Sensor, Signal, and Imaging Informatics
18 August 2017
11 September 2017 (online)
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.
- 1 Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316 (22) 2402-10.
- 2 Obermeyer Z, Emanuel EJ. Predicting the future — big data, machine learning, and clinical medicine. New Engl J Med 2016; 375 (13) 1216-9.
- 3 Chennubhotla C, Clarke LP, Fedorov A, Foran D, Harris G, Helton E. et al. Survey of informatics needs in quantitative imaging for precision medicine in cancer. Yearb Med Inform. 2017
- 4 Moss TJ, Lake DE, Calland JF, Enfield KB, Delos JB, Fairchild KD. et al. Signatures of subacute potentially catastrophic illness in the ICU: model development and validation. Crit Care Med 2016; 44 (09) 1639-48.
- 5 Springer DB, Tarassenko L, Clifford GD. Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng 2016; 63 (04) 822-32.
- 6 Hernando A, Lazaro J, Gil E, Arza A, Garzon JM, Lopez-Anton R. et al. Inclusion of respiratory frequency information in heart rate variability analysis for stress assessment. IEEE J Biomed Health Inform 2016; 20 (04) 1016-25.
- 7 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; 30 (06) 875-88.
- 8 Chincarini A, Sensi F, Rei L, Gemme G, Squarcia S, Longo R. et al. Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer’s disease. Neuroimage 2016; 125: 834-47.
- 9 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; 72: 42-55.
- 10 Arnold CW, Wallace WD, Chen S, Oh A, Abtin F, Genshaft S. et al. RadPath: a web-based system for integrating and correlating radiology and pathology findings during cancer diagnosis. Acad Radiol 2016; 23 (01) 90-100.
- 11 Licurse MY, Lalevic D, Zafar HM, Schnall MD, Cook TS. Expanding the scope of an automated radiology recommendation-tracking engine: initial experiences and lessons learned. J Digit Imaging 2017; 30 (02) 156-62.
- 12 Sun W, Tseng TB, Zhang J, Qian W. Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput Med Imaging Graph 2017; 57: 4-9.
- 13 Summers RM. Progress in fully automated abdominal CT interpretation. AJR Am J Roentgenol 2016; 207 (01) 67-79.
- 14 Peikari M, Gangeh MJ, Zubovits J, Clarke G, Martel AL. Triaging diagnostically relevant regions from pathology whole slides of breast cancer: a texture based approach. IEEE Trans Med Imaging 2016; 35 (01) 307-15.
- 15 Milchenko M, Snyder AZ, LaMontagne P, Shimony JS, Benzinger TL, Fouke SJ. et al. Heterogeneous optimization framework: reproducible preprocessing of multi-spectral clinical MRI for neuro-oncology imaging research. Neuroinformatics 2016; 14 (03) 305-17.
- 16 Brown JM, Horner NR, Lawson TN, Fiegel T, Greenaway S, Morgan H. et al. A bioimage informatics platform for high-throughput embryo phenotyping. Brief Bioinform. 2016
- 17 Liu C, Springer D, Li Q, Moody B, Juan RA, Chorro FJ. et al. An open access database for the evaluation of heart sound algorithms. Physiol Meas 2016; 37 (12) 2181-213.
- 18 Kalpathy-Cramer J, Zhao B, Goldgof D, Gu Y, Wang X, Yang H. et al. A comparison of lung nodule segmentation algorithms: methods and results from a multi-institutional study. J Digit Imaging 2016; 29 (04) 476-87.