scholarly journals Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics

2021 ◽  
Vol 30 (01) ◽  
pp. 150-158
Author(s):  
William Hsu ◽  
Christian Baumgartner ◽  
Thomas M. Deserno ◽  
Keyword(s):  
Imaging Informatics ◽  
Machine Learning Techniques ◽  
Medical Decision ◽  
Average Score ◽  
Imaging Data ◽  
Multiple Sensors ◽  
Mesh Terms ◽  
Image Informatics ◽  
Composite Rating

Summary Objective: To identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020. Method: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and image informatics. We only considered papers that have been published in journals providing at least three articles in the query response. Section editors then independently reviewed the titles and abstracts of preselected papers assessed on a three-point Likert scale. Papers were rated from 1 (do not include) to 3 (should be included) for each topical area (sensors, signals, and imaging informatics) and those with an average score of 2 or above were subsequently read and assessed again by two of the three co-editors. Finally, the top 14 papers with the highest combined scores were considered based on consensus. Results: The search for papers was executed in January 2021. After removing duplicates and conference proceedings, the query returned a set of 101, 193, and 529 papers for sensors, signals, and imaging informatics, respectively. We filtered out journals that had less than three papers in the query results, reducing the number of papers to 41, 117, and 333, respectively. From these, the co-editors identified 22 candidate papers with more than 2 Likert points on average, from which 14 candidate best papers were nominated after intensive discussion. At least five external reviewers then rated the remaining papers. The four finalist papers were found using the composite rating of all external reviewers. These best papers were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Conclusions. Sensors, signals, and imaging informatics is a dynamic field of intense research. The four best papers represent advanced approaches for combining, processing, modeling, and analyzing heterogeneous sensor and imaging data. The selected papers demonstrate the combination and fusion of multiple sensors and sensor networks using electrocardiogram (ECG), electroencephalogram (EEG), or photoplethysmogram (PPG) with advanced data processing, deep and machine learning techniques, and present image processing modalities beyond state-of-the-art that significantly support and further improve medical decision making.

scholarly journals Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics

2020 ◽  
Vol 29 (01) ◽  
pp. 139-144
Author(s):  
William Hsu ◽  
Christian Baumgartner ◽  
Thomas M. Deserno ◽  
Keyword(s):  
Imaging Informatics ◽  
Machine Learning Techniques ◽  
Average Score ◽  
Imaging Data ◽  
Medical Subject Heading ◽  
Multiple Sensors ◽  
Mesh Terms ◽  
Learning Techniques ◽  
Composite Rating

Objective: To highlight noteworthy papers that are representative of 2019 developments in the fields of sensors, signals, and imaging informatics. Method: A broad literature search was conducted in January 2020 using PubMed. Separate predefined queries were created for sensors/signals and imaging informatics using a combination of Medical Subject Heading (MeSH) terms and keywords. Section editors reviewed the titles and abstracts of both sets of results. Papers were assessed on a three-point Likert scale by two co-editors, rated from 3 (do not include) to 1 (should be included). Papers with an average score of 2 or less were then read by all three section editors, and the group nominated top papers based on consensus. These candidate best papers were then rated by at least six external reviewers. Results: The query related to signals and sensors returned a set of 255 papers from 140 unique journals. The imaging informatics query returned a set of 3,262 papers from 870 unique journals. Based on titles and abstracts, the section co-editors jointly filtered the list down to 50 papers from which 15 candidate best papers were nominated after discussion. A composite rating after review determined four papers which were then approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. These best papers represent different international groups and journals. Conclusions: The four best papers represent state-of-the-art approaches for processing, combining, and analyzing heterogeneous sensor and imaging data. These papers demonstrate the use of advanced machine learning techniques to improve comparisons between images acquired at different time points, fuse information from multiple sensors, and translate images from one modality to another.

scholarly journals Advancing Artificial Intelligence in Sensors, Signals, and Imaging Informatics

