Showing papers by "Philips published in 2021"

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Abstract: Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death. A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

Daily energy expenditure through the human life course

Abstract: Total daily energy expenditure ("total expenditure") reflects daily energy needs and is a critical variable in human health and physiology, but its trajectory over the life course is poorly studied. We analyzed a large, diverse database of total expenditure measured by the doubly labeled water method for males and females aged 8 days to 95 years. Total expenditure increased with fat-free mass in a power-law manner, with four distinct life stages. Fat-free mass-adjusted expenditure accelerates rapidly in neonates to ~50% above adult values at ~1 year; declines slowly to adult levels by ~20 years; remains stable in adulthood (20 to 60 years), even during pregnancy; then declines in older adults. These changes shed light on human development and aging and should help shape nutrition and health strategies across the life span.

MPEG Immersive Video Coding Standard

Abstract: This article introduces the ISO/IEC MPEG Immersive Video (MIV) standard, MPEG-I Part 12, which is undergoing standardization. The draft MIV standard provides support for viewing immersive volumetric content captured by multiple cameras with six degrees of freedom (6DoF) within a viewing space that is determined by the camera arrangement in the capture rig. The bitstream format and decoding processes of the draft specification along with aspects of the Test Model for Immersive Video (TMIV) reference software encoder, decoder, and renderer are described. The use cases, test conditions, quality assessment methods, and experimental results are provided. In the TMIV, multiple texture and geometry views are coded as atlases of patches using a legacy 2-D video codec, while optimizing for bitrate, pixel rate, and quality. The design of the bitstream format and decoder is based on the visual volumetric video-based coding (V3C) and video-based point cloud compression (V-PCC) standard, MPEG-I Part 5.

Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI

Abstract: One of the most common types of cancer in men is prostate cancer (PCa). Biopsies guided by bi-parametric magnetic resonance imaging (MRI) can aid PCa diagnosis. Previous works have mostly focused on either detection or classification of PCa from MRI. In this work, however, we present a neural network that simultaneously detects and grades cancer tissue in an end-to-end fashion. This is more clinically relevant than the classification goal of the ProstateX-2 challenge. We used the dataset of this challenge for training and testing. We use a 2D U-Net with MRI slices as input and lesion segmentation maps that encode the Gleason Grade Group (GGG), a measure for cancer aggressiveness, as output. We propose a method for encoding the GGG in the model target that takes advantage of the fact that the classes are ordinal. Furthermore, we evaluate methods for incorporating prostate zone segmentations as prior information, and ensembling techniques. The model scored a voxel-wise weighted kappa of $0.446 \pm 0.082$ and a Dice similarity coefficient for segmenting clinically significant cancer of $0.370 \pm 0.046$ , obtained using 5-fold cross-validation. The lesion-wise weighted kappa on the ProstateX-2 challenge test set was $0.13 \pm 0.27$ . We show that our proposed model target outperforms standard multiclass classification and multi-label ordinal regression. Additionally, we present a comparison of methods for further improvement of the model performance.

X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: a diagnostic accuracy study.

Abstract: BACKGROUND Although advanced medical imaging technologies give detailed diagnostic information, a low-dose, fast, and inexpensive option for early detection of respiratory diseases and follow-ups is still lacking. The novel method of x-ray dark-field chest imaging might fill this gap but has not yet been studied in living humans. Enabling the assessment of microstructural changes in lung parenchyma, this technique presents a more sensitive alternative to conventional chest x-rays, and yet requires only a fraction of the dose applied in CT. We studied the application of this technique to assess pulmonary emphysema in patients with chronic obstructive pulmonary disease (COPD). METHODS In this diagnostic accuracy study, we designed and built a novel dark-field chest x-ray system (Technical University of Munich, Munich, Germany)-which is also capable of simultaneously acquiring a conventional thorax radiograph (7 s, 0·035 mSv effective dose). Patients who had undergone a medically indicated chest CT were recruited from the department of Radiology and Pneumology of our site (Klinikum rechts der Isar, Technical University of Munich, Munich, Germany). Patients with pulmonary pathologies, or conditions other than COPD, that might influence lung parenchyma were excluded. For patients with different disease stages of pulmonary emphysema, x-ray dark-field images and CT images were acquired and visually assessed by five readers. Pulmonary function tests (spirometry and body plethysmography) were performed for every patient and for a subgroup of patients the measurement of diffusion capacity was performed. Individual patient datasets were statistically evaluated using correlation testing, rank-based analysis of variance, and pair-wise post-hoc comparison. FINDINGS Between October, 2018 and December, 2019 we enrolled 77 patients. Compared with CT-based parameters (quantitative emphysema ρ=-0·27, p=0·089 and visual emphysema ρ=-0·45, p=0·0028), the dark-field signal (ρ=0·62, p<0·0001) yields a stronger correlation with lung diffusion capacity in the evaluated cohort. Emphysema assessment based on dark-field chest x-ray features yields consistent conclusions with findings from visual CT image interpretation and shows improved diagnostic performance than conventional clinical tests characterising emphysema. Pair-wise comparison of corresponding test parameters between adjacent visual emphysema severity groups (CT-based, reference standard) showed higher effect sizes. The mean effect size over the group comparisons (absent-trace, trace-mild, mild-moderate, and moderate-confluent or advanced destructive visual emphysema grades) for the COPD assessment test score is 0·21, for forced expiratory volume in 1 s (FEV1)/functional vital capacity is 0·25, for FEV1% of predicted is 0·23, for residual volume % of predicted is 0·24, for CT emphysema index is 0·35, for dark-field signal homogeneity within lungs is 0·38, for dark-field signal texture within lungs is 0·38, and for dark-field-based emphysema severity is 0·42. INTERPRETATION X-ray dark-field chest imaging allows the diagnosis of pulmonary emphysema in patients with COPD because this technique provides relevant information representing the structural condition of lung parenchyma. This technique might offer a low radiation dose alternative to CT in COPD and potentially other lung disorders. FUNDING European Research Council, Deutsche Forschungsgemeinschaft, Royal Philips, and Karlsruhe Nano Micro Facility.

