Point-of-Care Imaging |

PromptGate: Client-Adaptive Vision–Language Gating for Open-Set Federated Active Learning

Sun, 27 Sep 2026 00:00:00 +0000

Eyelid Tumor Triage using Uncertainty-Aware and Explainable Artificial Intelligence

Fri, 25 Sep 2026 00:00:00 +0000

Entwicklung einer künstlichen Intelligenz zur ultraschallgestützten Detektion der Riesenzellarteriitis

Wed, 09 Sep 2026 00:00:00 +0000

When multi-institutional dataset aggregation masks shortcut-like behavior in Foundation Models for medical imaging

Tue, 01 Sep 2026 00:00:00 +0000

MRIQT – KI-gestützte Verbesserung der Bildqualität in der neonatalen Ultra-Niedrigfeld-MRT

Mon, 01 Jun 2026 00:00:00 +0000

Artificial intelligence for ultrasound-based detection of giant cell arteritis

Thu, 01 Jan 2026 00:00:00 +0000

MRIQT: Physics-Aware Diffusion Model for Image Quality Transfer in Neonatal Ultra-Low-Field MRI

Mon, 17 Nov 2025 00:00:00 +0000

OP0343 The ClaudicAItio Model: Ultrasound Image Classification in Giant Cell Arteritis by Application of Artificial Intelligence

Sun, 01 Jun 2025 00:00:00 +0000

Summer School on Biomedical Imaging with Deep Learning (BILD)

Sun, 01 Jun 2025 00:00:00 +0000

Medical imaging is central to diagnosis, yet many hospitals in North Africa and the Middle East work with portable devices, limited compute and few specialists. Training the next generation of AI researchers to build methods that work under these conditions — and connecting them across countries — is the goal of the Summer School on Biomedical Imaging with Deep Learning (BILD).

The first edition took place in Tunis, Tunisia, 1–5 September 2025, as part of , the Strategic Arab-German Network for Affordable and Democratized AI in Healthcare. It was organised by the Albarqouni Lab (University Hospital Bonn and University of Bonn) with the University of Tunis El Manar, the Lebanese American University and Duhok Polytechnic University, and funded by the German Academic Exchange Service (DAAD) with funds from the Federal Foreign Office.

Measures

Summer school for about 50 students and graduates from Germany, Lebanon, Iraq and Tunisia, selected through an open call, with around 10 faculty members.
Course modules that combine current AI research with hands-on, resource-aware exercises: deep learning for medical imaging, lightweight neural networks for portable ultrasound, and federated learning on decentralised datasets.
Expert lectures by speakers from academia and industry, including the University of Tunis El Manar, TU Munich, FAU Erlangen-Nürnberg, Imperial College London and NVIDIA.
Hands-on labs and collaborative case studies guided by tutors from Bonn, Tunis and partner institutions.
Co-mentored research projects and exchange sessions in mixed international teams, continuing the collaboration beyond the week in Tunis.

Outcomes

BILD strengthened practical skills in building and deploying AI models for medical imaging, brought together young researchers and faculty from four countries, and grew the SANAD network into a sustained transnational community of AI innovators in healthcare.

Artery-Fusion Deep Learning for Enhanced Ultrasound Diagnosis of Giant Cell Arteritis

Wed, 01 Jan 2025 00:00:00 +0000

Bias and generalizability of foundation models across datasets in breast mammography

Wed, 01 Jan 2025 00:00:00 +0000

Exploring the limit of image resolution for human expert classification of vascular ultrasound images in giant cell arteritis and healthy subjects: the GCA-US-AI project

Wed, 01 Jan 2025 00:00:00 +0000

Fetal Ultrasound Dataset for Congenital Diaphragmatic Hernia

Wed, 01 Jan 2025 00:00:00 +0000

Partner: PD Dr. Florian Recker, Center for Obstetrics and Gynecology, University Hospital Bonn.

