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Archaeologists tend to produce slow data that is contextually rich but often difficult to generalize. An example is the analysis of lithic microdebitage, or knapping debris, that is smaller than 6.3 mm (0.25 in.). So far, scholars have relied on manual approaches that are prone to intra- and interobserver errors. In the following, we present a machine learning–based alternative together with experimental archaeology and dynamic image analysis. We use a dynamic image particle analyzer to measure each particle in experimentally produced lithic microdebitage (N = 5,299) as well as an archaeological soil sample (N = 73,313). We have developed four machine learning models based on Naïve Bayes, glmnet (generalized linear regression), random forest, and XGBoost (“Extreme Gradient Boost[ing]”) algorithms. Hyperparameter tuning optimized each model. A random forest model performed best with a sensitivity of 83.5%. It misclassified only 28 or 0.9% of lithic microdebitage. XGBoost models reached a sensitivity of 67.3%, whereas Naïve Bayes and glmnet models stayed below 50%. Except for glmnet models, transparency proved to be the most critical variable to distinguish microdebitage. Our approach objectifies and standardizes microdebitage analysis. Machine learning allows studying much larger sample sizes. Algorithms differ, though, and a random forest model offers the best performance so far.

Purpose One popular method of assessing brain functional connectivity (FC) is through seed-based correlation (SCA) analysis. One drawback of this method is when the seed location is varied slightly, the FC can vary dramatically. We propose a method superior to SCA, robust to variations in seed location, which confers a probabilistic interpretation. Methods We introduce a probabilistic method which generates a cloud of highly connected voxels to determine a stable set of voxels connected to the seed location (SC-SCA). This cloud can generate a correlation map or a probabilistic map. The method is applied to the default mode network (DMN) based on a posterior cingulate cortex (PCC) seed, and the auditory network (AN) as validation on a smaller network. A Bayesian interpretation is demonstrated through performing a maximum a posteriori (MAP) estimation on the DMN. The advantages of the method are tested by performing stability analyses on its influential parameters. The method is extended to region-based SC-SCA, and then comparisons are made based on seed-based vs region-based versions of the methods SC-SCA vs traditional SCA. The statistical significance between the methods is assessed via a bootstrap method using the difference in medians of the standard deviation of the voxels for 16 subjects. Results The proposed method, SC-SCA, is able to identify a set of regions - the DMN - that are known to be associated with and have high correlation with the PCC, and the method is also extensible to smaller networks as shown by its performance on the AN. Based on the certainty of the a priori distribution for MAP analysis, the method is able to identify regions with high probability of belonging to the DMN. The stability analyses demonstrated that substantial deviations from the initial seed locations in the sagittal, posterior transverse, and axial directions by ±10 mm do not cause substantial variation in the correlation network produced. Qualitative inspection of the average correlation maps garnered from the four methods showed that SC-SCA shows a larger amount of detail in FC connectivity as compared to SCA. Seed-based methods show higher detail and contrast in the maps in comparison with region-based methods. Quantitatively, the statistical tests between seed-based vs region-based and SC-SCA vs SCA revealed that there is no significant difference between the following methods: region-based SCA or region-based SC-SCA, and seed-based SC-SCA or region-based SC-SCA. However, there are statistically significant differences and advantages conferred between the following methods: seed-based SC-SCA over seed-based SCA, region-based SC-SCA over seed-based SCA, region-based SCA over seed-based SCA, and region-based SCA over seed-based SC-SCA. Finally, seed-based SC-SCA outperforms sphere-based SCA. Conclusions The proposed method offers several advantages over traditional SCA: robust single-seed FC estimation, novel Bayesian estimation capabilities, enhanced detail of brain structures, robustness to initial seed location, and enhanced consistency in the correlation maps generated. Region-based SC-SCA is equivalent or superior to all investigated methods, where seed-based SCA is inferior to all methods. The method confers improved single-seed SCA with the additional benefit of Bayesian estimation.

For surgeons performing open procedures, the sense of touch is a valuable tool to directly access buried structures and organs, to identify their margins, detect tumors, and prevent undesired cuts. Minimally invasive surgical procedures provide great benefits for patients; however, they hinder the surgeon's ability to directly manipulate the tissue. In our previous work, we developed a Wireless Palpation Probe (WPP) to restore tissue palpation in Minimally Invasive Surgery (MIS) by creating a real-time stiffness distribution map of the target tissue. The WPP takes advantage of a field-based magnetic localization algorithm to measure its position, orientation, and tissue indentation depth, in addition to a barometric sensor measuring indentation tissue pressure. However, deformations of both the tissue and the silicone material used to cover the pressure sensors introduce detrimental nonlinearities in sensor measurements. In this work, we calibrated and characterized different diameter WPP heads with a new design allowing exchangeability and disposability of the probe head. Benchtop trials showed that this method can effectively reduce error in sensor pressure measurements up to 5% with respect to the reference sensor. Furthermore, we studied the effect of the head diameter on the device's spatial resolution in detecting tumor simulators embedded into silicone phantoms. Overall, the results showed a tumor detection rate over 90%, independent of the head diameter, when an indentation depth of 5 mm is applied on the tissue simulator.

