Inter-scanner comparability of Z-scores for native myocardial T1 and T2 mapping.

BACKGROUND: Cardiovascular Magnetic Resonance (CMR) native T1 and T2 mapping serve as robust, contrast-agent-free diagnostic tools, but hardware- and software-specific sources of variability limit the generalizability of data across CMR platforms, consequently limiting the interpretability of patient-specific parametric data. Z-scores are used to describe the relationship of observed values to the mean results as obtained in a sufficiently large normal sample. They have been successfully used to describe the severity of quantifiable abnormalities in medicine, specifically in children and adolescents. The objective of this study was to observe whether z-scores can improve the comparability of T1 and T2 mapping values across CMR scanners, field strengths, and sequences from different vendors in the same participant rather than different participants (as seen in previous studies). METHODS: Fifty-one healthy volunteers (26 men/25 women, mean age = 43 ± 13.51) underwent three CMR exams on three different scanners, using a Modified Look-Locker Inversion Recovery (MOLLI) 5-(3)- 3 sequence to quantify myocardial T1. For T2 mapping, a True Fast Imaging with steady-state free precession (TRUFI) sequence was used on a 3 T Skyra™ (Siemens), and a T2 Fast Spin Echo (FSE) sequence was used on 1.5 T Artist™ (GE) and 3.0 T Premier™ (GE) scanners. The averages of basal and mid-ventricular short axis slices were used to derive means and standard deviations of global mapping values. We used intra-class comparisons (ICC), repeated measures ANOVA, and paired Student's t-tests for statistical analyses. RESULTS: There was a significant improvement in intra-subject comparability of T1 (ICC of 0.11 (95% CI= -0.018, -0.332) vs 0.78 (95% CI= 0.650, 0.866)) and T2 (ICC of 0.35 (95% CI= -0.053, 0.652) vs 0.83 (95% CI= 0.726, 0.898)) when using z-scores across all three scanners. While the absolute global T1 and T2 values showed a statistically significant difference between scanners (p < 0.001), no such differences were identified using z-scores (T1z: p = 0.771; T2z: p = 0.985). Furthermore, when images were not corrected for motion, T1 z-scores showed significant inter-scanner variability (p < 0.001), resolved by motion correction. CONCLUSION: Employing z-scores for reporting myocardial T1 and T2 removes the variation of quantitative mapping results across different MRI systems and field strengths, improving the clinical utility of myocardial tissue characterization in patients with suspected myocardial disease.

Prioritization of head and neck cancer patient care during the COVID-19 pandemic: a retrospective cohort study.

BACKGROUND: The COVID-19 pandemic placed considerable strain on the healthcare system, leading to the re-allocation of resources and implementation of new practice guidelines. The objective of this study is to assess the impact of COVID-19 guideline modifications on head and neck cancer (HNC) care at two tertiary care centers in Canada. METHODS: A retrospective cohort study was conducted. HNC patients seen at two tertiary care centers before and after the onset of the COVID-19 pandemic (pre-pandemic: July 1st, 2019, to February 29th, 2020; pandemic: March 1st, 2020, to October 31st, 2020) were included. The pre-pandemic and pandemic cohorts were compared according to patient and tumor characteristics, duration of HNC workup, and treatment type and duration. Mean differences in cancer care wait times, including time to diagnosis, tumor board, and treatment as well as total treatment package time and postoperative hospital stay were compared between cohorts. Univariate and multivariate analyses were used to compare characteristics and outcomes between cohorts. RESULTS: Pre-pandemic (n = 132) and pandemic (n = 133) patients did not differ significantly in sex, age, habits, or tumor characteristics. The percentage of patients who received surgery only, chemo/radiotherapy (CXRT) only, and surgery plus adjuvant CXRT did not differ significantly between cohorts. Pandemic patients experienced a significant time reduction compared to pre-pandemic patients with regards to the date first seen by a HNC service until start of treatment ([Formula: see text] = 48.7 and 76.6 days respectively; p = .0001), the date first seen by a HNC service until first presentation at tumor board ([Formula: see text] = 25.1 and 38 days respectively; p = .001), mean total package time for patients who received surgery only ([Formula: see text] = 3.7 and 9.0 days respectively; p = .017), and mean total package time for patients who received surgery plus adjuvant CXRT ([Formula: see text] = 80.2 and 112.7 days respectively; p = .035). CONCLUSION: The time to treatment was significantly reduced during the COVID-19 pandemic as compared to pre-pandemic. This transparent model of patient-centered operative-room prioritization can serve as a model for improving resource allocation and efficiency of HNC care during emergency and non-emergency scenarios.

