Association between Estimated Cardiorespiratory Fitness and Abnormal Glucose Risk: A Cohort Study.

BACKGROUND: Cardiorespiratory fitness (CRF) is a predictor of chronic disease that is impractical to routinely measure in primary care settings. We used a new estimated cardiorespiratory fitness (eCRF) algorithm that uses information routinely documented in electronic health care records to predict abnormal blood glucose incidence. METHODS: Participants were adults (17.8% female) 20-81 years old at baseline from the Aerobics Center Longitudinal Study between 1979 and 2006. eCRF was based on sex, age, body mass index, resting heart rate, resting blood pressure, and smoking status. CRF was measured by maximal treadmill testing. Cox proportional hazards regression models were established using eCRF and CRF as independent variables predicting the abnormal blood glucose incidence while adjusting for covariates (age, sex, exam year, waist girth, heavy drinking, smoking, and family history of diabetes mellitus and lipids). RESULTS: Of 8602 participants at risk at baseline, 3580 (41.6%) developed abnormal blood glucose during an average of 4.9 years follow-up. The average eCRF of 12.03 ± 1.75 METs was equivalent to the CRF of 12.15 ± 2.40 METs within the 10% equivalence limit. In fully adjusted models, the estimated risks were the same (HRs = 0.96), eCRF (95% CIs = 0.93-0.99), and CRF (95% CI of 0.94-0.98). Each 1-MET increase was associated with a 4% reduced risk. CONCLUSIONS: Higher eCRF is associated with a lower risk of abnormal glucose. eCRF can be a vital sign used for research and prevention.

Estimating Cardiorespiratory Fitness Without Exercise Testing or Physical Activity Status in Healthy Adults: Regression Model Development and Validation.

BACKGROUND: Low cardiorespiratory fitness (CRF) is an independent predictor of morbidity and mortality. Most health care settings use some type of electronic health record (EHR) system. However, many EHRs do not have CRF or physical activity data collected, thereby limiting the types of investigations and analyses that can be done. OBJECTIVE: This study aims to develop a nonexercise equation to estimate and classify CRF (in metabolic equivalent tasks) using variables commonly available in EHRs. METHODS: Participants were 42,676 healthy adults (female participants: n=9146, 21.4%) from the Aerobics Center Longitudinal Study examined from 1974 to 2005. The nonexercise estimated CRF was based on sex, age, measured BMI, measured resting heart rate, measured resting blood pressure, and smoking status. A maximal treadmill test measured CRF. RESULTS: After conducting nonlinear feature augmentation, separate linear regression models were used for male and female participants to calculate correlation and regression coefficients. Cross-classification of actual and estimated CRF was performed using low CRF categories (lowest quintile, lowest quartile, and lowest tertile). The multiple correlation coefficient (R) was 0.70 (mean deviation 1.33) for male participants and 0.65 (mean deviation 1.23) for female participants. The models explained 48.4% (SE estimate 1.70) and 41.9% (SE estimate 1.56) of the variance in CRF for male and female participants, respectively. Correct category classification for low CRF (lowest tertile) was found in 77.2% (n=25,885) of male participants and 74.9% (n=6,850) of female participants. CONCLUSIONS: The regression models developed in this study provided useful estimation and classification of CRF in a large population of male and female participants. The models may provide a practical method for estimating CRF derived from EHRs for population health research.

A structured light laser probe for gastrointestinal polyp size measurement: a preliminary comparative study.

BACKGROUND AND STUDY AIMS: Polyp size measurement is an important diagnostic step during gastrointestinal endoscopy, and is mainly performed by visual inspection. However, lack of depth perception and objective reference points are acknowledged factors contributing to measurement errors in polyp size. In this paper, we describe the proof-of-concept of a polyp measurement device based on structured light technology for future endoscopes. PATIENTS AND METHODS: Measurement accuracy, time, user confidence, and satisfaction were evaluated for polyp size assessment by (a) visual inspection, (b) open biopsy forceps of known size, (c) ruled snare, and (d) structured light probe, for a total of 392 independent polyp measurements in ex vivo porcine stomachs. RESULTS: Visual assessment resulted in a median estimation error of 2.2 mm, IQR = 2.6 mm. The proposed probe can reduce the error to 1.5 mm, IQR = 1.67 mm ( P  = 0.002, 95 %CI) and its performance was found to be statistically similar to using forceps for reference ( P  = 0.81, 95 %CI) or ruled snare ( P  = 0.99, 95 %CI), while not occluding the tool channel. Timing performance with the probe was measured to be on average 54.75 seconds per polyp. This was significantly slower than visual assessment (20.7 seconds per polyp, P  = 0.005, 95 %CI) but not significantly different from using a snare (68.5 seconds per polyp, P  = 0.73, 95 %CI). However, the probe's timing performance was partly due to lens cleaning problems in our preliminary design. Reported average satisfaction on a 0 - 10 range was highest for the proposed probe (7.92), visual assessment (7.01), and reference forceps (7.82), while significantly lower for snare users with a score of 4.42 ( P  = 0.035, 95 %CI). CONCLUSIONS: The common practice of visual assessment of polyp size was found to be significantly less accurate than tool-based assessment, but easy to carry out. The proposed technology offers an accuracy on par with using a reference tool or ruled snare with the same satisfaction levels of visual assessment and without occluding the tool channel. Further study will improve the design to reduce the operating time by integrating the probe within the scope tip.

