Yearb Med Inform 2017; 26(01): 120-124
DOI: 10.15265/IY-2017-019
Section 4: Sensor, Signal and Imaging Informatics
Synopsis
Georg Thieme Verlag KG Stuttgart

Sensor, Signal, and Imaging Informatics

W. Hsu
1  University of California, Los Angeles, California, USA
,
S. Park
2  Columbia University College of Physicians and Surgeons, New York, New York, USA
,
Charles E. Kahn Jr.
3  University of Pennsylvania, Philadelphia, Pennsylvania, USA
› Author Affiliations
Further Information

Publication History

Publication Date:
11 September 2017 (online)

Summary

Objective: To summarize significant contributions to sensor, signal, and imaging informatics published in 2016.

Methods: We conducted an extensive search using PubMed® and Web of Science® to identify the scientific contributions published in 2016 that addressed sensors, signals, and imaging in medical informatics. The three section editors selected 15 candidate best papers by consensus. Each candidate article was reviewed by the section editors and at least two other external reviewers. The final selection of the six best papers was conducted by the editorial board of the Yearbook.

Results: The selected papers of 2016 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.

Conclusion: The growing volume of signal and imaging data provides exciting new challenges and opportunities for research in medical informatics. Evolving technologies provide faster and more effective approaches for pattern recognition and diagnostic evaluation. The papers selected here offer a small glimpse of the high-quality scientific work published in 2016 in the domain of sensor, signal, and imaging informatics.

Section
Sensor, Signal and Imaging Informatics

Arnold CW, Wallace WD, Chen S, Oh A, Abtin F, Genshaft S, Binder S, Aberle D, Enzmann D. RadPath: A web-based system for integrating and correlating radiology and pathology findings during cancer diagnosis. Acad Radiol 2016 Jan;23(1):90-100 http://escholarship.org/uc/item/22x4021q

Hravnak M, Chen L, Dubrawski A, Bose E, Clermont G, Pinsky MR. Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data. J Clin Monit Comput 2016 Dec;30(6):875-88 https://link.springer.com/article/10.1007%2Fs10877-015-9788-2

Kalpathy-Cramer J, Zhao B, Goldgof D, Gu Y, Wang X, Yang H, Tan Y, Gillies R, Napel S. A comparison of lung nodule segmentation algorithms: methods and results from a multi-institutional study. J Digit Imaging 2016 Aug;29(4):476-87 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4942386/

Moss TJ, Lake DE, Calland JF, Enfield KB, Delos JB, Fairchild KD, Moorman JR. Signatures of subacute potentially catastrophic illness in the ICU: model development and validation. Crit Care Med 2016 Sep;44(9):1639-48 https://insights.ovid.com/pubmed?pmid=27452809

Petousis P, Han SX, Aberle D, Bui AA. Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network. Artif Intell Med 2016 Sep;72:42-55 https://linkinghub.elsevier.com/retrieve/pii/S0933-3657(16)30106-3

Springer DB, Tarassenko L, Clifford GD. Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng 2016 Apr;63(4):822-32 http://ieeexplore.ieee.org/document/7234876/