Publications
1021 results found
Bercea CI, Wiestler B, Rueckert D, et al., 2023, Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection, Pages: 293-303, ISSN: 0302-9743
Early and accurate disease detection is crucial for patient management and successful treatment outcomes. However, the automatic identification of anomalies in medical images can be challenging. Conventional methods rely on large labeled datasets which are difficult to obtain. To overcome these limitations, we introduce a novel unsupervised approach, called PHANES (Pseudo Healthy generative networks for ANomaly Segmentation). Our method has the capability of reversing anomalies, i.e., preserving healthy tissue and replacing anomalous regions with pseudo-healthy (PH) reconstructions. Unlike recent diffusion models, our method does not rely on a learned noise distribution nor does it introduce random alterations to the entire image. Instead, we use latent generative networks to create masks around possible anomalies, which are refined using inpainting generative networks. We demonstrate the effectiveness of PHANES in detecting stroke lesions in T1w brain MRI datasets and show significant improvements over state-of-the-art (SOTA) methods. We believe that our proposed framework will open new avenues for interpretable, fast, and accurate anomaly segmentation with the potential to support various clinical-oriented downstream tasks. Code: https://github.com/ci-ber/PHANES
Bintsi KM, Baltatzis V, Potamias RA, et al., 2023, Multimodal Brain Age Estimation Using Interpretable Adaptive Population-Graph Learning, Pages: 195-204, ISSN: 0302-9743
Brain age estimation is clinically important as it can provide valuable information in the context of neurodegenerative diseases such as Alzheimer’s. Population graphs, which include multimodal imaging information of the subjects along with the relationships among the population, have been used in literature along with Graph Convolutional Networks (GCNs) and have proved beneficial for a variety of medical imaging tasks. A population graph is usually static and constructed manually using non-imaging information. However, graph construction is not a trivial task and might significantly affect the performance of the GCN, which is inherently very sensitive to the graph structure. In this work, we propose a framework that learns a population graph structure optimized for the downstream task. An attention mechanism assigns weights to a set of imaging and non-imaging features (phenotypes), which are then used for edge extraction. The resulting graph is used to train the GCN. The entire pipeline can be trained end-to-end. Additionally, by visualizing the attention weights that were the most important for the graph construction, we increase the interpretability of the graph. We use the UK Biobank, which provides a large variety of neuroimaging and non-imaging phenotypes, to evaluate our method on brain age regression and classification. The proposed method outperforms competing static graph approaches and other state-of-the-art adaptive methods. We further show that the assigned attention scores indicate that there are both imaging and non-imaging phenotypes that are informative for brain age estimation and are in agreement with the relevant literature.
Qin C, Wang S, Chen C, et al., 2023, Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior, MEDICAL IMAGE ANALYSIS, Vol: 83, ISSN: 1361-8415
Hammernik K, Kustner T, Yaman B, et al., 2023, Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 40, Pages: 98-114, ISSN: 1053-5888
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- Citations: 3
Kofler F, Wahle J, Ezhov I, et al., 2023, APPROACHING <i>PEAK GROUND TRUTH</i>, 20th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, ISSN: 1945-7928
Lekadir K, Feragen A, Fofanah AJ, et al., 2023, FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare., CoRR, Vol: abs/2309.12325
Feiner LF, Menten MJ, Hammernik K, et al., 2023, Propagation and Attribution of Uncertainty in Medical Imaging Pipelines, Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, Publisher: Springer Nature Switzerland, Pages: 1-11, ISBN: 9783031443350
Nasirigerdeh R, Torkzadehmahani J, Rueckert D, et al., 2023, Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning, 1st IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), Publisher: IEEE COMPUTER SOC, Pages: 107-118
Marcus A, Bentley P, Rueckert D, 2023, Stroke Outcome and Evolution Prediction from CT Brain Using a Spatiotemporal Diffusion Autoencoder, Lecture Notes in Computer Science, Publisher: Springer Nature Switzerland, Pages: 153-162, ISBN: 9783031448577