Giant Cell Arteritis Ultrasound Dataset

Wed, 01 Jan 2025 00:00:00 +0000

Curated with the Center for Rare Rheumatological Diseases (Claus-Jürgen Bauer, Prof. Valentin Schäfer), University Hospital Bonn. One of the largest clinically validated datasets for ultrasound-based GCA diagnosis; being extended towards a multi-center German cohort.

Our lab led the ML-readiness: quality control, cleaning, standardised preprocessing, annotation workflow and training/validation splits.

Investigation of the image resolution limit for human interpretation of ultrasound images in giant cell arteritis and healthy subjects-the GCA-US-AI project

Wed, 01 Jan 2025 00:00:00 +0000

KI-gestützte Diagnostik von Tumoren am Auge: Dignitätseinordnung von Lidtumoren mittels KI zur optimierten Patienteneinbestellung

Wed, 01 Jan 2025 00:00:00 +0000

Ein Team unter Leitung von Prof. Dr. Martina Christina Herwig-Carl (Klinik für Augenheilkunde, Universitätsklinikum Bonn) entwickelt eine App, die bei der Einschätzung und Klassifizierung von Augenlidtumoren unterstützt. Dies soll eine schnellere Diagnostik und eine bessere operative Versorgung ermöglichen.

Eyelid lesions are common, and telling benign from malignant tumours early is essential — yet many are first seen outside specialised centres, where subtle clinical and morphological features make the call difficult. The question there is less a definitive diagnosis than who needs to be referred, and how urgently.

What we do

Referral-oriented malignancy risk from routine photographs. A face-landmarker-guided pipeline standardises the periocular region, and a fine-tuned ResNet-50 estimates malignancy risk with patient-level evaluation on 956 patients from the University Eye Hospital Bonn archive (2020–2025), histopathology as ground truth — AUC 86% and sensitivity 84% ( ).
Knowing when to look again. Monte Carlo dropout gives each prediction an uncertainty that is highest for misclassified — especially missed malignant — cases, turning it into an alert to re-check those images; Grad-CAM shows that the model attends to the visible lesion.
From model to app. Funded as a prototyping grant by the Transfer Center enaCom of the University of Bonn, the work is being turned into an app that supports the assessment and triage of eyelid tumours in clinical practice.

Our role

Co-PI: methodological lead on uncertainty-aware and explainable AI, with Ashkan Shakarami (postdoctoral fellow, Department of Ophthalmology) developing the models.

Neonatal Ultra-Low-Field / High-Field MRI Dataset

Wed, 01 Jan 2025 00:00:00 +0000

Curated with the Department of Experimental Neonatology, University Hospital Bonn. Basis of MRIQT, a physics-aware 3D diffusion model for image-quality transfer; expert clinicians rated 85% of generated images as diagnostically useful.

Retinal Fundus Image Dataset

Wed, 01 Jan 2025 00:00:00 +0000

International collaboration with Dr. Said El Shamieh (Beirut Arab University, Lebanon). Harmonisation, label mapping and bias/generalisation evaluation protocols led by our lab.

Strategic Arab-German Network for Affordable and Democratized AI (SANAD)

Wed, 01 Jan 2025 00:00:00 +0000

In recent years, the transformative potential of Artificial Intelligence (AI) in revolutionizing global healthcare has become evident, offering promising advancements in diagnostic precision, personalized treatment, and healthcare delivery. However, integrating AI into low-resource environments, especially in low- and middle-income countries (LMICs), remains constrained by significant barriers such as limited computational resources, inadequate local data, and financial constraints. The Strategic Arab-German Network for Affordable and Democratized AI in Healthcare (SANAD- سند) seeks to address these challenges by fostering collaboration, empowering local expertise, and advancing Affordable AI and Collaborative Federated Learning solutions tailored to underserved regions. The word “SANAD” means support in Arabic, which is what this project is about.

