within the same image modality may vary depending on the imaging machine and artifacts may also be introduced in the imaging process. Third, processing 3D data requires high computational power. In recent years, significant research has been conducted in the field of 3D medical image classification. However, most of these make assumptions about patient orientation and imaging direction to simplify the problem and/or work with the full 3D images. As such, they perform poorly when these assumptions are not met. In this paper, we propose a method of classification for 3D organ images that is rotation and translation invariant. To this end, we extract a representative two-dimensional (2D) slice along the plane of best symmetry from the 3D image. We then use this slice to represent the 3D image and use a 20-layer deep convolutional neural network (DCNN) to perform the classification task. We show experimentally, using multi-modal data, that our method is comparable to existing methods when the assumptions of patient orientation and viewing direction are met. Notably, it shows similarly high accuracy even when these assumptions are violated, where other methods fail. We also explore how this method can be used with other DCNN models as well as conventional classification approaches.
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ItemThe Construct Validity and Reliability of an Assessment Tool for Competency in Cochlear Implant Surgery(HINDAWI LTD, 2014)INTRODUCTION: We introduce a rating tool that objectively evaluates the skills of surgical trainees performing cochlear implant surgery. METHODS: Seven residents and seven experts performed cochlear implant surgery sessions from mastoidectomy to cochleostomy on a standardized virtual reality temporal bone. A total of twenty-eight assessment videos were recorded and two consultant otolaryngologists evaluated the performance of each participant using these videos. RESULTS: Interrater reliability was calculated using the intraclass correlation coefficient for both the global and checklist components of the assessment instrument. The overall agreement was high. The construct validity of this instrument was strongly supported by the significantly higher scores in the expert group for both components. CONCLUSION: Our results indicate that the proposed assessment tool for cochlear implant surgery is reliable, accurate, and easy to use. This instrument can thus be used to provide objective feedback on overall and task-specific competency in cochlear implantation.
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ItemDo experts practice what they profess?(PUBLIC LIBRARY SCIENCE, 2018-01-05)We investigated the variation of drilled regions of expert and trainee surgeons performing virtual temporal bone surgery to identify their compliance with standard drilling procedures. To this end, we recruited seven expert and six trainee ENT surgeons, who were asked to perform the surgical preparations for cochlear implantation on a virtual temporal bone. The temporal bone was divided into six regions using a semi-automated approach. The drilled area in each region was compared between groups using a sign test. Similarity within groups was calculated as a ratio of voxels (3D points) drilled by at least 75% of surgeons and at least 25% of surgeons. We observed a significant difference between groups when performing critical tasks such as exposing the facial nerve, opening the facial recess, and finding the round window. In these regions, experts' practice is more similar to each other than that between trainees. Consistent with models of skills development, expertise and expert-performance, the outcome of the analysis shows that experts perform similarly in critical parts of the procedure, and do indeed practice what they profess.