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Robot-assisted endobronchial intervention requires accurate localisation based on both intra- and pre-operative data. Most existing methods achieve this by registering 2D videos with 3D CT models according to a defined similarity metric with local features. Instead, we formulate the bronchoscopic localisation as a learning-based global localisation using deep neural networks. The proposed network consists of two generative architectures and one auxiliary learning component. The cycle generative architecture bridges the domain variance between the real bronchoscopic videos and virtual views derived from pre-operative CT data so that the proposed approach can be trained through a large number of generated virtual images but deployed through real images. The auxiliary learning architecture leverages complementary relative pose regression to constrain the search space, ensuring consistent global pose predictions. Most importantly, the uncertainty of each global pose is obtained through variational inference by sampling within the learned underlying probability distribution. Detailed validation results demonstrate the localisation accuracy with reasonable uncertainty achieved and its potential clinical value.

Acquisition of bimanual motor skills, critical in several applications ranging from robotic teleoperations to surgery, is associated with a protracted learning curve. Brain connectivity based on functional Near Infrared Spectroscopy (fNIRS) data has shown promising results in distinguishing experts from novice surgeons. However, it is less well understood how expertise-related disparity in brain connectivity is modulated by dynamic temporal demands experienced during a surgical task. In this study, we use fNIRS to examine the interplay between frontal and motor brain regions in a cohort of surgical residents of varying expertise performing a laparoscopic surgical task under temporal demand. The results demonstrate that prefrontal-motor connectivity in senior residents is more resilient to time pressure. Furthermore, certain global characteristics of brain connectomes, such as the small-world index, may be used to detect the presence of an underlying stressor.

Nature is a great source of inspiration for the development of solutions for biomedical problems. We present a novel biosensor design utilizing two-photon polymerisation and graphene to fabricate an enhanced biosensing platform for the detection of motile bacteria. A cage comprising venous valve-inspired directional micro-structure is fabricated around graphene-based sensing electronics. The asymmetric 3D micro-structure promotes motile cells to swim from outside the cage towards the inner-most chamber, resulting in concentrated bacteria surrounding the central sensing region, thus enhancing the sensing signal. The concentrating effect is proved across a range of cell cultures - from 101 CFU/ml to 109 CFU/ml. Fluorescence analysis shows a 3.38–3.5 times enhanced signal. pH sensor presents a 2.14–3.08 times enhancement via the detection of cellar metabolite. Electrical measurements demonstrate an 8.8–26.7 times enhanced current. The proposed platform provides a new way of leveraging bio-inspired 3D printing and 2D materials for the development of sensing devices for biomedical applications.

The current standard of intra-operative navigation during Fenestrated Endovascular Aortic Repair (FEVAR) calls for need of 3D alignments between inserted devices and aortic branches. The navigation commonly via 2D fluoroscopic images, lacks anatomical information, resulting in longer operation hours and radiation exposure. In this paper, a framework for real-time 3D robotic path planning from a single 2D fluoroscopic image of Abdominal Aortic Aneurysm (AAA) is introduced. A graph matching method is proposed to establish the correspondence between the 3D preoperative and 2D intra-operative AAA skeletons, and then the two skeletons are registered by skeleton deformation and regularization in respect to skeleton length and smoothness. Furthermore, deep learning was used to segment 3D pre-operative AAA from Computed Tomography (CT) scans to facilitate the framework automation. Simulation, phantom and patient AAA data sets have been used to validate the proposed framework. 3D distance error of 2mm was achieved in the phantom setup. Performance advantages were also achieved in terms of accuracy, robustness and time-efficiency. All the code will be open source.

Pre-operative Abdominal Aortic Aneurysm (AAA) 3D shape is critical for customized stent-graft design in Fenestrated Endovascular Aortic Repair (FEVAR). Traditional segmentation approaches implement expert-designed feature extractors while recent deep neural networks extract features automatically with multiple non-linear modules. Usually, a large training dataset is essential for applying deep learning on AAA segmentation. In this paper, the AAA was segmented using U-net with a small number (two) of training subjects. Firstly, Computed Tomography Angiography (CTA) slices were augmented with gray value variation and translation to avoid the overfitting caused by the small number of training subjects. Then, U-net was trained to segment the AAA. Dice Similarity Coefficients (DSCs) over 0.8 were achieved on the testing subjects. The PLZ, DLZ and aortic branches are all reconstructed reasonably, which will facilitate stent graft customization and help shape instantiation for intra-operative surgery navigation in FEVAR.

This paper presents the use of tubing to store clinical microdialysis samples for delayed analysis with high temporal resolution, offering an alternative to traditional discrete offline microdialysis sampling. Samples stored in this way were found to be stable for up to 72 days at −80 °C. Examples of how this methodology can be applied to glucose and lactate measurement in a wide range of in vivo monitoring experiments are presented. This paper presents a general model, which allows for an informed choice of tubing parameters for a given storage time and flow rate avoiding high back pressure, which would otherwise cause the microdialysis probe to leak, while maximising temporal resolution.

