Sensor, Signal, and Imaging Informatics in 2017

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 of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook.

Results: The selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.

Conclusion: The growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics.

Keywords

Signal processing - biomedical imaging - deep learning - radiomics

• References

• 1 Ganapathy N, Swaminathan R, Deserno TM. Deep learning on 1D biosignals: a taxonomy-based survey. Yearb Med Inform 2018; 98-109
• 2 Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology 2018; 287 (01) 313-22
• 3 Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S. et al. Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 2017; 285 (03) 923-31
• 4 Dawes TJW, de Marvao A, Shi W, Fletcher T, Watson GMJ, Wharton J. et al. Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology 2017; 283 (02) 381-90
• 5 Sharma H, Zerbe N, Klempert I, Hellwich O, Hufnagl P. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput Med Imaging Graph 2017; 61: 2-13
• 6 Lee M, Woo B, Kuo MD, Jamshidi N, Kim JH. Quality of radiomic features in glioblastoma multiforme: Impact of semi-automated tumor segmentation software. Korean J Radiol 2017; 18 (03) 498-509
• 7 Grossmann P, Stringfield O, El-Hachem N, Bui MM, Rios Velazquez E, Parmar C. et al. Defining the biological basis of radiomic phenotypes in lung cancer. ELife 2017; 6
• 8 Wang KC, Patel JB, Vyas B, Toland M, Collins B, Vreeman DJ. et al. Use of radiology procedure codes in health care: The need for standardization and structure. RadioGraphics 2017; 37 (04) 1099-110
• 9 Parks CL, Monson KL. Automated facial recognition of computed tomography-derived facial images: Patient privacy implications. J Digit Imaging 2017; 30 (02) 204-14
• 10 Tobola A, Leutheuser H, Pollak M, Spies P, Hofmann C, Weigand C. et al. Self-powered multiparameter health sensor. IEEE J Biomed Health Inform 2018; 22 (01) 15-22
• 11 Sahoo PK, Thakkar HK, Lee MY. A cardiac early warning system with multi channel SCG and ECG monitoring for mobile health. Sensors (Basel) 2017;17(4):
• 12 Satija U, Ramkumar B, Manikandan MS. Automated ECG noise detection and classification system for unsupervised healthcare monitoring. IEEE J Biomed Health Inform 2018; 22 (03) 722-32
• 13 Bote JM, Recas J, Rincon F, Atienza D, Hermida R. A modular low-complexity ECG delineation algorithm for real-time embedded systems. IEEE J Biomed Health Inform 2018; 22 (02) 429-41
• 14 Christov I, Neycheva T, Schmid R, Stoyanov T, Abacherli R. Pseudo-real-time low-pass filter in ECG self-adjustable to the frequency spectra of the waves. Med Biol Eng Comput 2017; 55 (09) 1579-88
• 15 Te AL, Higa S, Chung FP, Lin CY, Lo MT, Liu CA. et al. The use of a novel signal analysis to identify the origin of idiopathic right ventricular outflow tract ventricular tachycardia during sinus rhythm: Simultaneous amplitude frequency electrogram transformation mapping. PLoS One 2017; 12 (03) e0173189
• 16 Młyńczak M, Migacz E, Migacz M, Kukwa W. Detecting breathing and snoring episodes using a wireless tracheal sensor -- a feasibility study. IEEE J Biomed Health Inform 2017; 21 (06) 1504-10