Ziller A, Erdur AC, Jungmann F, et al., 2023, EXPLOITING SEGMENTATION LABELS AND REPRESENTATION LEARNING TO FORECAST THERAPY RESPONSE OF PDAC PATIENTS, 20th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, ISSN: 1945-7928
Tanida T, Muller P, Kaissis G, et al., 2023, Interactive and Explainable Region-guided Radiology Report Generation, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE COMPUTER SOC, Pages: 7433-7442, ISSN: 1063-6919
Gatidis S, Kart T, Fischer M, et al., 2022, Better together: data harmonization and cross-study analysis of abdominal MRI data from UK biobank and the German national cohort., Investigative Radiology, Vol: 58, Pages: 346-354, ISSN: 0020-9996
OBJECTIVES: The UK Biobank (UKBB) and German National Cohort (NAKO) are among the largest cohort studies, capturing a wide range of health-related data from the general population, including comprehensive magnetic resonance imaging (MRI) examinations. The purpose of this study was to demonstrate how MRI data from these large-scale studies can be jointly analyzed and to derive comprehensive quantitative image-based phenotypes across the general adult population. MATERIALS AND METHODS: Image-derived features of abdominal organs (volumes of liver, spleen, kidneys, and pancreas; volumes of kidney hilum adipose tissue; and fat fractions of liver and pancreas) were extracted from T1-weighted Dixon MRI data of 17,996 participants of UKBB and NAKO based on quality-controlled deep learning generated organ segmentations. To enable valid cross-study analysis, we first analyzed the data generating process using methods of causal discovery. We subsequently harmonized data from UKBB and NAKO using the ComBat approach for batch effect correction. We finally performed quantile regression on harmonized data across studies providing quantitative models for the variation of image-derived features stratified for sex and dependent on age, height, and weight. RESULTS: Data from 8791 UKBB participants (49.9% female; age, 63 ± 7.5 years) and 9205 NAKO participants (49.1% female, age: 51.8 ± 11.4 years) were analyzed. Analysis of the data generating process revealed direct effects of age, sex, height, weight, and the data source (UKBB vs NAKO) on image-derived features. Correction of data source-related effects resulted in markedly improved alignment of image-derived features between UKBB and NAKO. Cross-study analysis on harmonized data revealed comprehensive quantitative models for the phenotypic variation of abdominal organs across the general adult population. CONCLUSIONS: Cross-study analysis of MRI data from UKBB and NAKO as proposed in this work can be helpful for futur
Neubauer A, Menegaux A, Wendt J, et al., 2022, Aberrant claustrum structure in preterm-born neonates: an MRI study., NeuroImage: Clinical, Vol: 37, Pages: 1-16, ISSN: 2213-1582
The human claustrum is a gray matter structure in the white matter between insula and striatum. Previous analysis found altered claustrum microstructure in very preterm-born adults associated with lower cognitive performance. As the claustrum development is related to hypoxia-ischemia sensitive transient cell populations being at-risk in premature birth, we hypothesized that claustrum structure is already altered in preterm-born neonates. We studied anatomical and diffusion-weighted MRIs of 83 preterm- and 83 term-born neonates at term-equivalent age. Additionally, claustrum development was analyzed both in a spectrum of 377 term-born neonates and longitudinally in 53 preterm-born subjects. Data was provided by the developing Human Connectome Project. Claustrum development showed increasing volume, increasing fractional anisotropy (FA), and decreasing mean diffusivity (MD) around term both across term- and preterm-born neonates. Relative to term-born ones, preterm-born neonates had (i) increased absolute and relative claustrum volumes, both indicating increased cellular and/or extracellular matter and being in contrast to other subcortical gray matter regions of decreased volumes such as thalamus; (ii) lower claustrum FA and higher claustrum MD, pointing at increased extracellular matrix and impaired axonal integrity; and (iii) aberrant covariance between claustrum FA and MD, respectively, and that of distributed gray matter regions, hinting at relatively altered claustrum microstructure. Results together demonstrate specifically aberrant claustrum structure in preterm-born neonates, suggesting altered claustrum development in prematurity, potentially relevant for later cognitive performance.
