Improving Visual Inspection Reliability in Aircraft Maintenance

Visual inspection is a fundamental safety critical task in the air transport industry. This study investigates how a visual search strategy with a specific eye scanning pattern can be used to improve the observation of aircraft defects during visual inspection tasks. N=100 aircraft maintenance technicians were recruited and N=48 were allocated to a control condition. This group conducted pre-flight visual inspections on aircraft, using their normal custom and practice. The remaining N=52 experimental group participants were trained to use a specific eye scanning pattern during their pre-flight inspection called systematic visual search. Prior to inspections, the number of observable defects on each aircraft has been ascertained by the researchers. The results demonstrated that the use of systematic visual search increased the mean number of defects observed from circa 36% to circa 56%. The experimental group were then tasked with further visual inspections using systematic visual search in order to investigate the effect of practice and feedback. This resulted in mean defect observation rates increasing to a plateau of circa 70%. The results clearly demonstrate that; by using a set eye scanning pattern as directed by the systematic visual search method, visual inspection reliability can be improved

Improving Visual Inspection Performance During Pre-Flight Visual Inspections By Aircraft Maintenance Technicians.

Visual inspection is a critical task in aircraft maintenance and is the predominant inspection technique used in the global aviation sector. For safety reasons, visual inspections by Aircraft Maintenance Technicians are routinely conducted on a daily basis during pre-flight inspections. Its significance becomes evident when considering the potential consequences of Aircraft Maintenance Technicians not seeing observable defects during visual inspections. For example, an aircraft crash landing in 1989 resulted in 111 fatalities and was attributed to a visual inspection failure. The industrial quality control literature also illustrates the varying accuracy for the observation of defects and hazards, underscoring the inherent challenges associated with visual inspection. Basically, visual inspections are error prone tasks that are difficult to do well

3D Laser-and-tissue Agnostic Data-driven Method for Robotic Laser Surgical Planning

In robotic laser surgery, shape prediction of an one-shot ablation cavity is an important problem for minimizing errant overcutting of healthy tissue during the course of pathological tissue resection and precise tumor removal. Since it is difficult to physically model the laser-tissue interaction due to the variety of optical tissue properties, complicated process of heat transfer, and uncertainty about the chemical reaction, we propose a 3D cavity prediction model based on an entirely data-driven method without any assumptions of laser settings and tissue properties. Based on the cavity prediction model, we formulate a novel robotic laser planning problem to determine the optimal laser incident configuration, which aims to create a cavity that aligns with the surface target (e.g. tumor, pathological tissue). To solve the one-shot ablation cavity prediction problem, we model the 3D geometric relation between the tissue surface and the laser energy profile as a non-linear regression problem that can be represented by a single-layer perceptron (SLP) network. The SLP network is encoded in a novel kinematic model to predict the shape of the post-ablation cavity with an arbitrary laser input. To estimate the SLP network parameters, we formulate a dataset of one-shot laser-phantom cavities reconstructed by the optical coherence tomography (OCT) B-scan images for the data-driven modelling. To verify the method. The learned cavity prediction model is applied to solve a simplified robotic laser planning problem modelled as a surface alignment error minimization problem. The initial results report (91.1 +- 3.0)% 3D-cavity-Intersection-over-Union (3D-cavity-IoU) for the 3D cavity prediction and an average of 97.9% success rate for the simulated surface alignment experiments

Towards the Development of a Tendon-Actuated Galvanometer for Endoscopic Surgical Laser Scanning

There is a need for precision pathological sensing, imaging, and tissue manipulation in neurosurgical procedures, such as brain tumor resection. Precise tumor margin identification and resection can prevent further growth and protect critical structures. Surgical lasers with small laser diameters and steering capabilities can allow for new minimally invasive procedures by traversing through complex anatomy, then providing energy to sense, visualize, and affect tissue. In this paper, we present the design of a small-scale tendon-actuated galvanometer (TAG) that can serve as an end-effector tool for a steerable surgical laser. The galvanometer sensor design, fabrication, and kinematic modeling are presented and derived. It can accurately rotate up to 30.14 degrees (or a laser reflection angle of 60.28 degrees). A kinematic mapping of input tendon stroke to output galvanometer angle change and a forward-kinematics model relating the end of the continuum joint to the laser end-point are derived and validated.Comment: 6 pages, 7 figures, conference paper at the 2024 International Symposium on Medical Robotic

