A5034493341- Acute Ischemic Stroke Management
- Artificial Intelligence in Healthcare and Education
- Cerebrovascular and Carotid Artery Diseases
- Advanced MRI Techniques and Applications
- Stroke Rehabilitation and Recovery
- Machine Learning in Healthcare
- MRI in cancer diagnosis
- Explainable Artificial Intelligence (XAI)
- Advanced Neuroimaging Techniques and Applications
- Traumatic Brain Injury and Neurovascular Disturbances
- Radiomics and Machine Learning in Medical Imaging
- Retinal Imaging and Analysis
- Generative Adversarial Networks and Image Synthesis
- Ethics in Clinical Research
- Sepsis Diagnosis and Treatment
- Medical Image Segmentation Techniques
- Ethics and Social Impacts of AI
- Privacy-Preserving Technologies in Data
- Metabolism and Genetic Disorders
- Cardiac Imaging and Diagnostics
- Neurological diseases and metabolism
- Traumatic Brain Injury Research
- Venous Thromboembolism Diagnosis and Management
- Dementia and Cognitive Impairment Research
- Neuroscience and Neuropharmacology Research
Birmingham City University
2020-2025
Berlin Institute of Health at Charité - Universitätsmedizin Berlin
2019-2025
Charité - Universitätsmedizin Berlin
2014-2023
Weatherford College
2022
City, University of London
2021
Humboldt-Universität zu Berlin
2019
Freie Universität Berlin
2019
Max Planck Institute for Metabolism Research
2015-2017
Aarhus University
2017
Fraunhofer Institute for Digital Medicine
2015
Abstract Background Explainability is one of the most heavily debated topics when it comes to application artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown outperform humans certain analytical tasks, lack explainability continues spark criticism. Yet, not a purely technological issue, instead invokes host medical, legal, ethical, and societal questions that require thorough exploration. This paper provides comprehensive assessment role medical AI makes...
Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based segmentation algorithms need to be hand-crafted are insufficiently validated. A specialized deep learning method-the U-net-is alternative. Using labeled data from 66 patients with disease, the U-net framework was optimized evaluated three metrics: Dice coefficient, 95% Hausdorff distance (95HD) average (AVD). The model performance compared traditional...
State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide decision support systems physicians. Modern ML approaches such as neural networks (ANNs) and tree boosting often perform better than more traditional like logistic regression. On the other hand, these modern yield a limited understanding of resulting predictions. However, medical domain, applied models is essential, particular, when informing support....
Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents review of the key arguments favor and against explainability AI-powered Clinical Decision Support System (CDSS) applied to concrete use case, namely an CDSS currently used emergency call setting identify patients with life-threatening cardiac arrest. More specifically, we performed normative analysis using socio-technical scenarios provide nuanced account role CDSSs allowing abstractions...
Abstract Average Hausdorff distance is a widely used performance measure to calculate the between two point sets. In medical image segmentation, it compare ground truth images with segmentations allowing their ranking. We identified, however, ranking errors of average making less suitable for applications in segmentation assessment. To mitigate this error, we present modified calculation that have coined “balanced distance”. simulate ranking, manually created non-overlapping common magnetic...
Artificial intelligence (AI) has the potential to transform clinical decision-making as we know it. Powered by sophisticated machine learning algorithms, decision support systems (CDSS) can generate unprecedented amounts of predictive information about individuals' health. Yet, despite these promote proactive and improve health outcomes, their utility impact remain poorly understood due still rare application in practice. Taking example AI-powered CDSS stroke medicine a case point, this...
Trustworthy medical AI requires transparency about the development and testing of underlying algorithms to identify biases communicate potential risks harm. Abundant guidance exists on how achieve for products, but it is unclear whether publicly available information adequately informs their risks. To assess this, we retrieved public documentation 14 CE-certified AI-based radiology products II b risk category in EU from vendor websites, scientific publications, European EUDAMED database....
Introduction: Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. Non-invasive neuroimaging techniques, such as time-of-flight (TOF) magnetic resonance angiography (MRA) imaging are applied clinical routine to depict arteries. They are, however, only visually assessed. Fully automated segmentation integrated into could facilitate time-critical diagnosis of abnormalities and might identification valuable biomarkers events. In...
Artificial intelligence (AI) in healthcare promises to make safer, more accurate, and cost-effective. Public private actors have been investing significant amounts of resources into the field. However, benefit from data-intensive medicine, particularly AI technologies, one must first foremost access data. It has previously argued that conventionally used "consent or anonymize approach" undermines worse, may ultimately harm patients. Yet, this is still a dominant approach European countries...
To evaluate the transferability of deep learning (DL) models for early detection adverse events to previously unseen hospitals.
In Wilson disease (WD), T2/T2*-weighted (T2*w) MRI frequently shows hypointensity in the basal ganglia that is suggestive of paramagnetic deposits. It currently unknown whether this related to copper or iron deposition. We examined neuropathological correlates pattern, particularly relation and concentrations.Brain slices from nine WD six control cases were investigated using a 7T-MRI system. High-resolution T2*w images acquired R2* parametric maps reconstructed multigradient recalled echo...
This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of artificial intelligence (AI) system component for healthcare. The explains decisions made by deep learning networks analyzing images skin lesions. trustworthy AI developed here used a holistic approach rather than static ethical checklist and required multidisciplinary team experts working with designers their managers. Ethical, legal, technical issues potentially arising...
Deep learning requires large labeled datasets that are difficult to gather in medical imaging due data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used generate images with their corresponding labels, they cannot capture the volumetric information 3D imaging. more suitable for this volumes but not labels. One reason might be synthesizing is challenging...
Reliable prediction of outcomes aneurysmal subarachnoid hemorrhage (aSAH) based on factors available at patient admission may support responsible allocation resources as well treatment decisions. Radiographic and clinical scoring systems help clinicians estimate disease severity, but their predictive value is limited, especially in devising strategies. In this study, we aimed to examine whether a machine learning (ML) approach using variables improve outcome aSAH compared established...
Artificial Intelligence (AI) has the potential to greatly improve delivery of healthcare and other services that advance population health wellbeing. However, use AI in also brings risks may cause unintended harm. To guide future developments AI, High-Level Expert Group on set up by European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These are aimed at a variety stakeholders, especially guiding practitioners toward more ethical robust...
This article's main contributions are twofold: 1) to demonstrate how apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice domain of healthcare and 2) investigate research question what does "trustworthy AI" mean at time COVID-19 pandemic. To this end, we present results a post-hoc self-assessment evaluate trustworthiness an system predicting multiregional score conveying degree lung compromise patients, developed verified by...