Analysis of Discrepancies Between Pulse Oximetry and Arterial Oxygen Saturation Measurements by Race and Ethnicity and Association With Organ Dysfunction and Mortality.

Abstract: Importance Discrepancies in oxygen saturation measured by pulse oximetry (Spo2), when compared with arterial oxygen saturation (Sao2) measured by arterial blood gas (ABG), may differentially affect patients according to race and ethnicity. However, the association of these disparities with health outcomes is unknown. Objective To examine racial and ethnic discrepancies between Sao2 and Spo2 measures and their associations with clinical outcomes. Design, setting, and participants This multicenter, retrospective, cross-sectional study included 3 publicly available electronic health record (EHR) databases (ie, the Electronic Intensive Care Unit-Clinical Research Database and Medical Information Mart for Intensive Care III and IV) as well as Emory Healthcare (2014-2021) and Grady Memorial (2014-2020) databases, spanning 215 hospitals and 382 ICUs. From 141 600 hospital encounters with recorded ABG measurements, 87 971 participants with first ABG measurements and an Spo2 of at least 88% within 5 minutes before the ABG test were included. Exposures Patients with hidden hypoxemia (ie, Spo2 ≥88% but Sao2 Main outcomes and measures Outcomes, stratified by race and ethnicity, were Sao2 for each Spo2, hidden hypoxemia prevalence, initial demographic characteristics (age, sex), clinical outcomes (in-hospital mortality, length of stay), organ dysfunction by scores (Sequential Organ Failure Assessment [SOFA]), and laboratory values (lactate and creatinine levels) before and 24 hours after the ABG measurement. Results The first Spo2-Sao2 pairs from 87 971 patient encounters (27 713 [42.9%] women; mean [SE] age, 62.2 [17.0] years; 1919 [2.3%] Asian patients; 26 032 [29.6%] Black patients; 2397 [2.7%] Hispanic patients, and 57 632 [65.5%] White patients) were analyzed, with 4859 (5.5%) having hidden hypoxemia. Hidden hypoxemia was observed in all subgroups with varying incidence (Black: 1785 [6.8%]; Hispanic: 160 [6.0%]; Asian: 92 [4.8%]; White: 2822 [4.9%]) and was associated with greater organ dysfunction 24 hours after the ABG measurement, as evidenced by higher mean (SE) SOFA scores (7.2 [0.1] vs 6.29 [0.02]) and higher in-hospital mortality (eg, among Black patients: 369 [21.1%] vs 3557 [15.0%]; P Conclusions and relevance In this study, there was greater variability in oxygen saturation levels for a given Spo2 level in patients who self-identified as Black, followed by Hispanic, Asian, and White. Patients with and without hidden hypoxemia were demographically and clinically similar at baseline ABG measurement by SOFA scores, but those with hidden hypoxemia subsequently experienced higher organ dysfunction scores and higher in-hospital mortality.

