Jan Egger

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• Professor
• Professor at University Hospital Essen

Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edge methods. Additionally, deep learning has delivered state-of-the-art results in tasks like autonomous driving, outclassing previous...

The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality (AR) display, is the main player in the recent boost in medical AR research. In this systematic review, we provide a comprehensive overview of the usage of the first-generation HoloLens within the medical domain, from its release in March 2016, until t...

In recent years, 3D printing (3DP) has gained importance in various fields. This technology has numerous applications, particularly in medicine. This contribution provides an overview on the state of the art of 3DP in medicine and showcases its current use in different medical disciplines and for medical education. In this meta-review, we provide a...

Objectives The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). H...

At the Worldwide Developers Conference in June 2023, Apple introduced the Vision Pro. The Apple Vision Pro (AVP) is a mixed reality headset; more specifically, it is a virtual reality device with an additional video see-through capability. The video see-through capability turns the AVP into an augmented reality (AR) device. The AR feature is enable...

The aortic vessel tree, composed of the aorta and its branches, is crucial for blood supply to the body. Aortic diseases, such as aneurysms and dissections, can lead to life-threatening ruptures, often requiring open surgery. Therefore, patients commonly undergo treatment under constant monitoring, which requires regular inspections of the vessels...

ABSTRACT The rapid evolution of generative artificial intelligence (genAI) has ushered in a new era of digital medical consultations, with patients turning to AI-driven tools for guidance. The emergence of Chinese-developed genAI models such as DeepSeek-R1 and Qwen-2.5 presented a challenge to the dominance of OpenAI’s ChatGPT. The aim of this stud...

Radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided RT offering superior soft tissue contrast and functional imaging. However, manual tumor segmentation is time-consuming and complex, and therefore remains a challenge. In this study, we present our solution as team TUMOR to the HNT...

In the rapidly evolving domain of generative artificial intelligence (genAI), the Chinese model DeepSeek-R1, launched in January 2025, emerges as a formidable contender. This model mirrors the capabilities of its Western counterparts by adeptly analyzing complex data to drive innovation across diverse fields with remarkable efficiency and scalabili...

DeepSeek, a Chinese artificial intelligence company, released its first free chatbot app based on its DeepSeek-R1 model. DeepSeek provides its models, algorithms, and training details to ensure transparency and reproducibility. Their new model is trained with reinforcement learning, allowing it to learn through interactions and feedback rather than...

Background: Systemic cancer therapy may trigger anxiety/depressive symptoms and toxicity. Relaxation techniques can help alleviate toxicities but their implementation in clinical practice is challenging. We hypothesize that virtual reality (VR) systems which project a relaxing nature environment may help to reduce psychological stress and toxicitie...

In this article, we present a brain tumor database collection comprising 23,049 samples, with each sample including four different types of MRI brain scans: FLAIR, T1, T1ce, and T2. Additionally, one or two segmentation masks (ground truth) are provided for each sample. The first mask is the raw output from the registration process and is provided...

ChatGPT represents a transformative technology in healthcare, with demonstrated impacts across clinical practice, medical education, and research. Studies show significant efficiency gains, including 70% reduction in administrative time for discharge summaries and achievement of medical professional-level performance on standardized tests (60% accu...

Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence (xAI) to introduce AI-derived (AID) markers for clinical decision support. We used xAI to decode the outcome of 15,726 pat...

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image...

This paper addresses the growing integration of Augmented Reality (AR) in biomedical sciences, emphasizing collaborative learning experiences. We present MultiAR, a versatile , domain-specific platform enabling multiuser interactions in AR for biomedical education. Unlike platform-specific solutions, MultiAR supports various AR devices, including h...

Radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided RT offering superior soft tissue contrast and functional imaging. However, manual tumor segmentation is time-consuming and complex, and therfore remains a challenge. In this study, we present our solution as team TUMOR to the HNTS...

Conventional navigation systems (CNS) in surgery require strong spatial cognitive abilities and hand-eye coordination. Augmented Reality Navigation Systems (ARNS) provide 3D guidance and may overcome these challenges, but their accuracy and efficiency compared to CNS have not been systematically evaluated. In this randomized crossover study with 36...

This paper presents the winning solution of task 1 and the third-placed solution of task 3 of the BraTS challenge. The use of automated tools in clinical practice has increased due to the development of more and more sophisticated and reliable algorithms. However, achieving clinical standards and developing tools for real-life scenarios is a major...

This paper presents the second-placed solution for task 8 and the participation solution for task 7 of BraTS 2024. The adoption of automated brain analysis algorithms to support clinical practice is increasing. However, many of these algorithms struggle with the presence of brain lesions or the absence of certain MRI modalities. The alterations in...