Mental health disorders are rank ed among the top twenty main causes of disability worldwide. It was found that there is an intriguing relationship between mental health problems and financial difficulties. Current technology uses mobile apps for self - monitoring of mental health conditions with a potential to avoid debt crisis caused by mental illness. In this paper, we propose the use of a wearable sensor as an objective evaluation tool for monitoring the e motional well - being of the subject. By fusing the sensory data with financial data, an intelligent self - guard system is proposed for preventing excessive spending caused by mental condition.

The diagnosis of CSF hypovolemia remains controversial. The primary diagnostic factor relies on confirmation of leakage of the CSF based on reduced spinal fluid pressure. Determining the specific leakage site is the most important issue for effective treatment but remains a difficult task. Although CT myelography, radioisotope cisternography, and MRI are commonly performed in the diagnosis of CSF hypovolemia, these techniques can rarely identify the precise leakage site. Therefore, an epidural blood patch is performed in the lumbar spine in many cases. This study reports a new diagnostic modality that can help to confirm the leakage site. Fat-suppressed T2-weighted sagittal images were compared before and after the infusion of 20 ml of saline into the subarachnoid space of the lumbar region to detect the specific leakage site with high probability. Three patients were successfully treated by the epidural blood patch based on data obtained with the new diagnostic modality. Two patients were treated in the cervical region and 1 in the lumbar region. The use of fat-suppressed T2-weighted sagittal images after saline infusion could be a relevant diagnostic modality compared with images obtained by CT myelography, radioisotope cisternography, and ordinary MRI to achieve accurate diagnosis and effective treatment of patients with CSF hypovolemia.

Background and Aim There are currently limited training and assessment tools available to novice endoscopists. A potential tool for the objective assessment of endoscopist visual search strategy is eye‐tracking technology. The aim of this study is to assess whether eye‐tracking technology can be used to differentiate the visual gaze patterns (VGP) of experienced and novice endoscopists, and to characterize any differences arising between the two groups. Method With the use of portable eye‐tracking glasses, VGP of novice (n = 20) and experienced (n = 14) endoscopists were compared while viewing a colonoscopy withdrawal through the hepatic flexure. Analysis was performed by comparing the central versus peripheral distribution and the horizontal distribution of novice and experienced endoscopist fixations, along with comparison of basic eye‐tracking metrics. Results This study found that experienced endoscopists had a significantly higher percentage of fixations within the periphery of the screen (13.4% vs 23.0%, P = 0.013). Experienced endoscopists also had a significantly greater percentage of fixations on the left side of the screen (18.6% vs 33.5%, P = 0.005) that displayed the poorly visualized “inside bend” of the hepatic flexure. Conclusion This study has detailed specific VGP acquired through expertise, which can potentially explain why adenomas are regularly missed at the hepatic flexure during colonoscopy. These may be useful for the training of novice colonoscopists, and further validation may utilize VGP in the development of an objective proficiency based curriculum to improve the detection of pathology and overall quality in endoscopy.

Understanding the solid biomechanics of the human body is important to the study of structure and function of the body, which can have a range of applications in health care, sport, well-being, and workflow analysis. Conventional laboratory-based biomechanical analysis systems and observation-based tests are designed only to capture brief snapshots of the mechanics of movement. With recent developments in wearable sensing technologies, biomechanical analysis can be conducted in less-constrained environments, thus allowing continuous monitoring and analysis beyond laboratory settings. In this paper, we review the current research in wearable sensing technologies for biomechanical analysis, focusing on sensing and analytics that enable continuous, long-term monitoring of kinematics and kinetics in a free-living environment. The main technical challenges, including measurement drift, external interferences, nonlinear sensor properties, sensor placement, and muscle variations, that can affect the accuracy and robustness of existing methods and different methods for reducing the impact of these sources of errors are described in this paper. Recent developments in motion estimation in kinematics, mobile force sensing in kinematics, sensor reduction for electromyography, and the future direction of sensing for biomechanics are also discussed.

Attitude estimation from miniature inertial and magnetic sensors has been used in a wide variety of applications, ranging from virtual reality, underwater vehicles, handheld navigation devices, to biomotion analysis. However, appropriate sensor calibrations for accurate sensor measurements are essential to the performance of attitude estimation algorithms. In this paper, we present a robust sensor calibration method for accurate attitude estimation from three-axis accelerometers, gyroscopes, and magnetometer measurements. The proposed calibration method only requires a simple pan-tilt unit. A unified sensor model for inertial and magnetic sensors is used to convert the sensor readings to physical quantities in metric units. Based on the sensor model, a cost function is constructed, and a two-step iterative algorithm is then proposed to calibrate the inertial sensors. Due to the difficulties of acquiring the ground-truth of the Earth magnetic field, a simplified pseudomagnetometer calibration method is also presented based on an ellipsoid fitting algorithm. The calibration method is then applied to our sensor nodes, and the good performance of the orientation estimation has illustrated the effectiveness of the proposed sensor calibration method.