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

An FBG-based Stiffness Estimation Sensor for In-vivo Diagnostics

In-vivo tissue stiffness identification can be useful in pulmonary fibrosis diagnostics and minimally invasive tumor identification, among many other applications. In this work, we propose a palpation-based method for tissue stiffness estimation that uses a sensorized beam buckled onto the surface of a tissue. Fiber Bragg Gratings (FBGs) are used in our sensor as a shape-estimation modality to get real-time beam shape, even while the device is not visually monitored. A mechanical model is developed to predict the behavior of a buckling beam and is validated using finite element analysis and bench-top testing with phantom tissue samples (made of PDMS and PA-Gel). Bench-top estimations were conducted and the results were compared with the actual stiffness values. Mean RMSE and standard deviation (from the actual stiffnesses) values of 413.86 KPa and 313.82 KPa were obtained. Estimations for softer samples were relatively closer to the actual values. Ultimately, we used the stiffness sensor within a mock concentric tube robot as a demonstration of \textit{in-vivo} sensor feasibility. Bench-top trials with and without the robot demonstrate the effectiveness of this unique sensing modality in \textit{in-vivo} applications.Comment: 6 pages (excluding the references), 5 figure

Computer Vision for Increased Operative Efficiency via Identification of Instruments in the Neurosurgical Operating Room: A Proof-of-Concept Study

Objectives Computer vision (CV) is a field of artificial intelligence that enables machines to interpret and understand images and videos. CV has the potential to be of assistance in the operating room (OR) to track surgical instruments. We built a CV algorithm for identifying surgical instruments in the neurosurgical operating room as a potential solution for surgical instrument tracking and management to decrease surgical waste and opening of unnecessary tools. Methods We collected 1660 images of 27 commonly used neurosurgical instruments. Images were labeled using the VGG Image Annotator and split into 80% training and 20% testing sets in order to train a U-Net Convolutional Neural Network using 5-fold cross validation. Results Our U-Net achieved a tool identification accuracy of 80-100% when distinguishing 25 classes of instruments, with 19/25 classes having accuracy over 90%. The model performance was not adequate for sub classifying Adson, Gerald, and Debakey forceps, which had accuracies of 60-80%. Conclusions We demonstrated the viability of using machine learning to accurately identify surgical instruments. Instrument identification could help optimize surgical tray packing, decrease tool usage and waste, decrease incidence of instrument misplacement events, and assist in timing of routine instrument maintenance. More training data will be needed to increase accuracy across all surgical instruments that would appear in a neurosurgical operating room. Such technology has the potential to be used as a method to be used for proving what tools are truly needed in each type of operation allowing surgeons across the world to do more with less.Comment: Data is openly available through The Open Science Framework: https://doi.org/10.17605/OSF.IO/BCQK

Concentric Tube Robot Design and Optimization Based on Task and Anatomical Constraints

Concentric tube robots are catheter-sized continuum robots that are well suited for minimally invasive surgery inside confined body cavities. These robots are constructed from sets of pre-curved superelastic tubes and are capable of assuming complex 3D curves. The family of 3D curves that the robot can assume depends on the number, curvatures, lengths and stiffnesses of the tubes in its tube set. The robot design problem involves solving for a tube set that will produce the family of curves necessary to perform a surgical procedure. At a minimum, these curves must enable the robot to smoothly extend into the body and to manipulate tools over the desired surgical workspace while respecting anatomical constraints. This paper introduces an optimization framework that utilizes procedureor patient-specific image-based anatomical models along with surgical workspace requirements to generate robot tube set designs. The algorithm searches for designs that minimize robot length and curvature and for which all paths required for the procedure consist of stable robot configurations. Two mechanics-based kinematic models are used. Initial designs are sought using a model assuming torsional rigidity. These designs are then refined using a torsionally-compliant model. The approach is illustrated with clinically relevant examples from neurosurgery and intracardiac surgery