scholarly journals Intraoperative Perfusion Assessment in Enhanced Reality Using Quantitative Optical Imaging: An Experimental Study in a Pancreatic Partial Ischemia Model

Diagnostics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 93
Author(s):  
Taiga Wakabayashi ◽  
Manuel Barberio ◽  
Takeshi Urade ◽  
Raoul Pop ◽  
Emilie Seyller ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Tissue Perfusion ◽  
Computational Imaging ◽  
Imaging Modalities ◽  
Tissue Oxygen Saturation ◽  
Pancreatic Stump ◽  
Pancreatic Ischemia ◽  
Perfusion Quantification ◽  
Enhanced Reality

To reduce the risk of pancreatic fistula after pancreatectomy, a satisfactory blood flow at the pancreatic stump is considered crucial. Our group has developed and validated a real-time computational imaging analysis of tissue perfusion, using fluorescence imaging, the fluorescence-based enhanced reality (FLER). Hyperspectral imaging (HSI) is another emerging technology, which provides tissue-specific spectral signatures, allowing for perfusion quantification. Both imaging modalities were employed to estimate perfusion in a porcine model of partial pancreatic ischemia. Perfusion quantification was assessed using the metrics of both imaging modalities (slope of the time to reach maximum fluorescence intensity and tissue oxygen saturation (StO2), for FLER and HSI, respectively). We found that the HSI-StO2 and the FLER slope were statistically correlated using the Spearman analysis (R = 0.697; p = 0.013). Local capillary lactate values were statistically correlated to the HSI-StO2 and to the FLER slope (R = −0.88; p < 0.001 and R = −0.608; p = 0.0074). HSI-based and FLER-based lactate prediction models had statistically similar predictive abilities (p = 0.112). Both modalities are promising to assess real-time pancreatic perfusion. Clinical translation in human pancreatic surgery is currently underway.

scholarly journals Detecting Microsurgical Complications with ViOptix Tissue Oximetry in a Pediatric Myocutaneous Free Flap: Case Presentation and Literature Review

2018 ◽  
Vol 03 (01) ◽  
pp. e8-e12
Author(s):  
Steven Kozusko ◽  
Uzoma Gbulie
Keyword(s):  
Real Time ◽  
Free Flap ◽  
Near Infrared ◽  
Clinical Symptoms ◽  
Tissue Perfusion ◽  
Venous Occlusion ◽  
Tissue Oxygen Saturation ◽  
Real Time Monitoring ◽  
Flap Monitoring ◽  
Tissue Oximetry

Background Microvascular compromise from arterial or venous occlusion is a common cause of free flap failure. The salvage rate following a microvascular compromise is dependent on detecting the problem early and intervening quickly. Methods The ViOptix tissue oximeter measures tissue oxygen saturation using the near-infrared spectroscopy technology. The ViOptix device has an alarm capability to warn of potential compromise to tissue perfusion. The tissue oximetry readings are visible on the bedside monitor and are relayed to a webpage link, which is accessible on a personal computer or mobile device, allowing real-time monitoring. This article presents a case where real-time monitoring allowed almost immediate detection of inadvertent pedicle compromise allowing flap salvage by repositioning without surgical intervention. Results In the case presented, the patient's nurse inadvertently positioned a pillow under the location of the vascular pedicle likely causing microvascular compression. The ViOptix reading dropped and for this reason the nurse contacted the Plastic Surgery team. The drop was confirmed remotely and the flap was urgently evaluated in person. Once the pillow was removed, the ViOptix readings normalized and Doppler signals strengthened in the flap. Discussion While tissue oximetry monitoring does not by itself ensure flap survival, it provides critical information than conventional flap monitoring would allow giving the microsurgeon the opportunity to make a quicker decision. ViOptix tissue oximeters are able to detect vascular compromise even before conventional clinical symptoms are present. Alas in several cases by the time clinical symptoms develop the flap may be beyond salvage.

