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Nasir IT, Shoab SS, Bani-Hani MG. Evaluation of outcomes and utility of abdominal aortic aneurysm surveillance in octogenarians and nonagenarians. Ann R Coll Surg Engl 2024; 106:642-646. [PMID: 38038059 PMCID: PMC11365734 DOI: 10.1308/rcsann.2023.0089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/11/2023] [Indexed: 12/02/2023] Open
Abstract
INTRODUCTION The aim of this study was to evaluate the utility of our regional abdominal aortic aneurysm (AAA) screening programme in octogenarians and nonagenarians. This was to help decide whether discontinuation might be appropriate in certain instances. Primary outcomes were the number of patients who reached threshold (5.5cm) and the number where intervention was offered. Secondary outcome was cost effectiveness. METHODS A retrospective review of a regional AAA surveillance database was carried out to evaluate outcomes. Data collected included patient age, sex, date of first and last scan, initial and latest size of aneurysm, outcome, time under surveillance and total number of scans. Patients were divided into three groups (80-84 years, 85-89 years and 90+ years). RESULTS The number of patients in this age group was 354. Only 2.0% (n=7) of patients underwent intervention. Threshold size was achieved in 8.3% (n=18), 14.8% (n=18) and 26.7% (n=4), in the age groups 80-84 years, 85-89 years and 90+ years, respectively. Of these patients, operative intervention was possible in 2.8% (n=6), 0.8% (n=1) and 0% (n=0), respectively. CONCLUSION A relatively small number of octogenarians and nonagenarians reach the threshold size during surveillance. An even smaller proportion require repair of their aneurysm. While there may be a role for AAA surveillance in octogenarians in highly selected groups, these data should inform the discussions made with individual patients. It should also inform future evaluation of such surveillance.
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Affiliation(s)
- IT Nasir
- Lancashire Teaching Hospitals NHS Foundation Trust,UK
| | - SS Shoab
- Lancashire Teaching Hospitals NHS Foundation Trust,UK
| | - MG Bani-Hani
- Lancashire Teaching Hospitals NHS Foundation Trust,UK
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Alawattegama LH, Gaddah M, Kimani L, Antoniou GA. The effect of diabetes on abdominal aortic aneurysm growth - updated systematic review and meta-analysis. VASA 2024. [PMID: 39206613 DOI: 10.1024/0301-1526/a001143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Studies have shown that diabetes mellitus is associated with a reduced prevalence and growth of abdominal aortic aneurysms (AAA). Establishing the factors that influence AAA growth will enable us to risk stratify patients and potentially optimise management. We aimed to provide an updated systematic review and meta-analysis that would inform more targeted screening practices based on patient demographics. MEDLINE, EMBASE, and DARE were searched using the Ovid interface and PubMed search engine. Studies were deemed eligible if they compared the growth rate of AAA between diabetic and non-diabetic populations. The mean difference (MD) and 95% confidence internal (CI) was used for data synthesis. Twenty-four studies from 20 articles with a total of 10,121 participants were included in our meta-analysis. An overall negative effect was shown between AAA growth and diabetes, with an annual mean effect of -0.25 mm/year (95% CI -0.35, -0.15; I2 = 73%). Our meta-analysis, which is larger and scientifically more robust compared to previous analyses, has confirmed that diabetes reduces the growth of AAA by approximately 0.25 mm a year compared to non-diabetic populations. This could have significant implications for AAA screening practices.