MYCN-Amplified Neuroblastoma Is Addicted to Iron and Vulnerable to Inhibition of the System Xc-/Glutathione Axis

Abstract: MYCN is amplified in 20% to 25% of neuroblastoma, and MYCN-amplified neuroblastoma contributes to a large percent of pediatric cancer-related deaths. Therapy improvements for this subtype of cancer are a high priority. Here we uncover a MYCN-dependent therapeutic vulnerability in neuroblastoma. Namely, amplified MYCN rewires the cell through expression of key receptors, ultimately enhancing iron influx through increased expression of the iron import transferrin receptor 1. Accumulating iron causes reactive oxygen species (ROS) production, and MYCN-amplified neuroblastomas show enhanced reliance on the system Xc- cystine/glutamate antiporter for ROS detoxification through increased transcription of this receptor. This dependence creates a marked vulnerability to targeting the system Xc-/glutathione (GSH) pathway with ferroptosis inducers. This reliance can be exploited through therapy with FDA-approved rheumatoid arthritis drugs sulfasalazine (SAS) and auranofin: in MYCN-amplified, patient-derived xenograft models, both therapies blocked growth and induced ferroptosis. SAS and auranofin activity was largely mitigated by the ferroptosis inhibitor ferrostatin-1, antioxidants like N-acetyl-L-cysteine, or by the iron scavenger deferoxamine (DFO). DFO reduced auranofin-induced ROS, further linking increased iron capture in MYCN-amplified neuroblastoma to a therapeutic vulnerability to ROS-inducing drugs. These data uncover an oncogene vulnerability to ferroptosis caused by increased iron accumulation and subsequent reliance on the system Xc-/GSH pathway. SIGNIFICANCE: This study shows how MYCN increases intracellular iron levels and subsequent GSH pathway activity and demonstrates the antitumor activity of FDA-approved SAS and auranofin in patient-derived xenograft models of MYCN-amplified neuroblastoma.

Association of Age, Antipsychotic Medication, and Symptom Severity in Schizophrenia With Proton Magnetic Resonance Spectroscopy Brain Glutamate Level: A Mega-analysis of Individual Participant-Level Data.

Abstract: Importance Proton magnetic resonance spectroscopy (1H-MRS) studies indicate that altered brain glutamatergic function may be associated with the pathophysiology of schizophrenia and the response to antipsychotic treatment. However, the association of altered glutamatergic function with clinical and demographic factors is unclear. Objective To assess the associations of age, symptom severity, level of functioning, and antipsychotic treatment with brain glutamatergic metabolites. Data Sources The MEDLINE database was searched to identify journal articles published between January 1, 1980, and June 3, 2020, using the following search terms: MRS or magnetic resonance spectroscopy and (1) schizophrenia or (2) psychosis or (3) UHR or (4) ARMS or (5) ultra-high risk or (6) clinical high risk or (7) genetic high risk or (8) prodrome* or (9) schizoaffective. Authors of 114 1H-MRS studies measuring glutamate (Glu) levels in patients with schizophrenia were contacted between January 2014 and June 2020 and asked to provide individual participant data. Study Selection In total, 45 1H-MRS studies contributed data. Data Extraction and Synthesis Associations of Glu, Glu plus glutamine (Glx), or total creatine plus phosphocreatine levels with age, antipsychotic medication dose, symptom severity, and functioning were assessed using linear mixed models, with study as a random factor. Main Outcomes and Measures Glu, Glx, and Cr values in the medial frontal cortex (MFC) and medial temporal lobe (MTL). Results In total, 42 studies were included, with data for 1251 patients with schizophrenia (mean [SD] age, 30.3 [10.4] years) and 1197 healthy volunteers (mean [SD] age, 27.5 [8.8] years). The MFC Glu (F1,1211.9 = 4.311,P = .04) and Glx (F1,1079.2 = 5.287,P = .02) levels were lower in patients than in healthy volunteers, and although creatine levels appeared lower in patients, the difference was not significant (F1,1395.9 = 3.622,P = .06). In both patients and volunteers, the MFC Glu level was negatively associated with age (Glu to Cr ratio,F1,1522.4 = 47.533,P Conclusions and Relevance Findings from this mega-analysis suggest that lower brain Glu levels in patients with schizophrenia may be associated with antipsychotic medication exposure rather than with greater age-related decline. Higher brain Glu levels may act as a biomarker of illness severity in schizophrenia.

Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images.

Abstract: Purpose Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak. Methods A retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60±17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student's t-test or Mann-Whitney U test. Cohen's kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation. Results Fifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (P = 0.008) and comorbidities (P = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (P = 0.0008 and P = 0.049) or central line (P = 0.003 and P = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities. Conclusion Chest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting.