Vlontzos A, Rueckert D, Kainz B, 2022, A Review of Causality for Learning Algorithms in Medical Image Analysis, Machine Learning for Biomedical Imaging, Vol: 1, Pages: 1-17
<jats:p>Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust and adaptable medical image analysis algorithms.<br>We review methods using causality in medical imaging AI/ML and find that causal analysis has the potential to mitigate critical problems for clinical translation but that uptake and clinical downstream research has been limited so far.</jats:p>
Richter S, Winzeck S, Czeiter E, et al., 2022, Serum biomarkers identify critically ill traumatic brain injury patients for MRI, CRITICAL CARE, Vol: 26, ISSN: 1364-8535
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- Citations: 1
Ouyang C, Chen C, Li S, et al., 2022, Causality-inspired single-source domain generalization for medical image segmentation, IEEE Transactions on Medical Imaging, Vol: 42, Pages: 1095-1106, ISSN: 0278-0062
Deep learning models usually suffer from the domain shift issue, where models trained on one source domain do not generalize well to other unseen domains. In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data are only available from one source domain, which is common in medical imaging applications. We tackle this problem in the context of cross-domain medical image segmentation. In this scenario, domain shifts are mainly caused by different acquisition processes. We propose a simple causality-inspired data augmentation approach to expose a segmentation model to synthesized domain-shifted training examples. Specifically, 1) to make the deep model robust to discrepancies in image intensities and textures, we employ a family of randomly-weighted shallow networks. They augment training images using diverse appearance transformations. 2) Further we show that spurious correlations among objects in an image are detrimental to domain robustness. These correlations might be taken by the network as domain-specific clues for making predictions, and they may break on unseen domains. We remove these spurious correlations via causal intervention. This is achieved by resampling the appearances of potentially correlated objects independently. The proposed approach is validated on three cross-domain segmentation scenarios: cross-modality (CT-MRI) abdominal image segmentation, cross-sequence (bSSFP-LGE) cardiac MRI segmentation, and cross-site prostate MRI segmentation. The proposed approach yields consistent performance gains compared with competitive methods when tested on unseen domains.
Kart T, Fischer M, Winzeck S, et al., 2022, Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies, SCIENTIFIC REPORTS, Vol: 12, ISSN: 2045-2322
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- Citations: 2
Maas AIR, Menon DK, Manley GT, et al., 2022, Traumatic brain injury: progress and challenges in prevention, clinical care, and research, LANCET NEUROLOGY, Vol: 21, Pages: 1004-1060, ISSN: 1474-4422
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- Citations: 80
Chen C, Qin C, Ouyang C, et al., 2022, Enhancing MR image segmentation with realistic adversarial data augmentation, Medical Image Analysis, Vol: 82, Pages: 1-15, ISSN: 1361-8415
The success of neural networks on medical image segmentation tasks typicallyrelies on large labeled datasets for model training. However, acquiring andmanually labeling a large medical image set is resource-intensive, expensive,and sometimes impractical due to data sharing and privacy issues. To addressthis challenge, we propose AdvChain, a generic adversarial data augmentationframework, aiming at improving both the diversity and effectiveness of trainingdata for medical image segmentation tasks. AdvChain augments data with dynamicdata augmentation, generating randomly chained photo-metric and geometrictransformations to resemble realistic yet challenging imaging variations toexpand training data. By jointly optimizing the data augmentation model and asegmentation network during training, challenging examples are generated toenhance network generalizability for the downstream task. The proposedadversarial data augmentation does not rely on generative networks and can beused as a plug-in module in general segmentation networks. It iscomputationally efficient and applicable for both low-shot supervised andsemi-supervised learning. We analyze and evaluate the method on two MR imagesegmentation tasks: cardiac segmentation and prostate segmentation with limitedlabeled data. Results show that the proposed approach can alleviate the needfor labeled data while improving model generalization ability, indicating itspractical value in medical imaging applications.
