Predicting health crises from early warning signs in patient medical records.

The COVID-19 global pandemic has caused unprecedented worldwide changes in healthcare delivery. While containment and mitigation approaches have been intensified, the progressive increase in the number of cases has overwhelmed health systems globally, highlighting the need for anticipation and prediction to be the basis of an efficient response system. This study demonstrates the role of population health metrics as early warning signs of future health crises.

Human knowledge models: Learning applied knowledge from the data.

Artificial intelligence and machine learning have demonstrated remarkable results in science and applied work. However, present AI models, developed to be run on computers but used in human-driven applications, create a visible disconnect between AI forms of processing and human ways of discovering and using knowledge. In this work, we introduce a new concept of "Human Knowledge Models" (HKMs), designed to reproduce human computational abilities.

Temporal quality degradation in AI models.

As AI models continue to advance into many real-life applications, their ability to maintain reliable quality over time becomes increasingly important. The principal challenge in this task stems from the very nature of current machine learning models, dependent on the data as it was at the time of training. In this study, we present the first analysis of AI "aging": the complex, multifaceted phenomenon of AI model quality degradation as more time passes since the last model training cycle.

Correction: An alternative to the black box: Strategy learning.

[This corrects the article DOI: 10.1371/journal.pone.

An alternative to the black box: Strategy learning.

In virtually any practical field or application, discovering and implementing near-optimal decision strategies is essential for achieving desired outcomes. Workflow planning is one of the most common and important problems of this kind, as sub-optimal decision-making may create bottlenecks and delays that decrease efficiency and increase costs. Recently, machine learning has been used to attack this problem, but unfortunately, most proposed solutions are "black box" algorithms with underlying logic unclear to humans.

COVID-19: Recovery Models for Radiology Departments.

The coronavirus disease 2019 (COVID-19) pandemic has greatly affected demand for imaging services, with marked reductions in demand for elective imaging and image-guided interventional procedures. To guide radiology planning and recovery from this unprecedented impact, three recovery models were developed to predict imaging volume over the course of the COVID-19 pandemic: (1) a long-term volume model with three scenarios based on prior disease outbreaks and other historical analogues, to aid in long-term planning when the pandemic was just beginning; (2) a short-term volume model based on the supply-demand approach, leveraging increasingly available COVID-19 data points to predict examination volume on a week-to-week basis; and (3) a next-wave model to estimate the impact from future COVID-19 surges. The authors present these models as techniques that can be used at any stage in an unpredictable pandemic timeline.

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

Artificial intelligence (AI) is becoming increasingly present in radiology and health care. This expansion is driven by the principal AI strengths: automation, accuracy, and objectivity. However, as radiology AI matures to become fully integrated into the daily radiology routine, it needs to go beyond replicating static models, toward discovering new knowledge from the data and environments around it.

Imaging Volume Trends and Recovery During the COVID-19 Pandemic: A Comparative Analysis Between a Large Urban Academic Hospital and Its Affiliated Imaging Centers.

Rationale And Objectives: While affiliated imaging centers play an important role in healthcare systems, little is known of how their operations are impacted by the COVID-19 pandemic. Our goal was to investigate imaging volume trends during the pandemic at our large academic hospital compared to the affiliated imaging centers.

Materials And Methods: This was a descriptive retrospective study of imaging volume from an academic hospital (main hospital campus) and its affiliated imaging centers from January 1 through May 21, 2020.

Modeling workflows: Identifying the most predictive features in healthcare operational processes.

Limited resources and increased patient flow highlight the importance of optimizing healthcare operational systems to improve patient care. Accurate prediction of exam volumes, workflow surges and, most notably, patient delay and wait times are known to have significant impact on quality of care and patient satisfaction. The main objective of this work was to investigate the choice of different operational features to achieve (1) more accurate and concise process models and (2) more effective interventions.

Modeling Human Perception of Image Quality.