Ensembling Classical Machine Learning and Deep Learning Approaches for Morbidity Identification From Clinical Notes

Abstract: The past decade has seen an explosion of the amount of digital information generated within the healthcare domain. Digital data exist in the form of images, video, speech, transcripts, electronic health records, clinical records, and free-text. Analysis and interpretation of healthcare data is a daunting task, and it demands a great deal of time, resources, and human effort. In this paper, we focus on the problem of co-morbidity recognition from patient’s clinical records. To this aim, we employ both classical machine learning and deep learning approaches. We use word embeddings and bag-of-words representations, coupled with feature selection techniques. The goal of our work is to develop a classification system to identify whether a certain health condition occurs for a patient by studying his/her past clinical records. In more detail, we have used pre-trained word2vec, domain-trained, GloVe, fastText, and universal sentence encoder embeddings to tackle the classification of sixteen morbidity conditions within clinical records. We have compared the outcomes of classical machine learning and deep learning approaches with the employed feature representation methods and feature selection methods. We present a comprehensive discussion of the performances and behaviour of the employed classical machine learning and deep learning approaches. Finally, we have also used ensemble learning techniques over a large number of combinations of classifiers to improve the single model performance. For our experiments, we used the n2c2 natural language processing research dataset, released by Harvard Medical School. The dataset is in the form of clinical notes that contain patient discharge summaries. Given the unbalancedness of the data and their small size, the experimental results indicate the advantage of the ensemble learning technique with respect to single classifier models. In particular, the ensemble learning technique has slightly improved the performances of single classification models but has greatly reduced the variance of predictions stabilizing the accuracies (i.e., the lower standard deviation in comparison with single classifiers). In real-life scenarios, our work can be employed to identify with high accuracy morbidity conditions of patients by feeding our tool with their current clinical notes. Moreover, other domains where classification is a common problem might benefit from our approach as well.

Delay-Aware Virtual Network Function Placement and Routing in Edge Clouds

Abstract: Mobile Edge Computing (MEC) offers a way to shorten the cloud servicing delay by building the small-scale cloud infrastructures at the network edge, which are in close proximity to the end users. Moreover, Network Function Virtualization (NFV) has been an emerging technology that transforms from traditional dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. Service Function Chaining (SFC) is defined as a chain-ordered set of placed VNFs that handles the traffic of the delivery and control of a specific application. NFV therefore allows to allocate network resources in a more scalable and elastic manner, offer a more efficient and agile management and operation mechanism for network functions and hence can largely reduce the overall costs in MEC. In this paper, we study the problem of how to place VNFs on edge and public clouds and route the traffic among adjacent VNF pairs, such that the maximum link load ratio is minimized and each user's requested delay is satisfied. We consider this problem for both totally ordered SFCs and partially ordered SFCs. We prove that this problem is NP-hard, even for the special case when only one VNF is requested. We subsequently propose an efficient randomized rounding approximation algorithm to solve this problem. Extensive simulation results show that the proposed approximation algorithm can achieve close-to-optimal performance in terms of acceptance ratio and maximum link load ratio.

Chest CT Patterns from Diagnosis to 1 Year of Follow-up in COVID-19.

Abstract: Background The chest CT manifestations of COVID-19 from hospitalization to convalescence after one year are not known. Purpose To assess chest CT manifestations of COVID-19 up to 1 year after symptom onset. Materials and Methods Patients were enrolled if they were admitted to the hospital due to COVID-19 and underwent CT scans during hospitalization at two isolation centers between 27 January and 31 March 2020. In a prospective study, three serial chest CTs were obtained at approximately 3, 7, and 12 months after symptom onset and longitudinally analyzed. The total CT score of pulmonary lobe involvement from 0 to 25 was assessed (score 1-5 for each lobe). Uni-/multi-variable logistic regression tests were performed to explore independent risk factors for residual CT abnormalities after one year. Results 209 study participants (mean age: 49±13 years, 116 women) were evaluated. At 3 months, 61% of participants (128 of 209) had resolution of CT abnormalities; at 12 months, 75% (156 of 209) had resolution. Of chest CT abnormalities that had not resolved, there were residual linear opacities in 25/209 (12%) and multifocal reticular/cystic lesions in 28/209 (13%) participants. Age≥50 years, lymphopenia, and severe/ARDS aggravation were independent risk factors for residual CT abnormalities at one year (odds ratios of 15.9, 18.9, and 43.9, respectively; P<.001, each). In 53 participants with residual CT abnormalities at 12 months, reticular lesions (41 of 53, 77%) and bronchial dilation (39 of 53, 74%) were observed at discharge and were persistent in 53% (28 of 53) and 45% (24 of 53) of participants, respectively. Conclusion One year after COVID-19 diagnosis, chest CT showed abnormal findings in 25% of participants, with 13% showing subpleural reticular/cystic lesions. Older participants with severe COVID-19 or acute respiratory distress syndrome were more likely to develop lung sequelae that persisted at 1 year. See also the editorial by Lee and Wi.