DeepClassRooms: a deep learning based digital twin framework for on-campus class rooms.

A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart classrooms to enhance faculty efficiency based on accumulated learning outcomes and interests. Smart classroom boards, audio-visual aids, and multimedia are directly related to the Smart classroom environment. Along with these facilities, more effort is required to monitor and analyze students' outcomes, teachers' performance, attendance records, and contents delivery in on-campus classrooms. One can achieve more improvement in quality teaching and learning outcomes by developing digital twins in on-campus classrooms. In this article, we have proposed DeepClass-Rooms, a digital twin framework for attendance and course contents monitoring for the public sector schools of Punjab, Pakistan. DeepClassRooms is cost-effective and requires RFID readers and high-edge computing devices at the Fog layer for attendance monitoring and content matching, using convolution neural network for on-campus and online classes.

Emerging Applications of Extracardiac Ultrasound in Critically Ill Cardiac Patients.

Point-of-care ultrasound has evolved as an invaluable diagnostic modality and procedural guidance tool in the care of critically ill cardiac patients. Beyond focused cardiac ultrasound, additional extracardiac ultrasound modalities may provide important information at the bedside. In addition to new uses of existing modalities, such as pulsed-wave Doppler ultrasound, the development of new applications is fostered by the implementation of additional features in mid-range ultrasound machines commonly acquired for intensive care units, such as tissue elastography, speckle tracking, and contrast-enhanced ultrasound quantification software. This review explores several areas in which ultrasound imaging technology may transform care in the future. First, we review how lung ultrasound in mechanically ventilated patients can enable the personalization of ventilator parameters and help to liberate them from mechanical ventilation. Second, we review the role of venous Doppler in the assessment of organ congestion and how tissue elastography may complement this application. Finally, we explore how contrast-enhanced ultrasound could be used to assess changes in organ perfusion.

Simultaneous multi-parametric acquisition and reconstruction techniques in cardiac magnetic resonance imaging: Basic concepts and status of clinical development.

Simultaneous multi-parametric acquisition and reconstruction techniques (SMART) are gaining attention for their potential to overcome some of cardiovascular magnetic resonance imaging's (CMR) clinical limitations. The major advantages of SMART lie within their ability to simultaneously capture multiple "features" such as cardiac motion, respiratory motion, T1/T2 relaxation. This review aims to summarize the overarching theory of SMART, describing key concepts that many of these techniques share to produce co-registered, high quality CMR images in less time and with less requirements for specialized personnel. Further, this review provides an overview of the recent developments in the field of SMART by describing how they work, the parameters they can acquire, their status of clinical testing and validation, and by providing examples for how their use can improve the current state of clinical CMR workflows. Many of the SMART are in early phases of development and testing, thus larger scale, controlled trials are needed to evaluate their use in clinical setting and with different cardiac pathologies.

COVID-19 Patient Count Prediction Using LSTM.

In December 2019, a pandemic named COVID-19 broke out in Wuhan, China, and in a few weeks, it spread to more than 200 countries worldwide. Every country infected with the disease started taking necessary measures to stop the spread and provide the best possible medical facilities to infected patients and take precautionary measures to control the spread. As the infection spread was exponential, there arose a need to model infection spread patterns to estimate the patient volume computationally. Such patients' estimation is the key to the necessary actions that local governments may take to counter the spread, control hospital load, and resource allocations. This article has used long short-term memory (LSTM) to predict the volume of COVID-19 patients in Pakistan. LSTM is a particular type of recurrent neural network (RNN) used for classification, prediction, and regression tasks. We have trained the RNN model on Covid-19 data (March 2020 to May 2020) of Pakistan and predict the Covid-19 Percentage of Positive Patients for June 2020. Finally, we have calculated the mean absolute percentage error (MAPE) to find the model's prediction effectiveness on different LSTM units, batch size, and epochs. Predicted patients are also compared with a prediction model for the same duration, and results revealed that the predicted patients' count of the proposed model is much closer to the actual patient count.