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

scholarly journals Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yaghoub Dabiri ◽  
Alex Van der Velden ◽  
Kevin L. Sack ◽  
Jenny S. Choy ◽  
Julius M. Guccione ◽  
...  
Keyword(s):  
Real Time ◽  
Material Properties ◽  
In Silico ◽  
Prediction Models ◽  
Heart Function ◽  
Active Material ◽  
Experimental Studies ◽  
Left Ventricular ◽  
Feed Forward ◽  
Ventricular Mechanics

AbstractAn understanding of left ventricle (LV) mechanics is fundamental for designing better preventive, diagnostic, and treatment strategies for improved heart function. Because of the costs of clinical and experimental studies to treat and understand heart function, respectively, in-silico models play an important role. Finite element (FE) models, which have been used to create in-silico LV models for different cardiac health and disease conditions, as well as cardiac device design, are time-consuming and require powerful computational resources, which limits their use when real-time results are needed. As an alternative, we sought to use deep learning (DL) for LV in-silico modeling. We used 80 four-chamber heart FE models for feed forward, as well as recurrent neural network (RNN) with long short-term memory (LSTM) models for LV pressure and volume. We used 120 LV-only FE models for training LV stress predictions. The active material properties of the myocardium and time were features for the LV pressure and volume training, and passive material properties and element centroid coordinates were features of the LV stress prediction models. For six test FE models, the DL error for LV volume was 1.599 ± 1.227 ml, and the error for pressure was 1.257 ± 0.488 mmHg; for 20 LV FE test examples, the mean absolute errors were, respectively, 0.179 ± 0.050 for myofiber, 0.049 ± 0.017 for cross-fiber, and 0.039 ± 0.011 kPa for shear stress. After training, the DL runtime was in the order of seconds whereas equivalent FE runtime was in the order of several hours (pressure and volume) or 20 min (stress). We conclude that using DL, LV in-silico simulations can be provided for applications requiring real-time results.

scholarly journals Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS

10.2196/30022 ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. e30022
Author(s):  
Ann Corneille Monahan ◽  
Sue S Feldman
Keyword(s):  
Emergency Department ◽  
Hospital Admission ◽  
Real Time ◽  
Prediction Models ◽  
Assessment Tool ◽  
Predictive Performance ◽  
Patient Data ◽  
Adult Patients ◽  
Emergency Department Crowding ◽  
Exit Block

Background Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding. Objective The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding. Methods We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies. Results Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments. Conclusions There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.

scholarly journals Adding to Fire Fighter Safety by Including Real-Time Radar Data in Short-Range Forecasts of Thunderstorm-Induced Wind Shifts

Fire ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 55
Author(s):  
Gary L. Achtemeier ◽  
Scott L. Goodrick
Keyword(s):  
Real Time ◽  
Short Range ◽  
Prediction Models ◽  
Weather Prediction ◽  
Radar Data ◽  
Mitigation Strategies ◽  
Density Currents ◽  
Gust Fronts ◽  
Gust Front ◽  
Range Forecasts

Abrupt changes in wind direction and speed caused by thunderstorm-generated gust fronts can, within a few seconds, transform slow-spreading low-intensity flanking fires into high-intensity head fires. Flame heights and spread rates can more than double. Fire mitigation strategies are challenged and the safety of fire crews is put at risk. We propose a class of numerical weather prediction models that incorporate real-time radar data and which can provide fire response units with images of accurate very short-range forecasts of gust front locations and intensities. Real-time weather radar data are coupled with a wind model that simulates density currents over complex terrain. Then two convective systems from formation and merger to gust front arrival at the location of a wildfire at Yarnell, Arizona, in 2013 are simulated. We present images of maps showing the progress of the gust fronts toward the fire. Such images can be transmitted to fire crews to assist decision-making. We conclude, therefore, that very short-range gust front prediction models that incorporate real-time radar data show promise as a means of predicting the critical weather information on gust front propagation for fire operations, and that such tools warrant further study.