SANAD aims to bridge the healthcare gap between resource-rich and resource-limited settings, particularly the Global North and Global South disparity. Many existing AI models are developed using centralized datasets from well-funded institutions, which often fail to generalize across diverse populations and contexts. SANAD proposes a strategic, inclusive approach to democratizing access to AI by leveraging interdisciplinary expertise and promoting knowledge-sharing between Arab and German stakeholders

Measures:

, Tunisia
, Germany

Summer School on Affordable AI (SAAI)

Wed, 01 May 2024 00:00:00 +0000

Artificial intelligence could ease the shortage of specialists and diagnostic capacity in many health systems, yet most AI courses assume powerful hardware, large datasets and stable infrastructure that are scarce in low- and middle-income countries of the Middle East and North Africa (MENA). The Summer School on Affordable AI (SAAI, سعي — “striving”) was designed to close this gap by teaching AI that works under real-world resource constraints.

SAAI took place in Bonn, 22–26 July 2024, and was organised by the AGYA working group Innovation, in close collaboration with the working group Health & Society, of the Arab-German Young Academy of Sciences and Humanities (AGYA), with the University of Bonn, the University Hospital Bonn and Helmholtz AI. It was hosted by the Albarqouni Lab and modelled on our earlier .

Programme

Participants: about 30 students and graduates and 12 faculty members from Germany and Arab countries, selected through an open call.
Foundations: Python programming, mathematics and statistics for machine learning, and analytical thinking.
Affordable AI: teaching modules tailored to the needs and resource limitations of low-income MENA countries, addressing shared health challenges in Germany and the Arab world.
Hands-on learning: lectures, hands-on labs and real-world challenges, worked on in mixed teams with faculty and experienced tutors.

Outcomes

Participants built practical skills in developing AI models for health under limited compute and data, and the school fostered knowledge transfer and collaboration between young researchers from Germany and the Arab world. SAAI laid the groundwork for the DAAD-funded and the in Tunis in 2025.

Affordable Deep Learning for Diagnosing Inherited and Common Retinal Diseases via Color Fundus Photography

Mon, 01 Jan 2024 00:00:00 +0000

MRIQT: Image-quality transfer from ultra-low-field to high-field neonatal MRI

Mon, 01 Jan 2024 00:00:00 +0000

This project addresses the limited diagnostic usability of portable ultra-low-field MRI (0.064 T) in neonatal neuroimaging, where reduced signal-to-noise ratio constrains clinical interpretation despite bedside accessibility. Together with the clinical partners, we curated a unique in-house cohort comprising 50 paired ultra-low-field and high-field 3D neonatal brain MRI scans, complemented by 230 additional unpaired scans across nine pathologies. Building on this paired–unpaired data structure, we developed MRIQT, a 3D conditional diffusion framework for structure-preserving image quality transfer from ultra-low-field to high-field MRI. The method integrates physics-consistent k-space degradation, classifier-free guided diffusion, and an SNR-weighted volumetric perceptual loss.

MRIQT outperformed CNN- and GAN-based baselines, achieving up to 15.3% improvement in PSNR, while expert clinicians rated 85% of generated images as diagnostically useful with preserved pathology.

Perspective

The project provides a methodological foundation for automated MRI quality enhancement and modality-agnostic quality control. Building on the current results, the work is prepared for translational follow-up and prospective validation, with a planned submission to the Go-Bio Initial funding program.

Affordable AI and Collaborative Federated Learning for Global Healthcare (EEDA)

Fri, 01 Sep 2023 00:00:00 +0000

Evolving the era of 5D ultrasound? A systematic literature review on the applications for artificial intelligence ultrasound imaging in obstetrics and gynecology

Sun, 01 Jan 2023 00:00:00 +0000

Affordable deep learning for inherited and common retinal diseases

Wed, 01 Jun 2022 00:00:00 +0000

This international collaboration addresses the diagnosis of inherited retinal diseases (IRDs) and common retinal disorders using affordable color fundus photography, motivated by limited access to genetic testing and advanced imaging in low-resource settings. Together with partners in Germany and Lebanon, we curated and analyzed heterogeneous multi-center retinal imaging datasets combining in-house cohorts and public data, spanning diverse demographics and disease spectra. Methodologically, we developed lightweight deep learning models for multi-class retinal disease classification covering 21 retinal conditions, including 13 IRDs. The approach achieved strong diagnostic performance for prevalent IRDs (e.g., F1 score of 0.86 for retinitis pigmentosa) while remaining computationally efficient and interpretable.