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Affiliation(s)
- Lakna Harindi Alawattegama
- Department of Vascular and Endovascular Surgery, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Mariam Gaddah
- Department of General Surgery, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Linda Kimani
- Department of Cardiothoracic Surgery, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Division of Cardiovascular Sciences, School of Medical Sciences, Manchester Academic Health Science Centre, The University of Manchester, United Kingdom
| | - George A Antoniou
- Department of Vascular and Endovascular Surgery, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Division of Cardiovascular Sciences, School of Medical Sciences, Manchester Academic Health Science Centre, The University of Manchester, United Kingdom
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Ullah N, Kiu Chou W, Vardanyan R, Arjomandi Rad A, Shah V, Torabi S, Avavde D, Airapetyan AA, Zubarevich A, Weymann A, Ruhparwar A, Miller G, Malawana J. Machine learning algorithms for the prognostication of abdominal aortic aneurysm progression: a systematic review. Minerva Surg 2024; 79:219-227. [PMID: 37987755 DOI: 10.23736/s2724-5691.23.10130-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
INTRODUCTION Abdominal aortic aneurysm (AAA), often characterized by an abdominal aortic diameter over 3.0 cm, is managed through screening, surveillance, and surgical intervention. AAA growth can be heterogeneous and rupture carries a high mortality rate, with size and certain risk factors influencing rupture risk. Research is ongoing to accurately predict individual AAA growth rates for personalized management. Machine learning, a subset of artificial intelligence, has shown promise in various medical fields, including endoleak detection post-EVAR. However, its application for predicting AAA growth remains insufficiently explored, thus necessitating further investigation. Subsequently, this paper aims to summarize the current status of machine learning in predicting AAA growth. EVIDENCE ACQUISITION A systematic database search of Embase, MEDLINE, Cochrane, PubMed and Google Scholar from inception till December 2022 was conducted of original articles that discussed the use of machine learning in predicting AAA growth using the aforementioned databases. EVIDENCE SYNTHESIS Overall, 2742 articles were extracted, of which seven retrospective studies involving 410 patients were included using a predetermined criteria. Six out of seven studies applied a supervised learning approach for their machine learning (ML) models, with considerable diversity observed within specific ML models. The majority of the studies concluded that machine learning models perform better in predicting AAA growth in comparison to reference models. All studies focused on predicting AAA growth over specified durations. Maximal luminal diameter was the most frequently used indicator, with alternative predictors being AAA volume, ILT (intraluminal thrombus) and flow-medicated diameter (FMD). CONCLUSIONS The nascent field of applying machine learning (ML) for Abdominal Aortic Aneurysm (AAA) expansion prediction exhibits potential to enhance predictive accuracy across diverse parameters. Future studies must emphasize evidencing clinical utility in a healthcare system context, thereby ensuring patient outcome improvement. This will necessitate addressing key ethical implications in establishing prospective studies related to this topic and collaboration among pivotal stakeholders within the AI field.
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Affiliation(s)
- Nazifa Ullah
- Faculty of Medicine, University College London, London, UK
| | - Wing Kiu Chou
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Robert Vardanyan
- Department of Medicine, Faculty of Medicine, Imperial College London, London, UK -
- Research Unit, The Healthcare Leadership Academy, London, UK
| | - Arian Arjomandi Rad
- Department of Medicine, Faculty of Medicine, Imperial College London, London, UK
- Research Unit, The Healthcare Leadership Academy, London, UK
- Medical Sciences Division, University of Oxford, Oxford, UK
| | - Viraj Shah
- Department of Medicine, Faculty of Medicine, Imperial College London, London, UK
| | - Saeed Torabi
- Department of Anesthesiology, University Hospital Cologne, Cologne, Germany
| | - Dani Avavde
- Department of Vascular Surgery, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Arkady A Airapetyan
- Department of Research and Academia, National Institute of Health, Yerevan, Armenia
| | - Alina Zubarevich
- Department of Cardiothoracic Transplant and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - Alexander Weymann
- Department of Cardiothoracic Transplant and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - Arjang Ruhparwar
- Department of Cardiothoracic Transplant and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - George Miller
- Research Unit, The Healthcare Leadership Academy, London, UK
- Centre for Digital Health and Education Research (CoDHER), University of Central Lancashire Medical School, Preston, UK
| | - Johann Malawana
- Research Unit, The Healthcare Leadership Academy, London, UK
