2,756 research outputs found
Evaluating Human Performance for Image-Guided Surgical Tasks
The following work focuses on the objective evaluation of human performance for two different interventional tasks; targeted prostate biopsy tasks using a tracked biopsy device, and external ventricular drain placement tasks using a mobile-based augmented reality device for visualization and guidance. In both tasks, a human performance methodology was utilized which respects the trade-off between speed and accuracy for users conducting a series of targeting tasks using each device. This work outlines the development and application of performance evaluation methods using these devices, as well as details regarding the implementation of the mobile AR application. It was determined that the Fittsā Law methodology can be applied for evaluation of tasks performed in each surgical scenario, and was sensitive to differentiate performance across a range which spanned experienced and novice users. This methodology is valuable for future development of training modules for these and other medical devices, and can provide details about the underlying characteristics of the devices, and how they can be optimized with respect to human performance
Automated assessment of echocardiographic image quality using deep convolutional neural networks
Myocardial ischemia tops the list of causes of death around the globe, but its diagnosis and early detection thrives on clinical echocardiography. Although echocardiography presents a huge advantage of a non-intrusive, low-cost point of care diagnosis, its image quality is inherently subjective with strong dependence on operatorsā experience level and acquisition skill. In some countries, echo specialists are mandated to supplementary years of training to achieve āgold standardā free-hand acquisition skill without which exacerbates the reliability of echocardiogram and increases possibility for misdiagnosis. These drawbacks pose significant challenges to adopting echocardiography as authoritative modalities for cardiac diagnosis. However, the prevailing and currently adopted solution is to manually carry out quality evaluation where an echocardiography specialist visually inspects several acquired images to make clinical decisions of its perceived quality and prognosis. This is a lengthening process and laced with variability of opinion consequently affection diagnostic responses. The goal of the research is to provide a multi-discipline, state-of-the-art solution that allows objective quality assessment of echocardiogram and to guarantee the reliability of clinical quantification processes. Computer graphic processing unit simulations, medical imaging analysis and deep convolutional neural network models were employed to achieve this goal. From a finite pool of echocardiographic patient datasets, 1650 random samples of echocardiogram cine-loops from different patients with age ranges from 17 and 85 years, who had undergone echocardiography between 2010 and 2020 were evaluated. We defined a set of pathological and anatomical criteria of image quality by which apical-four and parasternal long axis frames can be evaluated with feasibility for real-time optimization. The selected samples were annotated for multivariate model developments and validation of predicted quality score per frame. The outcome presents a robust artificial intelligence algorithm that indicate framesā quality rating, real-time visualisation of element of quality and updates quality optimization in real-time. A prediction errors of 0.052, 0.062, 0.069, 0.056 for visibility, clarity, depth-gain, and foreshortening attributes were achieved, respectively. The model achieved a combined error rate of 3.6% with average prediction speed of 4.24 ms per frame. The novel method established a superior approach to two-dimensional image quality estimation, assessment, and clinical adequacy on acquisition of echocardiogram prior to quantification and diagnosis of myocardial infarction
Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives
Ultrasound (US) is one of the most widely used modalities for clinical
intervention and diagnosis due to the merits of providing non-invasive,
radiation-free, and real-time images. However, free-hand US examinations are
highly operator-dependent. Robotic US System (RUSS) aims at overcoming this
shortcoming by offering reproducibility, while also aiming at improving
dexterity, and intelligent anatomy and disease-aware imaging. In addition to
enhancing diagnostic outcomes, RUSS also holds the potential to provide medical
interventions for populations suffering from the shortage of experienced
sonographers. In this paper, we categorize RUSS as teleoperated or autonomous.
Regarding teleoperated RUSS, we summarize their technical developments, and
clinical evaluations, respectively. This survey then focuses on the review of
recent work on autonomous robotic US imaging. We demonstrate that machine
learning and artificial intelligence present the key techniques, which enable
intelligent patient and process-specific, motion and deformation-aware robotic
image acquisition. We also show that the research on artificial intelligence
for autonomous RUSS has directed the research community toward understanding
and modeling expert sonographers' semantic reasoning and action. Here, we call
this process, the recovery of the "language of sonography". This side result of
research on autonomous robotic US acquisitions could be considered as valuable
and essential as the progress made in the robotic US examination itself. This
article will provide both engineers and clinicians with a comprehensive
understanding of RUSS by surveying underlying techniques.Comment: Accepted by Medical Image Analysi
A Survey on the Current Status and Future Challenges Towards Objective Skills Assessment in Endovascular Surgery
Minimally-invasive endovascular interventions have evolved rapidly over the past decade, facilitated by breakthroughs in medical
imaging and sensing, instrumentation and most recently robotics. Catheter based operations are potentially safer and applicable to
a wider patient population due to the reduced comorbidity. As a result endovascular surgery has become the preferred treatment
option for conditions previously treated with open surgery and as such the number of patients undergoing endovascular interventions
is increasing every year. This fact coupled with a proclivity for reduced working hours, results in a requirement for efficient training
and assessment of new surgeons, that deviates from the āsee one, do one, teach oneā model introduced by William Halsted, so
that trainees obtain operational expertise in a shorter period. Developing more objective assessment tools based on quantitative
metrics is now a recognised need in interventional training and this manuscript reports the current literature for endovascular skills
assessment and the associated emerging technologies. A systematic search was performed on PubMed (MEDLINE), Google Scholar,
IEEXplore and known journals using the keywords, āendovascular surgeryā, āsurgical skillsā, āendovascular skillsā, āsurgical training
endovascularā and ācatheter skillsā. Focusing explicitly on endovascular surgical skills, we group related works into three categories
based on the metrics used; structured scales and checklists, simulation-based and motion-based metrics. This review highlights the
key findings in each category and also provides suggestions for new research opportunities towards fully objective and automated
surgical assessment solutions
A computer vision based ultrasound operator skill evaluation
The aim of this thesis is to research inexpensive and automatic methods for analysing sonogra-
phers skill level, which reduces cost and improves objectivity. The current approach of teaching
physicians to generate good quality ultrasound images is expensive and subjective, also takes
significant time and resources, because it requires experienced instructors to guide and assess
trainees in person. In this thesis, a distributed data collection system for synchronising and
collecting data from multiple different sensors, including Microsoft Kinect 2 and ultrasound
machine, was designed. Then hand movements are extracted from ultrasound images with an
intensity-based image registration algorithm. The extracted movements data are analysed to
find different patterns between novice and expert sonographers. A multi-sensor fusion algorithm
is used in this thesis to extend the field of view of Microsoft Kinect 2, as well as overcome the
cluttered environments and obstacles in clinics. Hand tracking is performed in the registered
large point clouds with a semi-automatic colour-based segmentation algorithm
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