scholarly journals A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea

2020 ◽  
Author(s):  
Ben J. Brintz ◽  
Benjamin Haaland ◽  
Joel Howard ◽  
Dennis L. Chao ◽  
Joshua L. Proctor ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Predictive Performance ◽  
Data Availability ◽  
Clinical Prediction ◽  
Multiple Sources ◽  
Statistical Framework ◽  
Multiple Data ◽  
Post Test ◽  
The Individual

AbstractTraditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where “pre-test” epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics.

scholarly journals Clinical prediction models for hospital falls: a scoping review protocol

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e051047
Author(s):  
Rex Parsons ◽  
Susanna M Cramb ◽  
Steven M McPhail
Keyword(s):  
Real Time ◽  
Scoping Review ◽  
Fall Risk ◽  
Prediction Models ◽  
Reporting Quality ◽  
Reference List ◽  
Fall Prediction ◽  
Scoping Reviews ◽  
Ethical Approval ◽  
Meta Analyses

IntroductionFalls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs. Approaches to assess patients’ fall risk have been implemented in hospitals internationally, ranging from brief screening questions to multifactorial risk assessments and complex prediction models, despite a lack of clear evidence of effect in reducing falls in acute hospital environments. The increasing digitisation of hospital systems provides new opportunities to understand and predict falls using routinely recorded data, with potential to integrate fall prediction models into real-time or near-real-time computerised decision support for clinical teams seeking to mitigate fall risk. However, the use of non-traditional approaches to fall risk prediction, including machine learning using integrated electronic medical records, has not yet been reviewed relative to more traditional fall prediction models. This scoping review will summarise methodologies used to develop existing hospital fall prediction models, including reporting quality assessment.Methods and analysisThis scoping review will follow the Arksey and O’Malley framework and its recent advances, and will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews recommendations. Four electronic databases (CINAHL via EBSCOhost, PubMed, IEEE Xplore and Embase) will be initially searched for studies up to 12 November 2020, and searches may be updated prior to final reporting. Additional studies will be identified by reference list review and citation analysis of included studies. No restriction will be placed on the date or language of identified studies. Screening of search results and extraction of data will be performed by two independent reviewers. Reporting quality will be assessed by the adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis.Ethics and disseminationEthical approval is not required for this study. Findings will be disseminated through peer-reviewed publication and scientific conferences.

scholarly journals Similarity-based error prediction approach for real-time inflow forecasting

2013 ◽  
Vol 45 (4-5) ◽  
pp. 589-602 ◽  
Author(s):  
Mahmood Akbari ◽  
Abbas Afshar
Keyword(s):  
Real Time ◽  
Nearest Neighbor ◽  
Prediction Models ◽  
Error Prediction ◽  
K Nearest Neighbor ◽  
Forecasting Models ◽  
Main Challenge ◽  
Inflow Forecasting ◽  
Artificial Neural Network Ann ◽  
Prediction Approach

Regardless of extensive researches on hydrologic forecasting models, the issue of updating the outputs from forecasting models has remained a main challenge. Most of the existing output updating methods are mainly based on the presence of persistence in the errors. This paper presents an alternative approach to updating the outputs from forecasting models in order to produce more accurate forecast results. The approach uses the concept of the similarity in errors for error prediction. The K nearest neighbor (KNN) algorithm is employed as a similarity-based error prediction model and improvements are made by new data, and two other forms of the KNN are developed in this study. The KNN models are applied for the error prediction of flow forecasting models in two catchments and the updated flows are compared to those of persistence-based methods such as autoregressive (AR) and artificial neural network (ANN) models. The results show that the similarity-based error prediction models can be recognized as an efficient alternative for real-time inflow forecasting, especially where the persistence in the error series of flow forecasting model is relatively low.

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