- Centre for Digital Health and Education Research (CoDHER), University of Central Lancashire Medical School, Preston, UK
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Oude Wolcherink MJ, Behr CM, Pouwels XGLV, Doggen CJM, Koffijberg H. Health Economic Research Assessing the Value of Early Detection of Cardiovascular Disease: A Systematic Review. PHARMACOECONOMICS 2023; 41:1183-1203. [PMID: 37328633 PMCID: PMC10492754 DOI: 10.1007/s40273-023-01287-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/22/2023] [Indexed: 06/18/2023]
Abstract
INTRODUCTION Cardiovascular disease (CVD) is the most prominent cause of death worldwide and has a major impact on healthcare budgets. While early detection strategies may reduce the overall CVD burden through earlier treatment, it is unclear which strategies are (most) efficient. AIM This systematic review reports on the cost effectiveness of recent early detection strategies for CVD in adult populations at risk. METHODS PubMed and Scopus were searched to identify scientific articles published between January 2016 and May 2022. The first reviewer screened all articles, a second reviewer independently assessed a random 10% sample of the articles for validation. Discrepancies were solved through discussion, involving a third reviewer if necessary. All costs were converted to 2021 euros. Reporting quality of all studies was assessed using the CHEERS 2022 checklist. RESULTS In total, 49 out of 5552 articles were included for data extraction and assessment of reporting quality, reporting on 48 unique early detection strategies. Early detection of atrial fibrillation in asymptomatic patients was most frequently studied (n = 15) followed by abdominal aortic aneurysm (n = 8), hypertension (n = 7) and predicted 10-year CVD risk (n = 5). Overall, 43 strategies (87.8%) were reported as cost effective and 11 (22.5%) CVD-related strategies reported cost reductions. Reporting quality ranged between 25 and 86%. CONCLUSIONS Current evidence suggests that early CVD detection strategies are predominantly cost effective and may reduce CVD-related costs compared with no early detection. However, the lack of standardisation complicates the comparison of cost-effectiveness outcomes between studies. Real-world cost effectiveness of early CVD detection strategies will depend on the target country and local context. REGISTRATION OF SYSTEMATIC REVIEW CRD42022321585 in International Prospective Registry of Ongoing Systematic Reviews (PROSPERO) submitted at 10 May 2022.
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Affiliation(s)
- Martijn J Oude Wolcherink
- Health Technology and Services Research, Techmed Centre, University of Twente, Enschede, The Netherlands
| | - Carina M Behr
- Health Technology and Services Research, Techmed Centre, University of Twente, Enschede, The Netherlands
| | - Xavier G L V Pouwels
- Health Technology and Services Research, Techmed Centre, University of Twente, Enschede, The Netherlands
| | - Carine J M Doggen
- Health Technology and Services Research, Techmed Centre, University of Twente, Enschede, The Netherlands
| | - Hendrik Koffijberg
- Health Technology and Services Research, Techmed Centre, University of Twente, Enschede, The Netherlands.
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Shang W, Jin H, Vastani A, Mirza AB, Fisher B, Kalra N, Anderson I, Kailaya-Vasan A. Cost-effectiveness of repeat delayed imaging for spontaneous subarachnoid hemorrhage. PLoS One 2023; 18:e0289144. [PMID: 37494367 PMCID: PMC10370759 DOI: 10.1371/journal.pone.0289144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 07/12/2023] [Indexed: 07/28/2023] Open
Abstract
BACKGROUND In patients with intracranial aneurysm presenting with spontaneous subarachnoid hemorrhage (SAH), 15% of them could be missed by the initial diagnostic imaging. Repeat delayed imaging can help to identify previously undetected aneurysms, however, the cost-effectiveness of this strategy remains uncertain. OBJECTIVE The aim of this study is to assess the cost-effectiveness of repeat delayed imaging in patients with SAH who had a negative result during their initial imaging. METHODS A Markov model was developed to estimate the lifetime costs and quality-adjusted life-year (QALY) for patients who received or not received repeat delayed imaging. The analyses were conducted from a healthcare perspective, with costs reported in UK pounds and expressed in 2020 values. Extensive sensitivity analyses were performed to assess the robustness of the results. RESULTS The base case incremental cost-effectiveness ratio (ICER) of repeat delayed imaging is £9,314 per QALY compared to no-repeat delayed imaging. This ICER is below the National Institute for Health and Care Excellence (NICE) £20,000 per QALY willingness-to-pay threshold. At the NICE willingness-to-pay threshold of £20,000 per QALY, the probability that repeat delayed imaging is most cost-effective is 0.81. The results are sensitive to age, the utility of survived patients with a favorable outcome, the sensitivity of repeat delayed imaging, and the prevalence of aneurysm. CONCLUSIONS This study showed that, in the UK, it is cost-effective to provide repeat delayed imaging using computed tomographic angiography (CTA) for patients with SAH who had a negative result in their initial imaging.