Automated detection of tumour lesions on positron emission tomography–computed tomography (PET/CT) image data is a clinically relevant but highly challenging task. Progress in this field has been hampered in the past owing to the lack of publicly available annotated data and limited availability of platforms for inter-institutional collaboration. H...

This book constitutes the proceedings of the International Workshop on Shape in Medical Imaging, ShapeMI 2024, which took place in Marrakesh, Morocco, on October 6, 2024, held in conjunction with MICCAI 2024. The 16 full papers included in this book were carefully reviewed and selected from 24 submissions. They focus on shape and spectral analysis...

Point clouds provide an efficient and natural representation of 3D anatomy, capturing fine details of complex structures with minimal computational overhead. However, most learning-based approaches in medical imaging focus on 2D and 3D gridded data, disregarding the potential of point clouds. In this paper, we present a multimodal foundation model...

Unstructured data in industries such as healthcare, finance, and manufacturing presents significant challenges for efficient analysis and decision making. Detecting patterns within this data and understanding their impact is critical but complex without the right tools. Traditionally, these tasks relied on the expertise of data analysts or labor-in...

Introduction Artificial intelligence (AI) chatbots excel in language understanding and generation. These models can transform healthcare education and practice. However, it is important to assess the performance of such AI models in various topics to highlight its strengths and possible limitations. This study aimed to evaluate the performance of C...

The current gold standard of computer-assisted jaw reconstruction includes raising microvascular bone flaps with patient-specific 3D-printed cutting guides. The downsides of cutting guides are invasive fixation, periosteal denudation, preoperative lead time and missing intraoperative flexibility. This study aimed to investigate the feasibility and...

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In...

The article presents a semi-automatic approach to generating structured hex-ahedral meshes of patient-specific aortas ailed by aortic dissection. The condition manifests itself as a formation of two blood flow channels in the aorta, as a result of a tear in the inner layers of the aortic wall. Subsequently, the morphology of the aorta is greatly im...

Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computer vision , with applications in areas such as augmented reality (AR) and medical imaging. This work explores the intersection of two research trends: the integration of AR into image-guided surgery and the use of deep learning for point...

Diminished Reality is a technique for the removal of objects from the surroundings, providing a better view of otherwise obstructed features. This work explores the application of Diminished Reality in a medical setting, specifically aiming to visually eliminate surgical tools from operation sites for improved visibility and inspection. To this end...

State-of-the-art deep learning algorithms are easily biased and evaluated in misleading scenarios, especially in the medical context, where scenarios change rapidly and diseases develop quickly. The BraTS 2024 GoAT challenge aims to evaluate how brain tumour segmentation algorithms can adapt to different circumstances when these are not available f...

Background FHIR (Fast Healthcare Interoperability Resources) has been proposed to enable health data interoperability. So far, its applicability has been demonstrated for selected research projects with limited data. Objective This study aimed to design and implement a conceptual medical intelligence framework to leverage real-world care data for...

Background Assessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy of information in multilingual contexts. This study aimed to compare AI model efficiency in English and Arabic for infectious disease queries. Methods The study employed the METRICS checklist for the design and rep...

Augmented Reality (AR) Head Mounted Displays (HMDs) hold promise in revolutionizing surgical procedures by providing enhanced visualization and information overlay capabilities. This study evaluates and compares Optical See-Through (OST) and Video See-Through (VST) AR devices across key performance metrics crucial for surgical applications: depth p...

Augmented Reality (AR) Head Mounted Displays (HMDs) hold promise in revolutionizing surgical procedures by providing enhanced visualization and information overlay capabilities. This study evaluates and compares Optical See-Through (OST) and Video See-Through (VST) AR devices across key performance metrics crucial for surgical applications: depth p...

The accurate diagnosis of pathological subtypes of lung cancer is of paramount importance for follow-up treatments and prognosis managements. Assessment methods utilizing deep learning technologies have introduced novel approaches for clinical diagnosis. However, the majority of existing models rely solely on single-modality image input, leading to...

The development of magnetic resonance imaging (MRI) for medical imaging has provided a leap forward in diagnosis, providing a safe, non-invasive alternative to techniques involving ionising radiation exposure for diagnostic purposes. It was described by Block and Purcel in 1946, and it was not until 1980 that the first clinical application of MRI b...

Standardized reporting of multiparametric prostate MRI (mpMRI) is widespread and follows international standards (Pi-RADS). However, quantitative measurements from mpMRI are not widely comparable. Although T2 mapping sequences can provide repeatable quantitative image measurements and extract reliable imaging biomarkers from mpMRI, they are often t...

Objectives Tooth extraction is one of the most frequently performed medical procedures. The indication is based on the combination of clinical and radiological examination and individual patient parameters and should be made with great care. However, determining whether a tooth should be extracted is not always a straightforward decision. Moreover,...