2019 ◽  
Vol 28 (01) ◽  
pp. 115-117
Author(s):  
William Hsu ◽  
Christian Baumgartner ◽  
Thomas Deserno ◽  
Keyword(s):  
Artificial Intelligence ◽  
Imaging Informatics ◽  
Machine Learning Techniques ◽  
Mesh Terms ◽  
Learning Techniques ◽  
Different Types ◽  
Recent Developments ◽  
Hidden Patterns

Objective: To identify research works that exemplify recent developments in the field of sensors, signals, and imaging informatics. Method: A 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. Results: A 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. Conclusions: The 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.

scholarly journals Sensor, Signal, and Imaging Informatics in 2017

2018 ◽  
Vol 27 (01) ◽  
pp. 110-113 ◽  
Author(s):  
William Hsu ◽  
Thomas Deserno ◽  
Charles Kahn ◽  
Keyword(s):  
Medical Informatics ◽  
Imaging Informatics ◽  
Machine Learning Techniques ◽  
Sensor Signal ◽  
Imaging Data ◽  
Scientific Publications ◽  
Learning Techniques ◽  
Final Selection ◽  
Selection Of

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.

scholarly journals Sensor, Signal, and Imaging Informatics

2017 ◽  
Vol 26 (01) ◽  
pp. 120-124
Author(s):  
W. Hsu ◽  
S. Park ◽  
Charles Kahn
Keyword(s):  
Medical Informatics ◽  
Scientific Work ◽  
Imaging Informatics ◽  
Sensor Signal ◽  
Imaging Data ◽  
Extensive Search ◽  
Challenges And Opportunities ◽  
Final Selection ◽  
Selection Of

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.

scholarly journals Sensor, Signal, and Imaging Informatics

2017 ◽  
Vol 26 (01) ◽  
pp. 120-123
Author(s):  
W. Hsu ◽  
S. Park ◽  
Charles Kahn
Keyword(s):  
Medical Informatics ◽  
Scientific Work ◽  
Imaging Informatics ◽  
Sensor Signal ◽  
Imaging Data ◽  
Extensive Search ◽  
Challenges And Opportunities ◽  
Final Selection ◽  
Selection Of

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.

Machine Learning for Neurodegenerative Disorder Diagnosis — Survey of Practices and Launch of Benchmark Dataset

2018 ◽  
Vol 27 (03) ◽  
pp. 1850011 ◽  
Author(s):  
Athanasios Tagaris ◽  
Dimitrios Kollias ◽  
Andreas Stafylopatis ◽  
Georgios Tagaris ◽  
Stefanos Kollias
Keyword(s):  
Machine Learning ◽  
Neurodegenerative Disorder ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Network Architectures ◽  
Imaging Data ◽  
Learning Techniques ◽  
Medical Examinations

Neurodegenerative disorders, such as Alzheimer’s and Parkinson’s, constitute a major factor in long-term disability and are becoming more and more a serious concern in developed countries. As there are, at present, no effective therapies, early diagnosis along with avoidance of misdiagnosis seem to be critical in ensuring a good quality of life for patients. In this sense, the adoption of computer-aided-diagnosis tools can offer significant assistance to clinicians. In the present paper, we provide in the first place a comprehensive recording of medical examinations relevant to those disorders. Then, a review is conducted concerning the use of Machine Learning techniques in supporting diagnosis of neurodegenerative diseases, with reference to at times used medical datasets. Special attention has been given to the field of Deep Learning. In addition to that, we communicate the launch of a newly created dataset for Parkinson’s disease, containing epidemiological, clinical and imaging data, which will be publicly available to researchers for benchmarking purposes. To assess the potential of the new dataset, an experimental study in Parkinson’s diagnosis is carried out, based on state-of-the-art Deep Neural Network architectures and yielding very promising accuracy results.