Restoring tissue palpation in Minimally Invasive Surgery (MIS) procedures has been a relevant research topic in the last decade. Among the developed devices the Wireless Palpation Probe (WPP) embeds a barometric pressure sensor to create a stiffness distribution map of underlying tissues. In this work we characterized nine different interchangeable WPP heads fabricated with the combination of three different thickness/materials. Step responses and stress strain were evaluated to assess the best material and thickness combination for the next WPP heads fabrication.

Endoscopic training can be challenging for the trainee and preceptor. Frustration can result from ineffective communication regarding areas of interest. Our team developed a novel tablet application for real-time mirroring of the colonoscopy examination that allows preceptors to make annotations directly on the viewing monitor. The potential for improvement in team proficiency and satisfaction is unknown.The on-screen endoscopic image is mirrored to an Android tablet and permits real-time annotation directly on the in-room endoscopic image display. Preceptors can also "freeze-frame" an image and provide visual on-screen instruction (telestration). Trainees, precepted by a GI attending, were 1:1 randomized to perform colonoscopy on a training phantom using the application with traditional precepting or traditional precepting alone. Magnetized polyps (size 5 mm) were placed in 1 of 5 preset location scenarios. Each trainee performed a total of 10 colonoscopies and completed each location scenario twice. During withdrawal, the trainee and the attending identified polyps. Outcome measures included number of polyps missed and participant satisfaction after each trial.Fifteen trainees (6 novice and 9 GI fellows) performed a total of 150 colonoscopies where 330 polyps in total were placed. Fellows missed fewer polyps using the tablet versus traditional precepting alone (4.2% vs 12.5%; P = .04). There was no significant difference in missed polyps for novices (12.5% vs 18.8%; P = .66). Overall, fellows missed fewer polyps when compared with novices regardless of the precepting method (P = .01). The attending and all trainees reported reduced stress with improved communication using the tablet.Fellows missed fewer polyps using the tablet when compared with traditional endoscopy precepting. All trainees reported reduced stress, quicker identification of polyps, and improved educational satisfaction using the tablet. Our application has the potential to improve trainee plus attending team lesion detection and to enhance the endoscopy training experience for both the trainee and attending preceptor.

Colorectal cancer is one of the leading causes of cancer-related deaths worldwide, although it can be effectively treated if detected early. Teleoperated flexible endoscopes are an emerging technology to promote participation in these preventive screenings. Real-time pose estimation is therefore essential to enable feedback to the robotic endoscope's control system. Vision-based endoscope localization approaches are a promising avenue, since they do not require extra sensors on board the endoscopes. In this work, we compare several state-of-the-art algorithms for computing the image motion (optical flow), which is then used with a supervised learning strategy to provide an accurate estimate of the 6 degree of freedom endoscope motion. The method is validated using a robotically actuated endoscope in a human colon simulator, and represents a preliminary effort towards testing with clinical video data.

Colorectal cancer is one of the leading causes of cancer-related mortality in the world, although it can be efficiently treated if detected early. Colonoscopy is the most-commonly adopted visual screening procedure of the colon by means of a flexible tiny endoscopic camera. In an effort to promote early screening and to facilitate mastering the endoscope motion by the physician, teleoperable robotic endoscopes and wireless capsules are being developed. In order to enable precise and fast closed-loop control for these devices, the accurate 3-D position and orientation of the camera must be known. Estimating the camera ego-motion by processing the endoscopic video provides a viable solution since it does not require the adoption of external magnetic trackers during the screening procedure that can occupy the scope’s operation channel. Furthermore, and compared to SLAM or registration approaches, ego-motion estimation algorithms do not require to deal with the highly deformable (and thus highly-uncertain) global 3D map of the colon.