Diminished reality (DR) refers to the removal of real objects from the environment by virtually replacing them with their background. Modern DR frameworks use inpaint-ing to hallucinate unobserved regions. While recent deep learning-based inpainting is promising, the DR use case is complicated by the need to generate coherent structure and 3D geome...

Diminished reality (DR) involves virtually removing real objects from the environment using inpainting techniques. However, existing methods struggle with maintaining coherent structure and 3D geometry, particularly for advanced tasks like 3D scene editing. In response, we introduce DeepDR, a real-time RGB-D inpainting framework tailored for DR, en...

3D data from high-resolution volumetric imaging is a central resource for diagnosis and treatment in modern medicine. While the fast development of AI enhances imaging and analysis, commonly used visualization methods lag far behind. Recent research used extended reality (XR) for perceiving 3D images with visual depth perception and touch but used...

Aortic dissections (ADs) are serious conditions of the main artery of the human body, where a tear in the inner layer of the aortic wall leads to the formation of a new blood flow channel, named false lumen. ADs affecting the aorta distally to the left subclavian artery are classified as a Stanford type B aortic dissection (type B AD). This is link...

We present a novel preprocessing and prediction pipeline for the classification of magnetic resonance imaging (MRI) that takes advantage of the information rich complex valued k-Space. Using a publicly available MRI raw dataset with 312 subject and a total of 9508 slices, we show the advantage of utilizing the k-Space for better prostate cancer lik...

Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computer vision, with applications in areas such as augmented reality (AR) and medical imaging. This work explores the intersection of two research trends: the integration of AR into image-guided surgery and the use of deep learning for point c...

Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been diffi...

Background and objective: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinical observations. Unfortunately, most segmentation methods known today are limited to nuclei and cann...

Objectives: Tooth extraction is one of the most frequently performed medical procedures. The indication is based on the combination of clinical and radiological examination and individual patient parameters and should be made with great care. However, determining whether a tooth should be extracted is not always a straightforward decision. Moreover...

In traumatic medical emergencies, the patients heavily depend on cranioplasty-the craft of neurocranial repair using cranial implants. Despite the improvements made in recent years, the design of a patient-specific implant (PSI) is among the most complex, expensive, and least automated tasks in cranioplasty. Further research in this area is needed....

Machine learning (ML), especially deep learning (DL), is a field of research that has recently attracted enormous attention and is currently evolving rapidly. New applications in economics, industry, and healthcare create new challenges for the sustainable development of our society. We describe the organization and realization of a machine learnin...

The authors regret that the affiliations for Corresponding Author André Ferreira have been listed incorrectly.

Since its release at the end of 2022, ChatGPT has seen a tremendous rise in attention, not only from the general public, but also from medical researchers and healthcare professionals. ChatGPT definitely changed the way we can communicate now with computers. We still remember the limitations of (voice) assistants, like Alexa or Siri, that were "ove...

In the dynamic landscape of digitized healthcare, open science principles are instrumental in driving transformative changes. This contribution describes two open science initiatives: StudierFenster, a cloud-based framework for (bio-)medical image analysis, and MedShapeNet, a comprehensive and open-access dataset of medical shapes. StudierFenster o...

We introduce two open science initiatives: StudierFenster, an open, browser-based framework for biomedical image analysis, and MedShapeNet, a comprehensive repository of medical shapes.

Purpose Efficient and precise surgical skills are essential in ensuring positive patient outcomes. By continuously providing real-time, data driven, and objective evaluation of surgical performance, automated skill assessment has the potential to greatly improve surgical skill training. Whereas machine learning-based surgical skill assessment is ga...

Objective: The gold standard of oral cancer (OC) treatment is diagnostic confirmation by biopsy followed by surgical treatment. However, studies have shown that dentists have difficulty performing biopsies, dental students lack knowledge about OC, and surgeons do not always maintain a safe margin during tumor resection. To address this, biopsies an...

Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are...

Background The radial forearm free flap (RFFF) serves as a workhorse for a variety of reconstructions. Although there are a variety of surgical techniques for donor site closure after RFFF raising, the most common techniques are closure using a split-thickness skin graft (STSG) or a full-thickness skin graft (FTSG). The closure can result in wound...

As the field of digital pathology continues to advance, the computeraided analysis of whole slide images (WSI) has become an essential component for cancer diagnosis, staging, biomarker prediction, and therapy evaluation. However, even with the latest hardware developments, the processing of entire slides still demands significant computational res...

Background and Aims: ChatGPT represents the most popular and widely used generative artificial intelligence (AI) model that received significant attention in healthcare research. The aim of the current study was to assess the future trajectory of the needed research in this domain based on the recommendations of the top influential published record...

Background: In an era where artificial intelligence (AI) intertwines with medical research, the delineation of truth becomes increasingly complex. This study ostensibly examines a purported novel SARS-CoV-2 variant, dubbed the Omega variant, showcasing 31 unique mutations in the S gene region. However, the real undercurrent of this narrative is a d...