scholarly journals Quantitative Imaging Informatics for Cancer Research

2020 ◽  
pp. 444-453 ◽  
Author(s):  
Andrey Fedorov ◽  
Reinhard Beichel ◽  
Jayashree Kalpathy-Cramer ◽  
David Clunie ◽  
Michael Onken ◽  
...  
Keyword(s):  
Cancer Research ◽  
Best Practices ◽  
Cancer Imaging ◽  
Quantitative Imaging ◽  
Imaging Informatics ◽  
Imaging Data ◽  
Communication Interface ◽  
3D Slicer ◽  
And Training ◽  
Data Commons

PURPOSE We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program. METHODS QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach. RESULTS Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation. CONCLUSION Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.

scholarly journals Machine Classification of Transient Images

2014 ◽  
Vol 10 (S306) ◽  
pp. 288-291
Author(s):  
Lise du Buisson ◽  
Navin Sivanandam ◽  
Bruce A. Bassett ◽  
Mathew Smith
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Machine Learning Techniques ◽  
Imaging Data ◽  
Transient Detection ◽  
Minimum Error ◽  
Learning Techniques ◽  
Nearest Neighbours ◽  
Transient Imaging

AbstractUsing transient imaging data from the 2nd and 3rd years of the SDSS supernova survey, we apply various machine learning techniques to the problem of classifying transients (e.g. SNe) from artefacts, one of the first steps in any transient detection pipeline, and one that is often still carried out by human scanners. Using features mostly obtained from PCA, we show that we can match human levels of classification success, and find that a K-nearest neighbours algorithm and SkyNet perform best, while the Naive Bayes, SVM and minimum error classifier have performances varying from slightly to significantly worse.

scholarly journals Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective

2021 ◽  
Vol 8 ◽  
Author(s):  
Daniele Roberto Giacobbe ◽  
Alessio Signori ◽  
Filippo Del Puente ◽  
Sara Mora ◽  
Luca Carmisciano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Prediction Models ◽  
Early Recognition ◽  
Model Performance ◽  
Daily Practice ◽  
Machine Learning Techniques ◽  
Medical Decision ◽  
Long Run ◽  
Learning Techniques

Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.

scholarly journals Imaging Informatics: from Image Management to Image Navigation

2009 ◽  
Vol 18 (01) ◽  
pp. 167-172 ◽  
Author(s):  
O. Ratib
Keyword(s):  
Communication Systems ◽  
Rapid Development ◽  
Imaging Informatics ◽  
Multidimensional Data ◽  
Navigation Systems ◽  
Image Management ◽  
Care Providers ◽  
Imaging Data ◽  
Image Navigation ◽  
Picture Archiving And Communication

Summary Objective To review the rapid evolution of imaging informatics dealing with issues of management and communication of digital images starting from the era of simple storage and transfer of images to today’s world of interactive navigation in large sets of multidimensional data. Methods This paper will review the initial concepts of Picture Archiving and Communication Systems (PACS) and the early developments and standardization efforts that lead to the deployment of large intra-institutional networks of image distribution allowing radiologists and physicians to access and review images digitally. With the deployment of PACS came along the need for advanced tools for image visualization and image analysis. Results Review of the history of PACS and Imaging Informatics clearly shows that the early developments were focused on the radiologist’s requirements for diagnosis and image interpretation. These early developments lagged behind the rapid adoption of digital imaging in areas outside radiology. Only recently, imaging informatics shifted toward the development of new tools geared toward the needs of other users such as surgeons, referring physicians and care-providers, and even for the patients themselves. Also in the recent years, the development of multimedia and communication tools in the consumer market has influenced the design and strategic development of image management platforms inside and outside healthcare institutions. Conclusions The focus of imaging informatics has clearly shifted in the last decade from basic infrastructure design to complete data and image navigation systems. While the challenge of storing and managing large volumes of imaging data have slowly vanished with the rapid development if information technology, the new challenge emerged from the new requirements of image manipulation and analysis in clinical practice.

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