Humans can determine image quality instantly and intuitively, but the mechanism of human perception of image quality is unknown. The purpose of this work was to identify the most important quantitative metrics responsible for the human perception of digital image quality. Digital images from two different datasets-CT tomography (MedSet) and scenic photographs of trees (TreeSet)-were presented in random pairs to unbiased human viewers.

Current Applications and Future Impact of Machine Learning in Radiology.

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.

Machine Learning for Predicting Patient Wait Times and Appointment Delays.

Being able to accurately predict waiting times and scheduled appointment delays can increase patient satisfaction and enable staff members to more accurately assess and respond to patient flow. In this work, the authors studied the applicability of machine learning models to predict waiting times at a walk-in radiology facility (radiography) and delay times at scheduled radiology facilities (CT, MRI, and ultrasound). In the proposed models, a variety of predictors derived from data available in the radiology information system were used to predict waiting or delay times.

Improving Radiology Workflow with Automated Examination Tracking and Alerts.

The modern radiology workflow is a production line where imaging examinations pass in sequence through many steps. In busy clinical environments, even a minor delay in any step can propagate through the system and significantly lengthen the examination process. This is particularly true for the tasks delegated to the human operators, who may be distracted or stressed.

How Secure Is Your Radiology Department? Mapping Digital Radiology Adoption and Security Worldwide.

Objective: Despite the long history of digital radiology, one of its most critical aspects--information security--still remains extremely underdeveloped and poorly standardized. To study the current state of radiology security, we explored the worldwide security of medical image archives.

Materials And Methods: Using the DICOM data-transmitting standard, we implemented a highly parallel application to scan the entire World Wide Web of networked computers and devices, locating open and unprotected radiology servers.

Can We Predict Patient Wait Time?

Purpose: The importance of patient wait-time management and predictability can hardly be overestimated: For most hospitals, it is the patient queues that drive and define every bit of clinical workflow. The objective of this work was to study the predictability of patient wait time and identify its most influential predictors.

Methods: To solve this problem, we developed a comprehensive list of 25 wait-related parameters, suggested in earlier work and observed in our own experiments.

Digital perfusion phantoms for visual perfusion validation.

Objective: Despite the increasingly broad use of perfusion applications, we still have no generally accessible means for their verification: The common sense of perfusion maps and "bona fides" of perfusion software vendors remain the only grounds for acceptance. Thus, perfusion applications are one of a very few clinical tools considerably lacking practical objective hands-on validation.

Materials And Methods: To solve this problem, we introduce digital perfusion phantoms (DPPs)--numerically simulated DICOM image sequences specifically designed to have known perfusion maps with simple visual patterns.

Perfusion linearity and its applications in perfusion algorithm analysis.

Perfusion analysis computes blood flow parameters (blood volume, blood flow, and mean transit time) from the observed flow of a contrast agent passing through the patient's vascular system. Perfusion deconvolution has been widely accepted as the principal numerical tool for perfusion analysis, and is used routinely in clinical applications. The extensive use of perfusion in clinical decision-making makes numerical stability and robustness of perfusion computations vital for accurate diagnostics and patient safety.

Body tumor CT perfusion protocols: optimization of acquisition scan parameters in a rat tumor model.

Purpose: To evaluate the effects of total scanning time (TST), interscan delay (ISD), inclusion of image at peak vascular enhancement (IPVE), and selection of the input function vessel on the accuracy of tumor blood flow (BF) calculation with computed tomography (CT) in an animal model.

Materials And Methods: All animal protocols and experiments were approved by the institutional animal care and use committee prior to study initiation. After injection of 0.

The fast automatic algorithm for correction of MR bias field.

Purpose: To develop a method for efficient automatic correction of slow-varying nonuniformity in MR images.

Materials And Methods: The original MR image is represented by a piecewise constant function, and the bias (nonuniformity) field of an MR image is modeled as multiplicative and slow varying, which permits to approximate it with a low-order polynomial basis in a "log-domain." The basis coefficients are determined by comparing partial derivatives of the modeled bias field with the original image.