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Affiliation(s)
- Wenru Shang
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- WHO Collaborating Center for Guideline Implementation and Knowledge Translation, Lanzhou University, Lanzhou, China
- King's Health Economics (KHE), Institute of Psychiatry, Psychology & Neuroscience at King's College London, London, United Kingdom
| | - Huajie Jin
- King's Health Economics (KHE), Institute of Psychiatry, Psychology & Neuroscience at King's College London, London, United Kingdom
| | - Amisha Vastani
- Department of Neurosurgery, King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Asfand Baig Mirza
- Department of Neurosurgery, King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Benjamin Fisher
- Department of Neurosurgery, King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Neeraj Kalra
- Department of Neurosurgery, Leeds Centre for Neurosciences, Leeds General Infirmary, Leeds, United Kingdom
| | - Ian Anderson
- Department of Neurosurgery, Leeds Centre for Neurosciences, Leeds General Infirmary, Leeds, United Kingdom
| | - Ahilan Kailaya-Vasan
- Department of Neurosurgery, King's College Hospital NHS Foundation Trust, London, United Kingdom
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Gormley S, Bernau O, Xu W, Sandiford P, Khashram M. Incidence and Outcomes of Abdominal Aortic Aneurysm Repair in New Zealand from 2001 to 2021. J Clin Med 2023; 12:jcm12062331. [PMID: 36983332 PMCID: PMC10054325 DOI: 10.3390/jcm12062331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 03/08/2023] [Accepted: 03/12/2023] [Indexed: 03/19/2023] Open
Abstract
Purpose: The burden of abdominal aortic aneurysms (AAA) has changed in the last 20 years but is still considered to be a major cause of cardiovascular mortality. The introduction of endovascular aortic repair (EVAR) and improved peri-operative care has resulted in a steady improvement in both outcomes and long-term survival. The objective of this study was to identify the burden of AAA disease by analysing AAA-related hospitalisations and deaths. Methodology: All AAA-related hospitalisations in NZ from January 2001 to December 2021 were identified from the National Minimum Dataset, and mortality data were obtained from the NZ Mortality Collection dataset from January 2001 to December 2018. Data was analysed for patient characteristics including deprivation index, repair methods and 30-day outcomes. Results: From 2001 to 2021, 14,436 patients with an intact AAA were identified with a mean age of 75.1 years (SD 9.7 years), and 4100 (28%) were females. From 2001 to 2018, there were 5000 ruptured AAA with a mean age of 77.8 (SD 9.4), and 1676 (33%) were females. The rate of hospitalisations related to AAA has decreased from 43.7 per 100,000 in 2001 to 15.4 per 100,000 in 2018. There was a higher proportion of rupture AAA in patients living in more deprived areas. The use of EVAR for intact AAA repair has increased from 18.1% in 2001 to 64.3% in 2021. The proportion of octogenarians undergoing intact AAA repair has increased from 16.2% in 2001 to 28.4% in 2021. The 30-day mortality for intact AAA repair has declined from 5.8% in 2001 to 1.7% in 2021; however, it has remained unchanged for ruptured AAA repair at 31.6% across the same period. Conclusions: This study highlights that the incidence of AAA has declined in the last two decades. The mortality has improved for patients who had a planned repair. Understanding the contemporary burden of AAA is paramount to improve access to health, reduce variation in outcomes and promote surgical quality improvement.