Zhuang X, Xu J, Luo X, et al., 2022, Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge, MEDICAL IMAGE ANALYSIS, Vol: 81, ISSN: 1361-8415
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- Citations: 11
Hinterwimmer F, Consalvo S, Neumann J, et al., 2022, Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies-a scoping review., Eur Radiol, Vol: 32, Pages: 7173-7184
Musculoskeletal malignancies are a rare type of cancer. Consequently, sufficient imaging data for machine learning (ML) applications is difficult to obtain. The main purpose of this review was to investigate whether ML is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies and what the respective reasons for this might be. A scoping review was conducted by a radiologist, an orthopaedic surgeon and a data scientist to identify suitable articles based on the PRISMA statement. Studies meeting the following criteria were included: primary malignant musculoskeletal tumours, machine/deep learning application, imaging data or data retrieved from images, human/preclinical, English language and original research. Initially, 480 articles were found and 38 met the eligibility criteria. Several continuous and discrete parameters related to publication, patient distribution, tumour specificities, ML methods, data and metrics were extracted from the final articles. For the synthesis, diagnosis-oriented studies were further examined by retrieving the number of patients and labels and metric scores. No significant correlations between metrics and mean number of samples were found. Several studies presented that ML could support imaging-driven diagnosis of musculoskeletal malignancies in distinct cases. However, data quality and quantity must be increased to achieve clinically relevant results. Compared to the experience of an expert radiologist, the studies used small datasets and mostly included only one type of data. Key to critical advancement of ML models for rare diseases such as musculoskeletal malignancies is a systematic, structured data collection and the establishment of (inter)national networks to obtain substantial datasets in the future. KEY POINTS: • Machine learning does not yet significantly impact imaging-driven diagnosis for musculoskeletal malignancies compared to other disciplines such as lung, breast or CNS cancer. • R
Guo Y, Al-Jibury E, Garcia-Millan R, et al., 2022, Chromatin jets define the properties of cohesin-driven in vivo loop extrusion, Molecular Cell, Vol: 82, Pages: 3769-3780.e5, ISSN: 1097-2765
Complex genomes show intricate organization in three-dimensional (3D) nuclear space. Current models posit that cohesin extrudes loops to form self-interacting domains delimited by the DNA binding protein CTCF. Here, we describe and quantitatively characterize cohesin-propelled, jet-like chromatin contacts as landmarks of loop extrusion in quiescent mammalian lymphocytes. Experimental observations and polymer simulations indicate that narrow origins of loop extrusion favor jet formation. Unless constrained by CTCF, jets propagate symmetrically for 1-2 Mb, providing an estimate for the range of in vivo loop extrusion. Asymmetric CTCF binding deflects the angle of jet propagation as experimental evidence that cohesin-mediated loop extrusion can switch from bi- to unidirectional and is controlled independently in both directions. These data offer new insights into the physiological behavior of in vivo cohesin-mediated loop extrusion and further our understanding of the principles that underlie genome organization.
Chen C, Li Z, Ouyang C, et al., 2022, MaxStyle: adversarial style composition for robust medical image segmentation, Medical Image Computing and Computer Assisted Interventions (MICCAI) 2022, Publisher: Springer, Pages: 151-161
Convolutional neural networks (CNNs) have achieved remarkable segmentationaccuracy on benchmark datasets where training and test sets are from the samedomain, yet their performance can degrade significantly on unseen domains,which hinders the deployment of CNNs in many clinical scenarios. Most existingworks improve model out-of-domain (OOD) robustness by collecting multi-domaindatasets for training, which is expensive and may not always be feasible due toprivacy and logistical issues. In this work, we focus on improving modelrobustness using a single-domain dataset only. We propose a novel dataaugmentation framework called MaxStyle, which maximizes the effectiveness ofstyle augmentation for model OOD performance. It attaches an auxiliarystyle-augmented image decoder to a segmentation network for robust featurelearning and data augmentation. Importantly, MaxStyle augments data withimproved image style diversity and hardness, by expanding the style space withnoise and searching for the worst-case style composition of latent features viaadversarial training. With extensive experiments on multiple public cardiac andprostate MR datasets, we demonstrate that MaxStyle leads to significantlyimproved out-of-distribution robustness against unseen corruptions as well ascommon distribution shifts across multiple, different, unseen sites and unknownimage sequences under both low- and high-training data settings. The code canbe found at https://github.com/cherise215/MaxStyle.
