Sensor, Signal, and Imaging Informatics in 2017

@article{Hsu2018SensorSA,
  title={Sensor, Signal, and Imaging Informatics in 2017},
  author={William Hsu and Thomas Martin Deserno and Charles E. Kahn},
  journal={Yearbook of Medical Informatics},
  year={2018},
  volume={27},
  pages={110 - 113}
}
Summary Objective:  To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017. Methods:  PubMed ® and Web of Science ® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection… 

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References

SHOWING 1-10 OF 16 REFERENCES
Deep Learning on 1-D Biosignals: a Taxonomy-based Survey
TLDR
A large variability of research with respect to data, application, and network topology is demonstrated and future research is expected to focus on the standardization of deep learning architectures and on the optimization of the network parameters to increase performance and robustness.
Defining the biological basis of radiomic phenotypes in lung cancer
TLDR
It is demonstrated that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard-of-care medical images.
Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software
TLDR
The study results showed that most of the radiomic features in GBM were highly stable, and certain aspects of feature quality, including NDR and redundancy, need to be assessed for determination of representative signature features before further development of radiomics.
Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging.
TLDR
Findings support further investigation of the algorithm in a controlled and prospective clinical setting to determine whether it can independently screen noncontrast-enhanced head CT examinations and notify the interpreting radiologist of critical findings.
Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs.
TLDR
A deep-learning convolutional neural network model can estimate skeletal maturity with accuracy similar to that of an expert radiologist and to that that of existing automated models.
Use of Radiology Procedure Codes in Health Care: The Need for Standardization and Structure.
TLDR
The role of imaging procedure codes in radiology departments and across the health care enterprise is reviewed, standards for radiology procedure coding are described, and the mechanisms of structured coding systems are reviewed.
Detecting Breathing and Snoring Episodes Using a Wireless Tracheal Sensor—A Feasibility Study
TLDR
The sensitivity and specificity of a novel wireless system in detecting breathing and snoring episodes during sleep is investigated to open unexplored possibilities in sleep monitoring and study enabling a multinight recording strategy involving the collection and analysis of abundant data from thousands of people.
Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring
TLDR
A novel unified framework for automatic detection, localization, and classification of single and combined ECG noises not only achieves better noise detection and classification rates than the current state-of-the-art methods but also accurately localizes short bursts of noises with low endpoint delineation errors.
A Modular Low-Complexity ECG Delineation Algorithm for Real-Time Embedded Systems
TLDR
This work presents a new modular and low-complexity algorithm for the delineation of the different ECG waves (QRS, P and T peaks, onsets, and end), intended to perform real-time delineation on resource-constrained embedded systems.
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