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Affiliation(s)
- Sinead Gormley
- Department of Vascular & Endovascular Surgery, Waikato Hospital, Hamilton 3204, New Zealand
- Faculty of Medical & Health Sciences, University of Auckland, Auckland 1010, New Zealand
| | - Oliver Bernau
- Faculty of Medical & Health Sciences, University of Auckland, Auckland 1010, New Zealand
| | - William Xu
- Faculty of Medical & Health Sciences, University of Auckland, Auckland 1010, New Zealand
| | - Peter Sandiford
- Planning Funding and Outcomes Unit, Auckland and Waitemata District Health Boards, Auckland 1010, New Zealand
- School of Population Health, University of Auckland, Auckland 1010, New Zealand
| | - Manar Khashram
- Department of Vascular & Endovascular Surgery, Waikato Hospital, Hamilton 3204, New Zealand
- Faculty of Medical & Health Sciences, University of Auckland, Auckland 1010, New Zealand
- Correspondence:
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7
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Golla AK, Tönnes C, Russ T, Bauer DF, Froelich MF, Diehl SJ, Schoenberg SO, Keese M, Schad LR, Zöllner FG, Rink JS. Automated Screening for Abdominal Aortic Aneurysm in CT Scans under Clinical Conditions Using Deep Learning. Diagnostics (Basel) 2021; 11:2131. [PMID: 34829478 PMCID: PMC8621263 DOI: 10.3390/diagnostics11112131] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/10/2021] [Accepted: 11/14/2021] [Indexed: 11/16/2022] Open
Abstract
Abdominal aortic aneurysms (AAA) may remain clinically silent until they enlarge and patients present with a potentially lethal rupture. This necessitates early detection and elective treatment. The goal of this study was to develop an easy-to-train algorithm which is capable of automated AAA screening in CT scans and can be applied to an intra-hospital environment. Three deep convolutional neural networks (ResNet, VGG-16 and AlexNet) were adapted for 3D classification and applied to a dataset consisting of 187 heterogenous CT scans. The 3D ResNet outperformed both other networks. Across the five folds of the first training dataset it achieved an accuracy of 0.856 and an area under the curve (AUC) of 0.926. Subsequently, the algorithms performance was verified on a second data set containing 106 scans, where it ran fully automated and resulted in an accuracy of 0.953 and an AUC of 0.971. A layer-wise relevance propagation (LRP) made the decision process interpretable and showed that the network correctly focused on the aortic lumen. In conclusion, the deep learning-based screening proved to be robust and showed high performance even on a heterogeneous multi-center data set. Integration into hospital workflow and its effect on aneurysm management would be an exciting topic of future research.
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Affiliation(s)
- Alena-K. Golla
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (A.-K.G.); (C.T.); (T.R.); (D.F.B.); (L.R.S.); (F.G.Z.)
| | - Christian Tönnes
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (A.-K.G.); (C.T.); (T.R.); (D.F.B.); (L.R.S.); (F.G.Z.)
| | - Tom Russ
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (A.-K.G.); (C.T.); (T.R.); (D.F.B.); (L.R.S.); (F.G.Z.)
| | - Dominik F. Bauer
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (A.-K.G.); (C.T.); (T.R.); (D.F.B.); (L.R.S.); (F.G.Z.)
| | - Matthias F. Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (M.F.F.); (S.J.D.); (S.O.S.)
| | - Steffen J. Diehl
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (M.F.F.); (S.J.D.); (S.O.S.)
| | - Stefan O. Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (M.F.F.); (S.J.D.); (S.O.S.)
| | - Michael Keese
- Department of Surgery, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany;
| | - Lothar R. Schad
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (A.-K.G.); (C.T.); (T.R.); (D.F.B.); (L.R.S.); (F.G.Z.)
| | - Frank G. Zöllner
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (A.-K.G.); (C.T.); (T.R.); (D.F.B.); (L.R.S.); (F.G.Z.)
| | - Johann S. Rink
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany; (M.F.F.); (S.J.D.); (S.O.S.)
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