Objective: Colorectal cancer is one of the leading causes of cancer-related deaths in the world, although it can be effectively treated if detected early. Teleoperated flexible endoscopes are an emerging technology to ease patient apprehension about the procedure, and subsequently increase compliance. Essential to teleoperation is robust feedback reflecting the change in pose (i.e., position and orientation) of the tip of the endoscope. The goal of this study is to first describe a novel image-based tracking system for teleoperated flexible endoscopes, and subsequently determine its viability in a clinical setting. The proposed approach leverages artificial neural networks (ANNs) to learn the mapping that links the optical flow between two sequential images to the change in the pose of the camera. Secondly, the study investigates for the first time how narrow band illumination (NBI) - today available in commercial gastrointestinal endoscopes - can be applied to enhance feature extraction, and quantify the effect of NBI and white light illumination (WLI), as well as their color information, on the strength of features extracted from the endoscopic camera stream. Methods and materials: In order to provide the best features for the neural networks to learn the change in pose based on the image stream, we investigated two different imaging modalities - WLI and NBI - and we applied two different spatial partitions - lumen-centered and grid-based - to create descriptors used as input to the ANNs. An experiment was performed to compare the error of these four variations, measured in root mean square error (RMSE) from ground truth given by a robotic arm, to that of a commercial state-of-the-art magnetic tracker. The viability of this technique for a clinical setting was then tested using the four ANN variations, a magnetic tracker, and a commercial colonoscope. The trial was performed by an expert endoscopist (>2000 lifetime procedures) on a colonoscopy training model with porcine blood, and the RMSE of the ANN output was calculated with respect to the magnetic tracker readings. Using the image stream obtained from the commercial endoscope, the strength of features extracted was evaluated. Results: In the first experiment, the best ANNs resulted from grid-based partitioning under WLI (2.42mm RMSE) for position, and from lumen-centered partitioning under NBI (1.69^o RMSE) for rotation. By comparison, the performance of the tracker was 2.49mm RMSE in position and 0.89^o RMSE in rotation. The trial with the commercial endoscope indicated that lumen-centered partitioning was the best overall, while NBI outperformed WLI in terms of illumination modality. The performance of lumen-centered partitioning with NBI was 1.03+/-0.8mm RMSE in positional degrees of freedom (DOF), and 1.26+/-0.98^o RMSE in rotational DOF, while with WLI, the performance was 1.56+/-1.15mm RMSE in positional DOF and 2.45+/-1.90^o RMSE in rotational DOF. Finally, the features extracted under NBI were found to be twice as strong as those extracted under WLI, but no significance in feature strengths was observed between a grayscale version of the image, and the red, blue, and green color channels. Conclusions: This work demonstrates that both WLI and NBI, combined with feature partitioning based on the anatomy of the colon, provide valid mechanisms for endoscopic camera pose estimation via image stream. Illumination provided by WLI and NBI produce ANNs with similar performance which are comparable to that of a state-of-the-art magnetic tracker. However, NBI produces features that are stronger than WLI, which enables more robust feature tracking, and better performance of the ANN in terms of accuracy. Thus, NBI with lumen-centered partitioning resulted the best approach among the different variations tested for vision-based pose estimation. The proposed approach takes advantage of components already available in commercial gastrointestinal endoscopes to provide accurate feedback about the motion of the tip of the endoscope. This solution may serve as an enabling technology for closed-loop control of teleoperated flexible endoscopes.

BackgroundSchnitzler syndrome (SchS) is a rare autoinflammatory disease characterized by urticarial exanthema, bone and joint alterations, fever and monoclonal IgM gammopathy. Overactivation of the interleukin(IL)-1 system is reported, even though the exact pathophysiological pathways remain unknown.ObjectiveTo determine ex vivo cytokine profiles of Peripheral Blood Mononuclear Cells (PBMCs) from SchS patients prior to treatment and after initiation of anti-IL-1 therapy (anakinra). The sera cytokine profile was studied in parallel.MethodsWe collected blood samples from thirty-six untreated or treated SchS. PBMCs were cultured with and without LPS or anti-CD3/CD28. Cytokine levels were evaluated in serum and cell culture supernatants using Luminex technology.ResultsSpontaneous TNFα, IL-6, IL-1β, IL-1α, and IL-1RA release by PBMCs of SchS patients were higher than in controls. LPS-stimulation further induced the secretion of these cytokines. In contrast, after T-cell stimulation, TNFα, IL-10, IFNγ, IL-17A, and IL-4 production decreased in SchS patients compared to healthy controls, but less in treated patients. Whereas IL-1β serum level was not detected in most sera, IL-6, IL-10, and TNFα serum levels were higher in patients with SchS and IFNγ and IL-4 levels were lower. Of note, IL-6 decreased after treatment in SchS (p = 0.04).ConclusionOur data strengthen the hypothesis of myeloid inflammation in SchS, mediated in particular by IL-1β, TNFα, and IL-6, associated with overproduction of the inhibitors IL-1RA and IL-10. In contrast, we observed a loss of Th1, Th2, and Th17 cell functionalities that tends to be reversed by anakinra.