Anomaly Detection in Medical Imaging With Deep Perceptual Autoencoders

Abstract: Anomaly detection is the problem of recognizing abnormal inputs based on the seen examples of normal data. Despite recent advances of deep learning in recognizing image anomalies, these methods still prove incapable of handling complex images, such as those encountered in the medical domain. Barely visible abnormalities in chest X-rays or metastases in lymph nodes on the scans of the pathology slides resemble normal images and are very difficult to detect. To address this problem, we introduce a new powerful method of image anomaly detection. It relies on the classical autoencoder approach with a re-designed training pipeline to handle high-resolution, complex images, and a robust way of computing an image abnormality score. We revisit the very problem statement of fully unsupervised anomaly detection, where no abnormal examples are provided during the model setup. We propose to relax this unrealistic assumption by using a very small number of anomalies of confined variability merely to initiate the search of hyperparameters of the model. We evaluate our solution on two medical datasets containing radiology and digital pathology images, where the state-of-the-art anomaly detection models, originally devised for natural image benchmarks, fail to perform sufficiently well. The proposed approach suggests a new baseline for anomaly detection in medical image analysis tasks.

Generic acquisition protocol for quantitative MRI of the spinal cord.

Abstract: Quantitative spinal cord (SC) magnetic resonance imaging (MRI) presents many challenges, including a lack of standardized imaging protocols. Here we present a prospectively harmonized quantitative MRI protocol, which we refer to as the spine generic protocol, for users of 3T MRI systems from the three main manufacturers: GE, Philips and Siemens. The protocol provides guidance for assessing SC macrostructural and microstructural integrity: T1-weighted and T2-weighted imaging for SC cross-sectional area computation, multi-echo gradient echo for gray matter cross-sectional area, and magnetization transfer and diffusion weighted imaging for assessing white matter microstructure. In a companion paper from the same authors, the spine generic protocol was used to acquire data across 42 centers in 260 healthy subjects. The key details of the spine generic protocol are also available in an open-access document that can be found at https://github.com/spine-generic/protocols . The protocol will serve as a starting point for researchers and clinicians implementing new SC imaging initiatives so that, in the future, inclusion of the SC in neuroimaging protocols will be more common. The protocol could be implemented by any trained MR technician or by a researcher/clinician familiar with MRI acquisition.

Secure training of decision trees with continuous attributes

Abstract: Abstract We apply multiparty computation (MPC) techniques to show, given a database that is secret-shared among multiple mutually distrustful parties, how the parties may obliviously construct a decision tree based on the secret data. We consider data with continuous attributes (i.e., coming from a large domain), and develop a secure version of a learning algorithm similar to the C4.5 or CART algorithms. Previous MPC-based work only focused on decision tree learning with discrete attributes (De Hoogh et al. 2014). Our starting point is to apply an existing generic MPC protocol to a standard decision tree learning algorithm, which we then optimize in several ways. We exploit the fact that even if we allow the data to have continuous values, which a priori might require fixed or floating point representations, the output of the tree learning algorithm only depends on the relative ordering of the data. By obliviously sorting the data we reduce the number of comparisons needed per node to O(N log2N) from the naive O(N2), where N is the number of training records in the dataset, thus making the algorithm feasible for larger datasets. This does however introduce a problem when duplicate values occur in the dataset, but we manage to overcome this problem with a relatively cheap subprotocol. We show a procedure to convert a sorting network into a permutation network of smaller complexity, resulting in a round complexity of O(log N) per layer in the tree. We implement our algorithm in the MP-SPDZ framework and benchmark our implementation for both passive and active three-party computation using arithmetic modulo 264. We apply our implementation to a large scale medical dataset of ≈ 290 000 rows using random forests, and thus demonstrate practical feasibility of using MPC for privacy-preserving machine learning based on decision trees for large datasets.

A standard calculation methodology for human doubly labeled water studies.

Abstract: The doubly labeled water (DLW) method measures total energy expenditure (TEE) in free-living subjects. Several equations are used to convert isotopic data into TEE. Using the International Atomic Energy Agency (IAEA) DLW database (5,756 measurements of adults and children), we show considerable variability is introduced by different equations. The estimated rCO2 is sensitive to the dilution space ratio (DSR) of the two isotopes. Based on performance in validation studies, we propose a new equation based on a new estimate of the mean DSR. The DSR is lower at low body masses (<10 kg). Using data for 1,021 babies and infants, we show that the DSR varies non-linearly with body mass between 0 and 10 kg. Using this relationship to predict DSR from weight provides an equation for rCO2 over this size range that agrees well with indirect calorimetry (average difference 0.64%; SD = 12.2%). We propose adoption of these equations in future studies.

Microbicidal actives with virucidal efficacy against SARS-CoV-2 and other beta- and alpha-coronaviruses and implications for future emerging coronaviruses and other enveloped viruses.