Deep monocular 3D reconstruction for assisted navigation in bronchoscopy.

PURPOSE: In bronchoschopy, computer vision systems for navigation assistance are an attractive low-cost solution to guide the endoscopist to target peripheral lesions for biopsy and histological analysis. We propose a decoupled deep learning architecture that projects input frames onto the domain of CT renderings, thus allowing offline training from patient-specific CT data. METHODS: A fully convolutional network architecture is implemented on GPU and tested on a phantom dataset involving 32 video sequences and [Formula: see text]60k frames with aligned ground truth and renderings, which is made available as the first public dataset for bronchoscopy navigation. RESULTS: An average estimated depth accuracy of 1.5 mm was obtained, outperforming conventional direct depth estimation from input frames by 60%, and with a computational time of [Formula: see text]30 ms on modern GPUs. Qualitatively, the estimated depth and renderings closely resemble the ground truth. CONCLUSIONS: The proposed method shows a novel architecture to perform real-time monocular depth estimation without losing patient specificity in bronchoscopy. Future work will include integration within SLAM systems and collection of in vivo datasets.

Identification of Transform Coding Chains.

Transform coding is routinely used for lossy compression of discrete sources with memory. The input signal is divided into N-dimensional vectors, which are transformed by means of a linear mapping. Then, transform coefficients are quantized and entropy coded. In this paper, we consider the problem of identifying the transform matrix as well as the quantization step sizes. First, we study the case in which the only available information is a set of P transform decoded vectors. We formulate the problem in terms of finding the lattice with the largest determinant that contains all observed vectors. We propose an algorithm that is able to find the optimal solution and we formally study its convergence properties. Three potential realms of application are considered as example scenarios for the proposed theory: 1) parameter retrieval in the presence of a chain of two transform coders; 2) image tampering identification; and 3) parameter estimation for predictive coders. We show that, despite their differences, all three scenarios can be tackled by applying the same fundamental methodology. Experiments on both the synthetic data and the real images validate the proposed approach.

3D endoscope system using DOE projector.

For effective in situ endoscopic diagnosis and treatment, size measurement and shape characterization of lesions, such as tumors, is important. For this purpose, in the past we have developed a range of 3D endoscopic systems based on active stereo to measure the shape and size of living tissues. In those works, the main shortcoming was that the target area could only be reconstructed at a specific distance from the scope because of off-focus blurring effects and aberrations in the periphery of the field of view. These issues were compounded by the degree of reconstruction instability due to the strong subsurface scattering common in internal tissue. In this paper, we tackle these shortcomings by developing a new micro pattern laser projector to be inserted in the scope tool channel. The new projector uses a Diffractive Optical Element (DOE) instead of a single lens, which solves the off-focus blur. We also propose a new line-based grid pattern with gap coding to counter the subsurface scattering effect. In our experiments on ex vivo human tumor samples, we show that the tissue shapes were successfully reconstructed regardless of depth variance and strong subsurface scattering effects.

2-DOF auto-calibration for a 3D endoscope system based on active stereo.

For endoscopic medical treatment, measuring the size and shape of lesions, such as tumors, is important. We are developing a 3D endoscope system to measure the shape and size of living tissues based on active stereo. In previous works, our group attached a pattern projector outside the endoscope head. Since this increased the diameter of the endoscope, the burden and the risks of the patients would increase. In this paper, we set the pattern projector inside the instrument channel of the endoscope instead of mounting it outside, so that it can be deployed whenever required. This does not increase the size of the endoscope and facilitates the measuring process. However, since the projector is not physically fixed to the endoscope anymore prior to the operation, we propose an "auto-calibration" technique where extrinsic parameters are calibrated intra-operatively from a point marker on the projector observed simultaneously on the target surface. In the experiment, we show that the external parameters were successfully calibrated to obtain 3D reconstructions properly with the overall systems. The accuracy of the auto-calibration was validated by confirming that the epipolar constraints were kept, and a 3D reconstruction of a human tissue was demonstrated.

Probabilistic tracking of affine-invariant anisotropic regions.