Consalvo S, Hinterwimmer F, Neumann J, et al., 2022, Two-Phase Deep Learning Algorithm for Detection and Differentiation of Ewing Sarcoma and Acute Osteomyelitis in Paediatric Radiographs., Anticancer Res, Vol: 42, Pages: 4371-4380
BACKGROUND/AIM: Ewing sarcoma is a highly malignant tumour predominantly found in children. The radiological signs of this malignancy can be mistaken for acute osteomyelitis. These entities require profoundly different treatments and result in completely different prognoses. The purpose of this study was to develop an artificial intelligence algorithm, which can determine imaging features in a common radiograph to distinguish osteomyelitis from Ewing sarcoma. MATERIALS AND METHODS: A total of 182 radiographs from our Sarcoma Centre (118 healthy, 44 Ewing, 20 osteomyelitis) from 58 different paediatric (≤18 years) patients were collected. All localisations were taken into consideration. Cases of acute, acute on chronic osteomyelitis and intraosseous Ewing sarcoma were included. Chronic osteomyelitis, extra-skeletal Ewing sarcoma, malignant small cell tumour and soft tissue-based primitive neuroectodermal tumours were excluded. The algorithm development was split into two phases and two different classifiers were built and combined with a Transfer Learning approach to cope with the very limited amount of data. In phase 1, pathological findings were differentiated from healthy findings. In phase 2, osteomyelitis was distinguished from Ewing sarcoma. Data augmentation and median frequency balancing were implemented. A data split of 70%, 15%, 15% for training, validation and hold-out testing was applied, respectively. RESULTS: The algorithm achieved an accuracy of 94.4% on validation and 90.6% on test data in phase 1. In phase 2, an accuracy of 90.3% on validation and 86.7% on test data was achieved. Grad-CAM results revealed regions, which were significant for the algorithms decision making. CONCLUSION: Our AI algorithm can become a valuable support for any physician involved in treating musculoskeletal lesions to support the diagnostic process of detection and differentiation of osteomyelitis from Ewing sarcoma. Through a Transfer Learning approach, the algorithm wa
Taoudi-Benchekroun Y, Christiaens D, Grigorescu I, et al., 2022, Predicting age and clinical risk from the neonatal connectome, NEUROIMAGE, Vol: 257, ISSN: 1053-8119
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- Citations: 3
Meng Q, Bai W, Liu T, et al., 2022, MulViMotion: shape-aware 3D myocardial motion tracking from multi-view cardiac MRI, IEEE Transactions on Medical Imaging, Vol: 41, Pages: 1961-1974, ISSN: 0278-0062
Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart. In the proposed method, a hybrid 2D/3D network is built to generate dense 3D motion fields by learning fused representations from multi-view images. To ensure that the motion estimation is consistent in 3D, a shape regularization module is introduced during training, where shape information from multi-view images is exploited to provide weak supervision to 3D motion estimation. We extensively evaluate the proposed method on 2D cine CMR images from 580 subjects of the UK Biobank study for 3D motion tracking of the left ventricular myocardium. Experimental results show that the proposed method quantitatively and qualitatively outperforms competing methods.
Bloier M, Hinterwimmer F, Breden S, et al., 2022, Detection and Segmentation of Heterogeneous Bone Tumours in Limited Radiographs, Current Directions in Biomedical Engineering, Vol: 8, Pages: 69-72
Bone tumours are a rare and often highly malignant entity. Early clinical diagnosis is the most important step, but the difficulty of detecting and assessing bone malignancies is in its radiological peculiarity and limited experience of non-experts. Since X-ray imaging is the first imaging method of bone tumour diagnostics, the purpose of this study is to develop an artificial intelligence (AI) model to detect and segment the tumorous tissue in a radiograph. We investigated which methods are necessary to cope with limited and heterogeneous data. We collected 531 anonymised radiographs from our musculoskeletal tumour centre. In order to adapt to the complexity of recognizing the malignant tissue and cope with limited data, transfer learning, data augmentation as well as several architectures, some of which were initially designed for medical images, were implemented. Furthermore, dataset size was varied by adding another bone tumour entity. We applied a data split of 72%, 18%, 10% for training, validation and testing, respectively. To provide statistical significance and robustness, we applied a cross-validation and image stratification with respect to tumour pixels present. We achieved an accuracy of 99.72% and an intersection over union of 87.43% for hold-out test data by applying several methods to tackle limited data. Transfer learning and additional data brought the greatest performance increase. In conclusion, our model was able to detect and segment tumorous tissue in radiographs with good performance, although it was trained on a very limited amount of data. Transfer Learning and data augmentation proved to significantly mitigate the issue of limited data samples. However, to accomplish clinical significance, more data has to be acquired in the future. Through minor adjustments, the model could be adapted to other musculoskeletal tumour entities and become a general support tool for orthopaedic surgeons and radiologists.
