Oleg S. Pianykh

Bio: Oleg S. Pianykh is an academic researcher from Harvard University. The author has contributed to research in topics: Wavelet & Data compression. The author has an hindex of 13, co-authored 64 publications receiving 1287 citations. Previous affiliations of Oleg S. Pianykh include Beth Israel Deaconess Medical Center & LSU Health Sciences Center New Orleans.

Current Applications and Future Impact of Machine Learning in Radiology.

Abstract: Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed. © RSNA, 2018

Digital Imaging and Communications in Medicine (DICOM)

Abstract: Foreword This Supplement to the DICOM Standard introduces the SR SOP Classes for transmission and storage of documents that describe or refer to any number of images or waveforms or to the specific features that they contain. The SR SOP Classes fully support conventional free text reports and provide the capability 4 to record structured information that enhances the precision, clarity and value of clinical documents. The SR SOP Classes allow users to link text and other data to particular images and/or waveforms and to store the coordinates of findings so that users can see exactly what is being described in a report. In addition, users can label, index and retrieve clinically-relevant information using codes. SR SOP Classes 8 can be used in a variety of clinical contexts. For example:-in CT or MRI to convey the interpretation text, to record the DICOM identifiers of selected images and to denote the spatial coordinates of significant findings;-in ultrasound to transmit measurements; and 12-in cardiac catheterization laboratories to record a procedure log that time-stamps and describes significant measurements and interventions and link together all of the related images, waveforms, interpretation reports and related information into a convenient unit-record.

Continuous Learning AI in Radiology: Implementation Principles and Early Applications.

Abstract: Continuous learning artificial intelligence algorithms learn and retrain continually, making them less prone to error and bias.

Socioeconomic and Demographic Predictors of Missed Opportunities to Provide Advanced Imaging Services

Abstract: Purpose The extent to which racial and socioeconomic disparities exist in accessing clinically appropriate, advanced diagnostic imaging has not been well studied. This study assesses the relationship between demographic and socioeconomic factors and the incidence of imaging missed care opportunities (IMCOs). Methods We performed a retrospective review of outpatient CT and MRI appointments at a quaternary academic medical center and affiliated outpatient facilities during a 12-month period. Missed appointments not rescheduled in advance were classified as IMCOs. Appropriateness criteria scores and demographics were also obtained. Univariate and multivariate analyses were performed to determine if demographic and socioeconomic factors were predictive of IMCOs. Results Overall, 57,847 patients met inclusion criteria, representing 89,943 scheduled unique imaging appointments of which 5,840 (6.1%) were IMCOs; 0.8% of IMCO appointments had low appropriateness scores compared with 1.2% of completed appointments ( P P Conclusions Race and socioeconomic status are independent predictors of IMCOs. In efforts to enhance patient engagement, radiologists should be aware of the impact of race and socioeconomic status on access to clinically appropriate advanced diagnostic imaging.

Predicting No-Shows in Radiology Using Regression Modeling of Data Available in the Electronic Medical Record

Abstract: Purpose To test whether data elements available in the electronic medical record (EMR) can be effectively leveraged to predict failure to attend a scheduled radiology examination. Materials and Methods Using data from a large academic medical center, we identified all patients with a diagnostic imaging examination scheduled from January 1, 2016, to April 1, 2016, and determined whether the patient successfully attended the examination. Demographic, clinical, and health services utilization variables available in the EMR potentially relevant to examination attendance were recorded for each patient. We used descriptive statistics and logistic regression models to test whether these data elements could predict failure to attend a scheduled radiology examination. The predictive accuracy of the regression models were determined by calculating the area under the receiver operator curve. Results Among the 54,652 patient appointments with radiology examinations scheduled during the study period, 6.5% were no-shows. No-show rates were highest for the modalities of mammography and CT and lowest for PET and MRI. Logistic regression indicated that 16 of the 27 demographic, clinical, and health services utilization factors were significantly associated with failure to attend a scheduled radiology examination ( P ≤ .05). Stepwise logistic regression analysis demonstrated that previous no-shows, days between scheduling and appointments, modality type, and insurance type were most strongly predictive of no-show. A model considering all 16 data elements had good ability to predict radiology no-shows (area under the receiver operator curve = 0.753). The predictive ability was similar or improved when these models were analyzed by modality. Conclusion Patient and examination information readily available in the EMR can be successfully used to predict radiology no-shows. Moving forward, this information can be proactively leveraged to identify patients who might benefit from additional patient engagement through appointment reminders or other targeted interventions to avoid no-shows.

Fundamentals of Queueing Theory

Abstract: Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

Abstract: Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

The Essential Physics Of Medical Imaging

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Current Applications and Future Impact of Machine Learning in Radiology.

Abstract: Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed. © RSNA, 2018