Abstract: Mitigating the risk of acquiring coronaviruses including SARS-CoV-2 requires awareness of the survival of virus on high-touch environmental surfaces (HITES) and skin, and frequent use of targeted microbicides with demonstrated efficacy The data on stability of infectious SARS-CoV-2 on surfaces and in suspension have been put into perspective, as these inform the need for hygiene We evaluated the efficacies of formulated microbicidal actives against alpha- and beta-coronaviruses, including SARS-CoV-2 The coronaviruses SARS-CoV, SARS-CoV-2, human coronavirus 229E, murine hepatitis virus-1, or MERS-CoV were deposited on prototypic HITES or spiked into liquid matrices along with organic soil loads Alcohol-, quaternary ammonium compound-, hydrochloric acid-, organic acid-, p-chloro-m-xylenol-, and sodium hypochlorite-based microbicidal formulations were evaluated per ASTM International and EN standard methodologies All evaluated formulated microbicides inactivated SARS-CoV-2 and other coronaviruses in suspension or on prototypic HITES Virucidal efficacies (≥ 3 to ≥ 6 log10 reduction) were displayed within 30 s to 5 min The virucidal efficacy of a variety of commercially available formulated microbicides against SARS-CoV-2 and other coronaviruses was confirmed These microbicides should be useful for targeted surface and hand hygiene and disinfection of liquids, as part of infection prevention and control for SARS-CoV-2 and emerging mutational variants, and other emerging enveloped viruses

Feasibility of lung imaging with a large field-of-view spectral photon-counting CT system.

Abstract: Purpose The purpose of this study was to characterize the technical capabilities and feasibility of a large field-of-view clinical spectral photon-counting computed tomography (SPCCT) prototype for high-resolution (HR) lung imaging. Materials and methods Measurement of modulation transfer function (MTF) and acquisition of a line pairs phantom were performed. An anthropomorphic lung nodule phantom was scanned with standard (120 kVp, 62 mAs), low (120 kVp, 11 mAs), and ultra-low (80 kVp, 3 mAs) radiation doses. A human volunteer underwent standard (120 kVp, 63 mAs) and low (120 kVp, 11 mAs) dose scans after approval by the ethics committee. HR images were reconstructed with 1024 matrix, 300 mm field of view and 0.25 mm slice thickness using a filtered-back projection (FBP) and two levels of iterative reconstruction (iDose 5 and 9). The conspicuity and sharpness of various lung structures (distal airways, vessels, fissures and proximal bronchial wall), image noise, and overall image quality were independently analyzed by three radiologists and compared to a previous HR lung CT examination of the same volunteer performed with a conventional CT equipped with energy integrating detectors (120 kVp, 10 mAs, FBP). Results Ten percent MTF was measured at 22.3 lp/cm with a cut-off at 31 lp/cm. Up to 28 lp/cm were depicted. While mixed and solid nodules were easily depicted on standard and low-dose phantom images, higher iDose levels and slice thicknesses (1 mm) were needed to visualize ground-glass components on ultra-low-dose images. Standard dose SPCCT images of in vivo lung structures were of greater conspicuity and sharpness, with greater overall image quality, and similar image noise (despite a flux reduction of 23%) to conventional CT images. Low-dose SPCCT images were of greater or similar conspicuity and sharpness, similar overall image quality, and lower but acceptable image noise (despite a flux reduction of 89%). Conclusions A large field-of-view SPCCT prototype demonstrates HR technical capabilities and high image quality for high resolution lung CT in human.

Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation.

Abstract: Volumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicated meningioma deep learning model based on routine magnetic resonance imaging (MRI) data and evaluated its performance for automated tumor segmentation. The MRI datasets included T1-weighted/T2-weighted, T1-weighted contrast-enhanced (T1CE) and FLAIR of 126 patients with intracranial meningiomas (grade I: 97, grade II: 29). For automated segmentation, an established deep learning model architecture (3D deep convolutional neural network, DeepMedic, BioMedIA) operating on all four MR sequences was used. Segmentation included the following two components: (i) contrast-enhancing tumor volume in T1CE and (ii) total lesion volume (union of lesion volume in T1CE and FLAIR, including solid tumor parts and surrounding edema). Preprocessing of imaging data included registration, skull stripping, resampling, and normalization. After training of the deep learning model using manual segmentations by 2 independent readers from 70 patients (training group), the algorithm was evaluated on 56 patients (validation group) by comparing automated to ground truth manual segmentations, which were performed by 2 experienced readers in consensus. Of the 56 meningiomas in the validation group 55 were detected by the deep learning model. In these patients the comparison of the deep learning model and manual segmentations revealed average dice coefficients of 0.91 ± 0.08 for contrast-enhancing tumor volume and 0.82 ± 0.12 for total lesion volume. In the training group, interreader variabilities of the 2 manual readers were 0.92 ± 0.07 for contrast-enhancing tumor and 0.88 ± 0.05 for total lesion volume. Deep learning-based automated segmentation yielded high segmentation accuracy, comparable to manual interreader variability.