Hinterwimmer F, Consalvo S, Neumann J, et al., 2022, From Self-supervised Learning to Transfer Learning with Musculoskeletal Radiographs, Current Directions in Biomedical Engineering, Vol: 8, Pages: 9-12
Ewing sarcomas are malignant neoplasm entities typically found in children and adolescents. Early detection is crucial for therapy and prognosis. Due to the low incidence the general experience as well as according data is limited. Novel support tools for diagnosis, such as deep learning models for image interpretation, are required. While acquiring sufficient data is a common obstacle in medicine, several techniques to tackle small data sets have emerged. The general necessity of large data sets in addition to a rare disease lead to the question whether transfer learning can solve the issue of limited data and subsequently support tasks such as distinguishing Ewing sarcoma from its main differential diagnosis (acute osteomyelitis) in paediatric radiographs. 42,608 unstructured radiographs from our musculoskeletal tumour centre were retrieved from the PACS. The images were clustered with a DeepCluster, a self-supervised algorithm. 1000 clusters were used for the upstream task (pretraining). Following, the pretrained classification network was applied for the downstream task of differentiating Ewing sarcoma and acute osteomyelitis. An untrained network achieved an accuracy of 81.5%/54.2%, while an ImageNet-pretrained network resulted in 89.6%/70.8% for validation and testing, respectively. Our transfer learning approach surpassed the best result by 4.4%/17.3% percentage points. Transfer learning demonstrated to be a powerful technique to support image interpretation tasks. Even for small data sets, the impact can be significant. However, transfer learning is not a final solution to small data sets. To achieve clinically relevant results, a structured and systematic data acquisition is of paramount importance.
Bercea C, Wiestler B, Rueckert D, et al., 2022, Federated disentangled representation learning for unsupervised brain anomaly detection, NATURE MACHINE INTELLIGENCE, Vol: 4, Pages: 685-+
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- Citations: 7
van der Vlegel M, Mikolić A, Lee Hee Q, et al., 2022, Health care utilization and outcomes in older adults after Traumatic Brain Injury: A CENTER-TBI study., Injury, Vol: 53, Pages: 2774-2782
INTRODUCTION: The incidence of Traumatic Brain Injury (TBI) is increasingly common in older adults aged ≥65 years, forming a growing public health problem. However, older adults are underrepresented in TBI research. Therefore, we aimed to provide an overview of health-care utilization, and of six-month outcomes after TBI and their determinants in older adults who sustained a TBI. METHODS: We used data from the prospective multi-center Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. In-hospital and post-hospital health care utilization and outcomes were described for patients aged ≥65 years. Ordinal and linear regression analyses were performed to identify determinants of the Glasgow Outcome Scale Extended (GOSE), health-related quality of life (HRQoL), and mental health symptoms six-months post-injury. RESULTS: Of 1254 older patients, 45% were admitted to an ICU with a mean length of stay of 9 days. Nearly 30% of the patients received inpatient rehabilitation. In total, 554/1254 older patients completed the six-month follow-up questionnaires. The mortality rate was 9% after mild and 60% after moderate/severe TBI, and full recovery based on GOSE was reported for 44% of patients after mild and 6% after moderate/severe TBI. Higher age and increased injury severity were primarily associated with functional impairment, while pre-injury systemic disease, psychiatric conditions and lower educational level were associated with functional impairment, lower generic and disease-specific HRQoL and mental health symptoms. CONCLUSION: The rate of impairment and disability following TBI in older adults is substantial, and poorer outcomes across domains are associated with worse preinjury health. Nonetheless, a considerable number of patients fully or partially returns to their preinjury functioning. There should not be pessimism about outcomes in older adults who survive.
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