13 research outputs found
Deep Learning-based Anonymization of Chest Radiographs: A Utility-preserving Measure for Patient Privacy
Robust and reliable anonymization of chest radiographs constitutes an
essential step before publishing large datasets of such for research purposes.
The conventional anonymization process is carried out by obscuring personal
information in the images with black boxes and removing or replacing
meta-information. However, such simple measures retain biometric information in
the chest radiographs, allowing patients to be re-identified by a linkage
attack. Therefore, there is an urgent need to obfuscate the biometric
information appearing in the images. We propose the first deep learning-based
approach (PriCheXy-Net) to targetedly anonymize chest radiographs while
maintaining data utility for diagnostic and machine learning purposes. Our
model architecture is a composition of three independent neural networks that,
when collectively used, allow for learning a deformation field that is able to
impede patient re-identification. Quantitative results on the ChestX-ray14
dataset show a reduction of patient re-identification from 81.8% to 57.7% (AUC)
after re-training with little impact on the abnormality classification
performance. This indicates the ability to preserve underlying abnormality
patterns while increasing patient privacy. Lastly, we compare our proposed
anonymization approach with two other obfuscation-based methods (Privacy-Net,
DP-Pix) and demonstrate the superiority of our method towards resolving the
privacy-utility trade-off for chest radiographs.Comment: Accepted at MICCAI 202
Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Data
With the rise and ever-increasing potential of deep learning techniques in
recent years, publicly available medical datasets became a key factor to enable
reproducible development of diagnostic algorithms in the medical domain.
Medical data contains sensitive patient-related information and is therefore
usually anonymized by removing patient identifiers, e.g., patient names before
publication. To the best of our knowledge, we are the first to show that a
well-trained deep learning system is able to recover the patient identity from
chest X-ray data. We demonstrate this using the publicly available large-scale
ChestX-ray14 dataset, a collection of 112,120 frontal-view chest X-ray images
from 30,805 unique patients. Our verification system is able to identify
whether two frontal chest X-ray images are from the same person with an AUC of
0.9940 and a classification accuracy of 95.55%. We further highlight that the
proposed system is able to reveal the same person even ten and more years after
the initial scan. When pursuing a retrieval approach, we observe an mAP@R of
0.9748 and a precision@1 of 0.9963. Furthermore, we achieve an AUC of up to
0.9870 and a precision@1 of up to 0.9444 when evaluating our trained networks
on external datasets such as CheXpert and the COVID-19 Image Data Collection.
Based on this high identification rate, a potential attacker may leak
patient-related information and additionally cross-reference images to obtain
more information. Thus, there is a great risk of sensitive content falling into
unauthorized hands or being disseminated against the will of the concerned
patients. Especially during the COVID-19 pandemic, numerous chest X-ray
datasets have been published to advance research. Therefore, such data may be
vulnerable to potential attacks by deep learning-based re-identification
algorithms.Comment: Published in Scientific Report
A European research agenda for somatic symptom disorders, bodily distress disorders, and functional disorders: Results of an estimate-talk-estimate delphi expert study
Background: Somatic Symptom Disorders (SSD), Bodily Distress Disorders (BDD) and functional disorders (FD) are associated with high medical and societal costs and pose a substantial challenge to the population and health policy of Europe. To meet this challenge, a specific research agenda is needed as one of the cornerstones of sustainable mental health research and health policy for SSD, BDD, and FD in Europe. Aim: To identify the main challenges and research priorities concerning SSD, BDD, and FD from a European perspective. Methods: Delphi study conducted from July 2016 until October 2017 in 3 rounds with 3 workshop meetings and 3 online surveys, involving 75 experts and 21 European countries. EURONET-SOMA and the European Association of Psychosomatic Medicine (EAPM) hosted the meetings. Results: Eight research priorities were identified: (1) Assessment of diagnostic profiles relevant to course and treatment outcome. (2) Development and evaluation of new, effective interventions. (3) Validation studies on questionnaires or semi-structured interviews that assess chronic medical conditions in this context. (4) Research into patients preferences for diagnosis and treatment. (5) Development of new methodologic designs to identify and explore mediators and moderators of clinical course and treatment outcomes (6). Translational research exploring how psychological and somatic symptoms develop from somatic conditions and biological and behavioral pathogenic factors. (7) Development of new, effective interventions to personalize treatment. (8) Implementation studies of treatment interventions in different settings, such as primary care, occupational care, general hospital and specialty mental health settings. The general public and policymakers will benefit from the development of new, effective, personalized interventions for SSD, BDD, and FD, that will be enhanced by translational research, as well as from the outcomes of research into patient involvement, GP-patient communication, consultation-liaison models and implementation. Conclusion: Funding for this research agenda, targeting these challenges in coordinated research networks such as EURONET-SOMA and EAPM, and systematically allocating resources by policymakers to this critical area in mental and physical well-being is urgently needed to improve efficacy and impact for diagnosis and treatment of SSD, BDD, and FD across Europe
Age stereotypes towards younger and older colleagues in registered nurses and supervisors in a university hospital: A generic qualitative study
Aim
This study aimed to identify and compare age stereotypes of registered nurses and supervisors in clinical inpatient settings.