Consensus for the management of pancreatic exocrine insufficiency: UK practical guidelines.

Abstract: Introduction Pancreatic exocrine insufficiency is a finding in many conditions, predominantly affecting those with chronic pancreatitis, pancreatic cancer and acute necrotising pancreatitis. Patients with pancreatic exocrine insufficiency can experience gastrointestinal symptoms, maldigestion, malnutrition and adverse effects on quality of life and even survival. There is a need for readily accessible, pragmatic advice for healthcare professionals on the management of pancreatic exocrine insufficiency. Methods and analysis A review of the literature was conducted by a multidisciplinary panel of experts in pancreatology, and recommendations for clinical practice were produced and the strength of the evidence graded. Consensus voting by 48 pancreatic specialists from across the UK took place at the 2019 Annual Meeting of the Pancreatic Society of Great Britain and Ireland annual scientific meeting. Results Recommendations for clinical practice in the diagnosis, initial management, patient education and long term follow up were developed. All recommendations achieved over 85% consensus and are included within these comprehensive guidelines.

A deep transfer learning approach for wearable sleep stage classification with photoplethysmography

Abstract: Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography (PPG) could open the way for better sleep disorder screening and health monitoring. However, PPG is rarely included in large sleep studies with gold-standard sleep annotation from polysomnography. Therefore, training data-intensive state-of-the-art deep neural networks is challenging. In this work a deep recurrent neural network is first trained using a large sleep data set with electrocardiogram (ECG) data (292 participants, 584 recordings) to perform 4-class sleep stage classification (wake, rapid-eye-movement, N1/N2, and N3). A small part of its weights is adapted to a smaller, newer PPG data set (60 healthy participants, 101 recordings) through three variations of transfer learning. Best results (Cohen’s kappa of 0.65 ± 0.11, accuracy of 76.36 ± 7.57%) were achieved with the domain and decision combined transfer learning strategy, significantly outperforming the PPG-trained and ECG-trained baselines. This performance for PPG-based 4-class sleep stage classification is unprecedented in literature, bringing home sleep stage monitoring closer to clinical use. The work demonstrates the merit of transfer learning in developing reliable methods for new sensor technologies by reusing similar, older non-wearable data sets. Further study should evaluate our approach in patients with sleep disorders such as insomnia and sleep apnoea.

Chest CT for rapid triage of patients in multiple emergency departments during COVID-19 epidemic: experience report from a large French university hospital.

Abstract: To assess the diagnostic performances of chest CT for triage of patients in multiple emergency departments during COVID-19 epidemic, in comparison with reverse transcription polymerase chain reaction (RT-PCR) test. From March 3 to April 4, 2020, 694 consecutive patients from three emergency departments of a large university hospital, for which a hospitalization was planned whatever the reasons, i.e., COVID- or non-COVID-related, underwent a chest CT and one or several RT-PCR tests. Chest CTs were rated as “Surely COVID+,” “Possible COVID+,” or “COVID−” by experienced radiologists. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated using the final RT-PCR test as standard of reference. The delays for CT reports and RT-PCR results were recorded and compared. Among the 694 patients, 287 were positive on the final RT-PCR exam. Concerning the 694 chest CT, 308 were rated as “Surely COVID+”, 34 as “Possible COVID+,” and 352 as “COVID−.” When considering only the “Surely COVID+” CT as positive, accuracy, sensitivity, specificity, PPV, and NPV reached 88.9%, 90.2%, 88%, 84.1%, and 92.7%, respectively, with respect to final RT-PCR test. The mean delay for CT reports was three times shorter than for RT-PCR results (187 ± 148 min versus 573 ± 327 min, p < 0.0001). During COVID-19 epidemic phase, chest CT is a rapid and most probably an adequately reliable tool to refer patients requiring hospitalization to the COVID+ or COVID− hospital units, when response times for virological tests are too long. • In a large university hospital in Lyon, France, the accuracy, sensitivity, specificity, PPV, and NPV of chest CT for COVID-19 reached 88.9%, 90.2%, 88%, 84.1%, and 92.7%, respectively, using RT-PCR as standard of reference. • The mean delay for CT reports was three times shorter than for RT-PCR results (187 ± 148 min versus 573 ± 327 min, p < 0.0001). • Due to high accuracy of chest CT for COVID-19 and shorter time for CT reports than RT-PCR results, chest CT can be used to orient patients suspected to be positive towards the COVID+ unit to decrease congestion in the emergency departments.

Machine QA for the Elekta Unity system: A Report from the Elekta MR-linac consortium.

