Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?

Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning–based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning–based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy

Use of smart glasses for ultrasound-guided peripheral venous access: a randomized controlled pilot study

Objective Smart glasses can provide sonographers with real-time ultrasound images. In the present study, we aimed to evaluate the utility of smart-glasses for ultrasound-guided peripheral venous access. Methods In this randomized, crossover-design, simulation study, 12 participants were recruited from the emergency department residents at a university hospital. Each participant attempted ultrasound-guided peripheral venous access on a pediatric phantom at intervals of 5 days with (glasses group) or without (non-glasses group) the use of smart glasses. In the glasses group, participants confirmed the ultrasound image through the lens of the smart glasses. In the non-glasses group, participants confirmed the ultrasound image through the display viewer located next to the phantom. Procedure time was regarded as the primary outcome, while secondary outcomes included the number of head movements for the participant, number of skin punctures, number of needle redirections, and subjective difficulty. Results No significant differences in procedural time were observed between the groups (non-glasses group: median time, 15.5 seconds; interquartile range [IQR], 10.3 to 27.3 seconds; glasses group: median time, 19.0 seconds; IQR, 14.3 to 39.3 seconds; P=0.58). The number of head movements was lower in the glasses group than in the non-glasses group (glasses group: median, 0; IQR, 0 to 0; non-glasses group: median, 4; IQR, 3 to 5; P<0.01). No significant differences in the number of skin punctures or needle restrictions were observed between the groups. Conclusion Our results indicate that smart-glasses may aid in ensuring ultrasound-guided peripheral venous access by reducing head movements

Maximum Correntropy Criterion Based l1-Iterative Wiener Filter for Sparse Channel Estimation Robust to Impulsive Noise

In this paper, we propose a new sparse channel estimator robust to impulsive noise environments. For this kind of estimator, the convex regularized recursive maximum correntropy (CR-RMC) algorithm has been proposed. However, this method requires information about the true sparse channel to find the regularization coefficient for the convex regularization penalty term. In addition, the CR-RMC has a numerical instability in the finite-precision cases that is linked to the inversion of the auto-covariance matrix. We propose a new method for sparse channel estimation robust to impulsive noise environments using an iterative Wiener filter. The proposed algorithm does not need information about the true sparse channel to obtain the regularization coefficient for the convex regularization penalty term. It is also numerically more robust, because it does not require the inverse of the auto-covariance matrix

Regularization Factor Selection Method for l1-Regularized RLS and Its Modification against Uncertainty in the Regularization Factor

This paper presents a new l1-RLS method to estimate a sparse impulse response estimation. A new regularization factor calculation method is proposed for l1-RLS that requires no information of the true channel response in advance. In addition, we also derive a new model to compensate for uncertainty in the regularization factor. The results of the estimation for many different kinds of sparse impulse responses show that the proposed method without a priori channel information is comparable to the conventional method with a priori channel information

A Modified Recursive Regularization Factor Calculation for Sparse RLS Algorithm with l1-Norm

In this paper, we propose a new calculation method for the regularization factor in sparse recursive least squares (SRLS) with l1-norm penalty. The proposed regularization factor requires no prior knowledge of the actual system impulse response, and it also reduces computational complexity by about half. In the simulation, we use Mean Square Deviation (MSD) to evaluate the performance of SRLS, using the proposed regularization factor. The simulation results demonstrate that SRLS using the proposed regularization factor calculation shows a difference of less than 2 dB in MSD from SRLS, using the conventional regularization factor with a true system impulse response. Therefore, it is confirmed that the performance of the proposed method is very similar to that of the existing method, even with half the computational complexity

ELM (Extreme Learning Machine) Based Correlated Interference Canceller for Small Aperture Array Antenna

The multipath effect creates a highly correlated interference. Subsequently, small aperture array antennas equipped in mobile devices must be able to effectively cancel this coherent interference. Spatial smoothing MMSE is a typical coherent interference cancellation algorithm; however, this method further reduces the small aperture size and reduces the number of coherent interferences to cancel out. This paper proposes a new method to reject coherent interferences without a reduction in the antenna aperture size. We show the superiority of the proposed algorithm through a comparison of cancellation performance with existing adaptive beamforming algorithms

Recommendation in Offline Stores

With the current advancements in mobile and sensing technologies used to collect real-time data in offline stores, retailers and wholesalers have attempted to develop recommender systems to enhance sales and customer experience. However, existing studies on recommender systems have primarily focused on e-commerce platforms and other online services. They did not consider the unique features of indoor shopping in real stores such as the physical environments and objects, which significantly affect the movement and purchase behaviors of customers, thereby representing the &quot;spatiotemporal contexts&quot; that are critical to identifying recommendable items. In this study, we propose a gamification approach wherein a real store is emulated in a pixel world and a recurrent convolutional network is trained to learn the spatiotemporal representation of offline shopping. The superiority and advantages of our method over existing sequential recommender systems are demonstrated through a real-world application in a hypermarket. We believe that our work can significantly contribute to promoting the practice of providing recommendations in offline stores and services

Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs

Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user’s next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP) pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user’s past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user’s next places than the previous approaches considered in most cases

A feasible phenylboronic acid-based delivery platform of anti-PD-L1 antibody for effective cancer immunotherapy

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A feasible Phenylboronic acid-based delivery platform of immune checkpoint blockade for effective cancer immunotherapy

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