Design
Generic qualitative study using halfâstandardized interviews.
Method
Nineteen faceâtoâface interviews and five focus groups (N = 50) were conducted with nurses of varying levels at a hospital of maximum medical care in Germany between August and November 2018 and were subjected to structured qualitative content analysis.
Results
Reflecting the ageing process and cooperation in mixedâage teams, nursing staff and supervisors defined similar age stereotypes towards older and younger nurses reminiscent of common generational labels âBaby Boomersâ and Generations X. Their evaluation created an inconsistent and contradictory pattern differing to the respective work context and goals. Age stereotypes were described as both potentially beneficial and detrimental for the individual and the cooperation in the team. If a successfully implemented diversity management focuses age stereotypes, negative assumptions can be reduced and cooperation in mixedâage teams can be considered beneficial.
Conclusion
Diversity management as measures against age stereotypes and for mutual acceptance and understanding should include staff from various hierarchical levels of the inpatient setting
Quantitative monitoring of paramagnetic contrast agents and their allocation in plant tissues via DCE-MRI
BACKGROUND: Studying dynamic processes in living organisms with MRI is one of the most promising research areas. The use of paramagnetic compounds as contrast agents (CA), has proven key to such studies, but so far, the lack of appropriate techniques limits the application of CA-technologies in experimental plant biology. The presented proof-of-principle aims to support method and knowledge transfer from medical research to plant science. RESULTS: In this study, we designed and tested a new approach for plant Dynamic Contrast Enhanced Magnetic Resonance Imaging (pDCE-MRI). The new approach has been applied in situ to a cereal crop (Hordeum vulgare). The pDCE-MRI allows non-invasive investigation of CA allocation within plant tissues. In our experiments, gadolinium-DTPA, the most commonly used contrast agent in medical MRI, was employed. By acquiring dynamic T1-maps, a new approach visualizes an alteration of a tissue-specific MRI parameter T1 (longitudinal relaxation time) in response to the CA. Both, the measurement of local CA concentration and the monitoring of translocation in low velocity ranges (cm/h) was possible using this CA-enhanced method. CONCLUSIONS: A novel pDCE-MRI method is presented for non-invasive investigation of paramagnetic CA allocation in living plants. The temporal resolution of the T1-mapping has been significantly improved to enable the dynamic in vivo analysis of transport processes at low-velocity ranges, which are common in plants. The newly developed procedure allows to identify vascular regions and to estimate their involvement in CA allocation. Therefore, the presented technique opens a perspective for further development of CA-aided MRI experiments in plant biology
Structural Equation Modeling of a Global Stress Index in Healthy Soldiers
Accumulation of stress is a prognostic trigger for cardiovascular disease. Classical scores for cardiovascular risk estimation typically do not consider psychosocial stress. The aim of this study was to develop a global stress index (GSI) from healthy participants by combining individual measures of acute and chronic stress from childhood to adult life. One-hundred and ninety-two female and male soldiers completed the Perceived Stress Scale (PSS4), Trier Inventory for Chronic Stress (TICS), Hospital Anxiety and Depression Scale (HADS), Childhood Trauma Questionnaire (CTQ), Posttraumatic Diagnostic Scale Checklist (PDS), and the Deployment Risk and Resilience Inventory (DRRI-2). The underlying structure for the GSI was examined through structural equation modeling. The final hierarchical multilevel model revealed fair fit by taking modification indices into account. The highest order had a g-factor called the GSI. On a second level the latent variables stress, HADS and CTQ were directly loading on the GSI. A third level with the six CTQ subscales was implemented. On the lowest hierarchical level all manifest variables and the DRRI-2/PDS sum scores were located. The presented GSI serves as a valuable and individual stress profile for soldiers and could potentially complement classical cardiovascular risk factors