Precise aortic vessel tree segmentation is critical in the continuously evolving medical imaging domain. This study highlights the role of global sensitivity analysis in stimulating innovation in quality assessment techniques for aortic segmentation. In this methodology paper, we propose a novel method that integrates global sensitivity analysis wi...

First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

Medical imaging faces challenges such as limited spatial resolution, interference from electronic noise and poor contrast-to-noise ratios. Photon Counting Computed Tomography (PCCT) has emerged as a solution, addressing these issues with its innovative technology. This review delves into the recent developments and applications of PCCT in pre-clini...

https://jjournals.ju.edu.jo/index.php/JMJ/announcement/view/3 Special Issue Information Dear colleagues, Large language models (LLMs) gained popularity particularly for ChatGPT publicly released by OpenAI a year ago. Other popular conversational chatbots include Bing by Microsoft and Bard by Google. Since then, an extensive number of studies invest...

The concept of reality-virtuality (RV) continuum was introduced by Paul Milgram and Fumio Kishino in 1994. It describes a spectrum that ranges from a purely physical reality (the real world) to a purely virtual reality (a completely computer-generated environment), with various degrees of mixed reality in between. This continuum is "realized" by di...

Background: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinical observations. Unfortunately, most segmentation methods known today are limited to nuclei and cannot segmentate...

Purpose: To assess the diagnostic accuracy of BMI-adapted, low-radiation and low-iodine dose, dual-source aortic CT for endoleak detection in non-obese and obese patients following endovascular aortic repair. Methods: In this prospective single-center study, patients referred for follow-up CT after endovascular repair with a history of at least one...

Background The advances in large language models (LLMs) are evolving rapidly. Artificial intelligence (AI) chatbots based on LLMs excel in language understanding and generation, with potential utility to transform healthcare education and practice. However, it is important to assess the performance of such AI models in various topics to highlight i...

Background Assessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy of information in multilingual contexts. This study aimed to compare AI model efficiency in English and Arabic for infectious disease queries. Methods The study employed the METRICS checklist for the design and rep...

The advances in large language models (LLMs) are evolving rapidly. Artificial intelligence (AI) chatbots based on LLMs excel in language understanding and generation, with potential utility to transform healthcare education and practice. However, it is important to assess the performance of such AI models in various topics to highlight its strength...

Medical studies are an essential part of advancing research. A uniform, flexible software infrastructure that allows for straightforward data management stands at the core of studies that involve multiple sites. Such a solution must accommodate the specific technical needs of clinical practitioners and researchers, such as uploading, viewing, downl...

X-rays are the most commonly performed medical imaging tests to detect fractures. However, some fractures are difficult to detect and may go unnoticed by physicians. In addition, there are very few public X-rays datasets of rib fractures. Although, the creation of such datasets is very time-consuming because of the bureaucratic and ethical issues i...

BACKGROUND FHIR (Fast Healthcare Interoperability Resources) has been proposed to enable health data interoperability. So far, its applicability has been demonstrated for selected research projects with limited data. OBJECTIVE This study aimed to design and implement a conceptual medical intelligence framework to leverage real-world care data for...

Diminished reality (DR) refers to the removal of real objects from the environment by virtually replacing them with their background. Modern DR frameworks use inpaint-ing to hallucinate unobserved regions. While recent deep learning-based inpainting is promising, the DR use case is complicated by the need to generate coherent structure and 3D geome...

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In...

Traditional convolutional neural network (CNN) methods rely on dense tensors, which makes them suboptimal for spatially sparse data. In this paper, we propose a CNN model based on sparse tensors for efficient processing of high-resolution shapes represented as binary voxel occupancy grids. In contrast to a dense CNN that takes the entire voxel grid...

The availability of computational hardware and developments in (medical) machine learning (MML) increases medical mixed realities' (MMR) clinical usability. Medical instruments have played a vital role in surgery for ages. To further accelerate the implementation of MML and MMR, three-dimensional (3D) datasets of instruments should be publicly avai...

In this paper, we introduce a completion framework to reconstruct the geometric shapes of various anatomies, including organs, vessels and muscles. Our work targets a scenario where one or multiple anatomies are missing in the imaging data due to surgical, pathological or traumatic factors, or simply because these anatomies are not covered by image...

Background With the rise in importance of personalized medicine and deep learning, we combine the two to create personalized neural networks. The aim of the study is to show a proof of concept that data from just one patient can be used to train deep neural networks to detect tumor progression in longitudinal datasets. Methods Two datasets with 64...

Objective: 3D modeling is a major challenge in computer-assisted surgery (CAS). Manual segmentation, as the gold standard, is tedious, time consuming, and particularly challenging for the mandible, while artificial intelligence (AI)-based segmentation is a promising and time-saving alternative. However, little is known about the clinical implicatio...