Capsule endoscopy (CE) provides noninvasive access to a large part of the small bowel that is otherwise inaccessible without invasive and traumatic treatment. However, it also produces large amounts of data (approximately 50,000 images) that must be then manually reviewed by a clinician. Such large datasets provide an opportunity for application of image analysis and supervised learning methods. Automated analysis of CE images has only focused on detection, and often only for bleeding. Compared to these detection approaches, we explored assessment of discrete disease for lesions created by mucosal inflammation in Crohn's disease (CD). Our work is the first study to systematically explore supervised classification for CD lesions, a classifier cascade to classify discrete lesions, as well as quantitative assessment of lesion severity. We used a well-developed database of 47 studies for evaluation of these methods. The developed methods show high agreement with ground truth severity ratings manually assigned by an expert, and good precision ( > 90% for lesion detection) and recall ( > 90%) for lesions of varying severity.

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the combination of a materials database with high-throughput ion-transport calculations is an effective approach to screen for promising solid electrolytes. However, automating the complicated preprocessing involved in currently widely used ion-transport characterization algorithms, such as the first-principles nudged elastic band (FP-NEB) method, remains challenging. Here, we report on high-throughput screening platform for solid electrolytes (SPSE) that integrates a materials database with hierarchical ion-transport calculations realized by implementing empirical algorithms to assist in FP-NEB completing automatic calculation. We first preliminarily screen candidates and determine the approximate ion-transport paths using empirical both geometric analysis and the bond valence site energy method. A chain of images are then automatically generated along these paths for accurate FP-NEB calculation. In addition, an open web interface is actualized to enable access to the SPSE database, thereby facilitating machine learning. This interactive platform provides a workflow toward high-throughput screening for future discovery and design of promising solid electrolytes and the SPSE database is based on the FAIR principles for the benefit of the broad research community.

Chemical design of SiO2-based glasses with high elastic moduli and low weight is of great interest. However, it is difficult to find a universal expression to predict the elastic moduli according to the glass composition before synthesis since the elastic moduli are a complex function of interatomic bonds and their ordering at different length scales.Here we show that the densities and elastic moduli of SiO2-based glasses can be efficiently predicted by machine learning (ML) techniques across a complex compositional space with multiple (>10) types of additive oxides besides SiO2. Our machine learning approach relies on a training set generated by high-throughput molecular dynamic (MD) simulations, a set of elaborately constructed descriptors that bridges the empirical statistical modeling with the fundamental physics of interatomic bonding, and a statistical learning/predicting model developed by implementing least absolute shrinkage and selection operator with a gradient boost machine (GBM-LASSO). The predictions of the ML model are comprehensively compared and validated with a large amount of both simulation and experimental data. By just training with a dataset only composed of binary and ternary glass samples, our model shows very promising capabilities to predict the density and elastic moduli for k-nary SiO2based glasses beyond the training set. As an example of its potential applications, our GBM-LASSO model was used to perform a rapid and low-cost screening of many (~10 5 ) compositions of a multicomponent glass system to construct a compositionalproperty database that allows for a fruitful overview on the glass density and elastic properties.

Transport characteristics of ionic conductors play a key role in the performance of electrochemical devices such as solid‐state batteries, solid‐oxide fuel cells, and sensors. Despite the significance of the transport characteristics, they have been experimentally measured only for a very small fraction of all inorganic compounds, which limits the technological progress. To address this deficiency, a database containing crystal structure information, ion migration channel connectivity information, and 3D channel maps for over 29 000 inorganic compounds is presented. The database currently contains ionic transport characteristics for all potential cation and anion conductors, including Li+, Na+, K+, Ag+, Cu(2)+, Mg2+, Zn2+, Ca2+, Al3+, F−, and O2−, and this number is growing steadily. The methods used to characterize materials in the database are a combination of structure geometric analysis based on Voronoi decomposition and bond valence site energy (BVSE) calculations, which yield interstitial sites, transport channels, and BVSE activation energy. The computational details are illustrated on several typical compounds. This database is created to accelerate the screening of fast ionic conductors and to accumulate descriptors for machine learning, providing a foundation for large‐scale research on ion migration in inorganic materials.