Perspective

The collaboration established a sustainable international research link between ophthalmology groups in Germany and Lebanon and contributed to interdisciplinary student training. Ongoing work explores privacy-preserving and federated learning strategies to improve robustness and equitable deployment across populations further.

Deep learning for fetal diaphragmatic hernia detection in ultrasound

Wed, 01 Jun 2022 00:00:00 +0000

Congenital diaphragmatic hernia (CDH) is a rare but severe fetal malformation associated with high neonatal morbidity and mortality, where early detection is critical for risk stratification, parental counseling, and perinatal management. Although ultrasound is the primary imaging modality in prenatal screening, CDH detection remains challenging due to limited acoustic windows, overlapping anatomical structures, and strong operator dependence.

The initial objective of this project was to develop deep learning–based methods for automated CDH detection in fetal ultrasound. Due to changes in the clinical collaboration context, the project could not be continued in its originally planned data-driven form. As a result, the focus shifted toward consolidating the existing evidence base and systematically analyzing methodological and clinical barriers for AI-assisted fetal ultrasound diagnostics.

As a central outcome, we co-authored a comprehensive systematic review on artificial intelligence in obstetric and gynecologic ultrasound, critically assessing methodological rigor, data availability, validation practices, and clinical readiness across 189 published studies. The review identified fetal malformation detection, including CDH, as a clinically high-impact yet underexplored application area, highlighting the need for standardized datasets, multi-center validation, and interpretable AI approaches.

Perspective

The insights gained from this project provided a methodological foundation for subsequent ultrasound- and MRI-based research with sustained clinical partnerships and informed the design of later projects with improved data availability and validation strategies. Building on this groundwork, renewed efforts toward CDH detection are envisaged within a national multi-center collaboration, leveraging harmonized data collection and shared clinical expertise.

Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global health

Sat, 01 Jan 2022 00:00:00 +0000

GCA-US-AI: Deep learning–based ultrasound diagnosis of Giant Cell Arteritis

Sat, 01 Jan 2022 00:00:00 +0000

In close interdisciplinary cooperation with clinical rheumatology, this project addresses the highly operator-dependent and time-critical ultrasound diagnosis of Giant Cell Arteritis (GCA), a vasculitis associated with irreversible vision loss. Together with our partners, we curated and annotated an in-house dataset of 3,800 ultrasound examinations from 344 patients, constituting one of the largest clinically validated datasets for ultrasound-based GCA diagnosis. Methodologically, we developed artery-aware deep learning models that integrate anatomical context into ultrasound image analysis, achieving robust performance improvements over artery-agnostic baselines (AUROC up to ~0.9 for larger arteries). Uncertainty estimation and visual explainability were incorporated to support reliable clinical interpretation.

Perspective

The collaboration is ongoing and continues to serve as a basis for joint publications, supervised theses, and continued dataset extension (in-house already ~15,000 images) toward a multi-center cohort. Current work explores vision–language models integrating imaging and clinical data and forms the basis of a planned joint DFG proposal coordinated together with the clinical partners.

Domain adaptation and representation transfer, and affordable healthcare and AI for resource diverse global health

Fri, 01 Jan 2021 00:00:00 +0000

Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health: Third MICCAI Workshop, DART 2021, and First MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings

Fri, 01 Jan 2021 00:00:00 +0000

Steps towards Establishing Telemedicine Center in Palestine

Thu, 17 Sep 2009 00:00:00 +0000

Telemedicine in Palestine

Tue, 01 Jan 2008 00:00:00 +0000

Access to specialist care in Palestine is limited, which makes telemedicine a natural way to bring expertise to patients rather than patients to expertise.

What we did

2008 — a self-initiated plan for a telemedicine centre. Laid out the steps towards establishing a telemedicine centre in Palestine ( ).
2014 — consultancy for GIZ. Advised the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) on telemedicine in Palestine.

Telemedicine in Palestine from Shadi Nabil Albarqouni