Despite a wide range of feature detectors developed in the computer vision community over the years, direct application of these techniques to surgical navigation has shown significant difficulties due to the paucity of reliable salient features coupled with free--form tissue deformation and changing visual appearance of surgical scenes. The aim of this paper is to propose a novel probabilistic framework to track affine-invariant anisotropic regions under contrastingly different visual appearances during Minimally Invasive Surgery (MIS). The theoretical background of the affine-invariant anisotropic feature detector is presented and a real-time implementation exploiting the computational power of the GPU is proposed. An Extended Kalman Filter (EKF) parameterization scheme is used to adaptively adjust the optimal templates of the detected regions, enabling accurate identification and matching of the tracked features. For effective tracking verification, spatial context and region similarity have also been incorporated. They are used to boost the prediction of the EKF and recover potential tracking failure due to drift or false positives. The proposed framework is compared to the existing methods and their respective performance is evaluated with in vivo video sequences recorded from robotic-assisted MIS procedures, as well as real-world scenes.

A hand-held instrument to maintain steady tissue contact during probe-based confocal laser endomicroscopy.

Probe-based confocal laser endomicroscopy (pCLE) provides high-resolution in vivo imaging for intraoperative tissue characterization. Maintaining a desired contact force between target tissue and the pCLE probe is important for image consistency, allowing large area surveillance to be performed. A hand-held instrument that can provide a predetermined contact force to obtain consistent images has been developed. The main components of the instrument include a linear voice coil actuator, a donut load-cell, and a pCLE probe. In this paper, detailed mechanical design of the instrument is presented and system level modeling of closed-loop force control of the actuator is provided. The performance of the instrument has been evaluated in bench tests as well as in hand-held experiments. Results demonstrate that the instrument ensures a consistent predetermined contact force between pCLE probe tip and tissue. Furthermore, it compensates for both simulated physiological movement of the tissue and involuntary movements of the operator's hand. Using pCLE video feature tracking of large colonic crypts within the mucosal surface, the steadiness of the tissue images obtained using the instrument force control is demonstrated by confirming minimal crypt translation.

Dynamic guidance for robotic surgery using image-constrained biomechanical models.

The use of physically-based models combined with image constraints for intraoperative guidance is important for surgical procedures that involve large-scale tissue deformation. A biomechanical model of tissue deformation is described in which surface positional constraints and internally generated forces are derived from endoscopic images and preoperative 4D CT data, respectively. Considering cardiac motion, a novel technique is presented which minimises the average registration error over one or more complete cycles. Features tracked in the stereo video stream provide surface constraints, and an inverse finite element simulation is presented which allows internal forces to be recovered from known preoperative displacements. The accuracy of surface texture, segmented mesh and volumetrically rendered overlays is evaluated with detailed phantom experiments. Results indicate that by combining preoperative and intraoperative images in this manner, accurate intraoperative tissue deformation modelling can be achieved.

Tracking of irregular graphical structures for tissue deformation recovery in minimally invasive surgery.

Tissue deformation tracking is an important topic of minimally invasive surgery with applications ranging from intra-operative guidance to augmented reality visualisation. In this paper, we present a technique for visual tracking of irregular structures with an arbitrary degree of connectivity in space. The variational formulation of the proposed method ensures that correlation is maximised between tracked points and their computed new positions while the overall structure shape variation is minimised, thus maintaining spatial coherence of the tracked structure. The proposed method is applied to surgical annotation and tracking in 3D for telementoring and path-planning. The results are validated both on a CT-scanned phantom model and in vivo, showing an average alignment error of 1.79 mm (+/- 0.72 mm).

From medical images to minimally invasive intervention: Computer assistance for robotic surgery.

Minimally invasive surgery has been established as an important way forward in surgery for reducing patient trauma and hospitalization with improved prognosis. The introduction of robotic assistance enhances the manual dexterity and accuracy of instrument manipulation. Further development of the field in using pre- and intra-operative imaging guidance requires the integration of the general anatomy of the patient with clear pathologic indications and geometrical information for preoperative planning and intra-operative manipulation. It also requires effective visualization and the recreation of haptic and tactile sensing with dynamic active constraints to improve consistency and safety of the surgical procedures. This paper describes key technical considerations of tissue deformation tracking, 3D reconstruction, subject-specific modeling, image guidance and augmented reality for robotic assisted minimally invasive surgery. It highlights the importance of adapting preoperative surgical planning according to intra-operative data and illustrates how dynamic information such as tissue deformation can be incorporated into the surgical navigation framework. Some of the recent trends are discussed in terms of instrument design and the usage of dynamic active constraints and human-robot perceptual docking for robotic assisted minimally invasive surgery.

i-BRUSH: a gaze-contingent virtual paintbrush for dense 3D reconstruction in robotic assisted surgery.

With increasing demand on intra-operative navigation and motion compensation during robotic assisted minimally invasive surgery, real-time 3D deformation recovery remains a central problem. Currently the majority of existing methods rely on salient features, where the inherent paucity of distinctive landmarks implies either a semi-dense reconstruction or the use of strong geometrical constraints. In this study, we propose a gaze-contingent depth reconstruction scheme by integrating human perception with semi-dense stereo and p-q based shading information. Depth inference is carried out in real-time through a novel application of Bayesian chains without smoothness priors. The practical value of the scheme is highlighted by detailed validation using a beating heart phantom model with known geometry to verify the performance of gaze-contingent 3D surface reconstruction and deformation recovery.