Advancing Artificial Intelligence in Sensors, Signals, and Imaging Informatics.

ObjectiveTo identify research works that exemplify recent developments in the field of sensors, signals, and imaging informatics.MethodA broad literature search was conducted using PubMed and Web of Science, supplemented with individual papers that were nominated by section editors. A predefined query made from a combination of Medical Subject Heading (MeSH) terms and keywords were used to search both sources. Section editors then filtered the entire set of retrieved papers with each paper having been reviewed by two section editors. Papers were assessed on a three-point Likert scale by two section editors, rated from 0 (do not include) to 2 (should be included). Only papers with a combined score of 2 or above were considered.ResultsA search for papers was executed at the start of January 2019, resulting in a combined set of 1,459 records published in 2018 in 119 unique journals. Section editors jointly filtered the list of candidates down to 14 nominations. The 14 candidate best papers were then ranked by a group of eight external reviewers. Four papers, representing different international groups and journals, were selected as the best papers by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board.ConclusionsThe fields of sensors, signals, and imaging informatics have rapidly evolved with the application of novel artificial intelligence/machine learning techniques. Studies have been able to discover hidden patterns and integrate different types of data towards improving diagnostic accuracy and patient outcomes. However, the quality of papers varied widely without clear reporting standards for these types of models. Nevertheless, a number of papers have demonstrated useful techniques to improve the generalizability, interpretability, and reproducibility of increasingly sophisticated models

Proof-of-principle validation of a novel intraluminal optical sensor for dynamic monitoring of intestinal anastomosis: An in vivo animal model case study

Intestinal resections are commonly performed to treat different colorectal conditions, including colorectal cancer. A successful primary anastomosis is the desired optimal outcome after intestinal resection. Maintaining adequate blood flow across the anastomosis is paramount for reducing anastomotic failure. Currently, there are no clinical devices capable of continuously assessing blood flow and blood perfusion at an anastomosis during and after surgery. The aim of this study was to develop an indwelling optical sensor for the monitoring of perfusion biomarkers using photoplethysmography and near-infrared spectroscopy principles. In an animal in-vivo proof-of-principle study, it was found that the developed sensor performed appropriately for the assessment of blood flow and perfusion in an anastomosis, showing changes in the assessed parameters after gradual devascularization of the transected bowel

Measuring thermal conductivity in freezing and thawing soil using the soil temperature response to heating

The thermal conductivity of the thin seasonally freezing and thawing soil layer in permafrost landscapes exerts considerable control over the sensitivity of the permafrost to energy and mass exchanges at the surface. At the same time, the thermal conductivity is sensitive to the state of the soil, varying, for example, by up to two orders of magnitude with varying water contents. In situ measurement techniques perturb the soil thermally and are affected by changes in soil composition, for example through variations in thermal contact resistance between sensor and soil. The design of a sensor for measuring the temperature of the soil rather than the axial heating wire temperature has consequences for the modeling of heat flow. We introduce an approximation of heat flow from a heated cylinder with thermal contact resistance between the cylinder and the surrounding medium. This approximation is compared to the standard line source approximation, and both are applied to data measured over a one-year period in northern Alaska. Comparisons of thermal conductivity values determined numerically using the line source solution, line source approximation and the analytical form of the heated cylinder model fall within 10% of accepted values, except for measurements made in pure ice, for which all methods of calculation under-predicted the thermal conductivity. Field data collected from a complete freeze¿thaw cycle in silty clay show a seasonally bimodal apparent thermal conductivity, with a sharp transition between frozen and thawed values during thaw, but a three-month transition period during freezing. The use of soil composition data to account for changes in heat flow due to the effect of latent heat during phase change results in a relationship between soil thermal conductivity and temperature

Retraction: Muminov, A. et al. Modern Virtual Fencing Application: Monitoring and Controlling Behavior of Goats Using GPS Collars and Warning Signals. Sensors 2019, 19, 1598

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Sensors 2020 Best Paper Awards

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Acknowledgment to the Reviewers of Sensors in 2022

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Acknowledgment to Reviewers of Sensors in 2021

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Acknowledgment to Reviewers of Sensors in 2020

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