Abstract: Over the last few years, magnetic resonance image-guided radiotherapy systems have been introduced into the clinic, allowing for daily online plan adaption. While quality assurance (QA) is similar to conventional radiotherapy systems, there is a need to introduce or modify measurement techniques. As yet, there is no consensus guidance on the QA equipment and test requirements for such systems. Therefore, this report provides an overview of QA equipment and techniques for mechanical, dosimetric, and imaging performance of such systems and recommendation of the QA procedures, particularly for a 1.5T MR-linac device. An overview of the system design and considerations for QA measurements, particularly the effect of the machine geometry and magnetic field on the radiation beam measurements is given. The effect of the magnetic field on measurement equipment and methods is reviewed to provide a foundation for interpreting measurement results and devising appropriate methods. And lastly, a consensus overview of recommended QA, appropriate methods, and tolerances is provided based on conventional QA protocols. The aim of this consensus work was to provide a foundation for QA protocols, comparative studies of system performance, and for future development of QA protocols and measurement methods.

A highly efficient and durable air electrode for intermediate-temperature reversible solid oxide cells

Abstract: Solid oxide cells (SOCs) are considered the most efficient system for reversible conversion between chemical and electrical energy, thus having potential to be an attractive technology for a sustainable energy future. To achieve high round-trip efficiency, highly efficient and durable air electrode materials are needed to minimize energy loss associated with oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Here we report a bi-functional air electrode material, PrBa0.9Co1.96Nb0.04O5+δ, demonstrating outstanding electrochemical performance (e.g., achieving peak power densities of over 1.5 and 1 W cm−2, respectively, for Gd0.1Ce0.9O1.95 and BaZr0.1Ce0.7Y0.1Yb0.1O3-δ based fuel cells at 600 °C) while maintaining excellent stability (e.g., having a degradation rate of 40 mV per 1,000 h for H2O electrolysis cells). The excellent property of the new electrode is attributed to the improved stability from Nb doping and the enhanced electrocatalytic activity from tuning Ba deficiency, as confirmed by experimental results and computational analysis.

Remote ischaemic preconditioning ameliorates anthracycline-induced cardiotoxicity and preserves mitochondrial integrity.

Abstract: Aims Anthracycline-induced cardiotoxicity (AIC) is a serious adverse effect among cancer patients A central mechanism of AIC is irreversible mitochondrial damage Despite major efforts, there are currently no effective therapies able to prevent AIC Methods and results Forty Large-White pigs were included In Study 1, 20 pigs were randomized 1:1 to remote ischemic pre-conditioning (RIPC, 3 cycles of 5 min leg ischemia followed by 5 min reperfusion) or no pretreatment RIPC was performed immediately before each intracoronary doxorubicin injections (045 mg/kg) given at weeks 0, 2, 4, 6, and 8 A group of 10 pigs with no exposure to doxorubicin served as healthy controls Pigs underwent serial cardiac magnetic resonance (CMR) exams at baseline and at weeks 6, 8, 12, and 16, being sacrifice after that In study 2, 10 new pigs received 3 doxorubicin injections (with/out preceding RIPC) and were sacrificed at week 6In Study 1, LVEF depression was blunted animals receiving RIPC before doxorubicin (RIPC-Doxo), which had a significantly higher LVEF at week 16 than doxorubicin treated pigs that received no pretreatment (Untreated-Doxo) (415±91% vs 325±87%, p = 004) It was mainly due to conserved regional contractile function In Study 2, transmission electron microscopy (TEM) at week 6 showed fragmented mitochondria with severe morphological abnormalities in Untreated-Doxo pigs, together with upregulation of fission and autophagy proteins At the end of the 16-week Study 1 protocol, TEM revealed overt mitochondrial fragmentation with structural fragmentation in Untreated-Doxo pigs, whereas interstitial fibrosis was less severe in RIPC+Doxo pigs Conclusion In a translatable large animal model of AIC, RIPC applied immediately before each doxorubicin injection resulted in preserved cardiac contractility with significantly higher long-term LVEF and less cardiac fibrosis RIPC prevented mitochondrial fragmentation and dysregulated autophagy from AIC early stages RIPC is a promising intervention for testing in clinical trials in AIC Translational perspective Serial cardiac magnetic resonance (CMR) evaluation of a highly translatable large animal model of anthracycline-induced cardiotoxicity (AIC) shows that cumulative exposure to doxorubicin results in significantly reduced LVEF and extensive mitochondrial fragmentation Remote ischemic preconditioning (RIPC) applied before each doxorubicin cycle preserved cardiac contractility and LVEF in long-term CMR exams RIPC prevented doxorubicin-induced irreversible mitochondrial fragmentation and dysregulated autophagy RIPC is as an attractive strategy for testing in clinical trials in AIC