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Kulkarni C, Liu D, Fardeen T, Dickson ER, Jang H, Sinha SR, Gubatan J. Artificial intelligence and machine learning technologies in ulcerative colitis. Therap Adv Gastroenterol 2024; 17:17562848241272001. [PMID: 39247718 PMCID: PMC11378191 DOI: 10.1177/17562848241272001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 06/17/2024] [Indexed: 09/10/2024] Open
Abstract
Interest in artificial intelligence (AI) applications for ulcerative colitis (UC) has grown tremendously in recent years. In the past 5 years, there have been over 80 studies focused on machine learning (ML) tools to address a wide range of clinical problems in UC, including diagnosis, prognosis, identification of new UC biomarkers, monitoring of disease activity, and prediction of complications. AI classifiers such as random forest, support vector machines, neural networks, and logistic regression models have been used to model UC clinical outcomes using molecular (transcriptomic) and clinical (electronic health record and laboratory) datasets with relatively high performance (accuracy, sensitivity, and specificity). Application of ML algorithms such as computer vision, guided image filtering, and convolutional neural networks have also been utilized to analyze large and high-dimensional imaging datasets such as endoscopic, histologic, and radiological images for UC diagnosis and prediction of complications (post-surgical complications, colorectal cancer). Incorporation of these ML tools to guide and optimize UC clinical practice is promising but will require large, high-quality validation studies that overcome the risk of bias as well as consider cost-effectiveness compared to standard of care.
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Affiliation(s)
- Chiraag Kulkarni
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Derek Liu
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Touran Fardeen
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Eliza Rose Dickson
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Hyunsu Jang
- Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA, USA
| | - Sidhartha R Sinha
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, 300 Pasteur Drive, M211, Stanford, CA 94305, USA
| | - John Gubatan
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, 300 Pasteur Drive, M211, Stanford, CA 94305, USA
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Ryu HM, Islam SMS, Riaz B, Sayeed HM, Choi B, Sohn S. Immunomodulatory Effects of a Probiotic Mixture: Alleviating Colitis in a Mouse Model through Modulation of Cell Activation Markers and the Gut Microbiota. Int J Mol Sci 2024; 25:8571. [PMID: 39201260 PMCID: PMC11354276 DOI: 10.3390/ijms25168571] [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] [Received: 07/12/2024] [Revised: 08/02/2024] [Accepted: 08/02/2024] [Indexed: 09/02/2024] Open
Abstract
Ulcerative colitis (UC) is a persistent inflammatory intestinal disease that consistently affects the colon and rectum. Its exact cause remains unknown. UC causes a considerable challenge in healthcare, prompting research for novel therapeutic strategies. Although probiotics have gained popularity as possible candidates for managing UC, studies are still ongoing to identify the best probiotics or probiotic mixtures for clinical applications. This study aimed to determine the efficacy of a multi-strain probiotic mixture in mitigating intestinal inflammation in a colitis mouse model induced by dextran sulfate sodium. Specifically, a multi-strain probiotic mixture consisting of Tetragenococcus halophilus and Eubacterium rectale was used to study its impact on colitis symptoms. Anti-inflammatory effects were evaluated using ELISA and flow cytometry. The configuration of gut microbial communities was determined using 16S rRNA metagenomic analysis. According to this study, colitis mice treated with the probiotic mixture experienced reduced weight loss and significantly less colonic shortening compared to untreated mice. Additionally, the treated mice exhibited increased levels of forkhead box P3 (Foxp3) and interleukin 10, along with decreased expression of dendritic cell activation markers, such as CD40+, CD80+, and CD83+, in peripheral blood leukocytes and intraepithelial lymphocytes. Furthermore, there was a significant decrease in the frequencies of CD8+N.K1.1+ cells and CD11b+Ly6G+ cells. In terms of the gut microbiota, probiotic-mixture treatment of colitis mice significantly increased the abundance of the phyla Actinobacteria and Verrucomicrobia (p < 0.05). These results provide valuable insights into the therapeutic promise of multi-strain probiotics, shedding light on their potential to alleviate colitis symptoms. This research contributes to the ongoing exploration of effective probiotic interventions for managing inflammatory bowel disease.
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Affiliation(s)
- Hye-Myung Ryu
- Department of Microbiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea;
| | - S. M. Shamsul Islam
- Department of Biomedical Sciences, Ajou University School of Medicine, Suwon 16499, Republic of Korea; (S.M.S.I.); (B.R.); (H.M.S.)
| | - Bushra Riaz
- Department of Biomedical Sciences, Ajou University School of Medicine, Suwon 16499, Republic of Korea; (S.M.S.I.); (B.R.); (H.M.S.)
| | - Hasan M. Sayeed
- Department of Biomedical Sciences, Ajou University School of Medicine, Suwon 16499, Republic of Korea; (S.M.S.I.); (B.R.); (H.M.S.)
| | - Bunsoon Choi
- Institute of Medical Science, Ajou University School of Medicine, Suwon 16499, Republic of Korea;
| | - Seonghyang Sohn
- Department of Microbiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea;
- Department of Biomedical Sciences, Ajou University School of Medicine, Suwon 16499, Republic of Korea; (S.M.S.I.); (B.R.); (H.M.S.)
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Liu X, Reigle J, Prasath VBS, Dhaliwal J. Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review. Comput Biol Med 2024; 171:108093. [PMID: 38354499 DOI: 10.1016/j.compbiomed.2024.108093] [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] [Received: 09/04/2023] [Revised: 01/04/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND There has been an increase in the development of both machine learning (ML) and deep learning (DL) prediction models in Inflammatory Bowel Disease. We aim in this systematic review to assess the methodological quality and risk of bias of ML and DL IBD image-based prediction studies. METHODS We searched three databases, PubMed, Scopus and Embase, to identify ML and DL diagnostic or prognostic predictive models using imaging data in IBD, to Dec 31, 2022. We restricted our search to include studies that primarily used conventional imaging data, were undertaken in human participants, and published in English. Two reviewers independently reviewed the abstracts. The methodological quality of the studies was determined, and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST). RESULTS Forty studies were included, thirty-nine developed diagnostic models. Seven studies utilized ML approaches, six were retrospective and none used multicenter data for model development. Thirty-three studies utilized DL approaches, ten were prospective, and twelve multicenter studies. Overall, all studies demonstrated high risk of bias. ML studies were evaluated in 4 domains all rated as high risk of bias: participants (6/7), predictors (1/7), outcome (3/7), and analysis (7/7), and DL studies evaluated in 3 domains: participants (24/33), outcome (10/33), and analysis (18/33). The majority of image-based studies used colonoscopy images. CONCLUSION The risk of bias was high in AI IBD image-based prediction models, owing to insufficient sample size, unreported missingness and lack of an external validation cohort. Models with a high risk of bias are unlikely to be generalizable and suitable for clinical implementation.
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Affiliation(s)
- Xiaoxuan Liu
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - James Reigle
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - V B Surya Prasath
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - Jasbir Dhaliwal
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA.
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Pal P, Pooja K, Nabi Z, Gupta R, Tandan M, Rao GV, Reddy N. Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review. Indian J Gastroenterol 2024; 43:172-187. [PMID: 38418774 DOI: 10.1007/s12664-024-01531-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/08/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND AND OBJECTIVES In spite of rapid growth of artificial intelligence (AI) in digestive endoscopy in lesion detection and characterization, the role of AI in inflammatory bowel disease (IBD) endoscopy is not clearly defined. We aimed at systematically reviewing the role of AI in IBD endoscopy and identifying future research areas. METHODS We searched the PubMed and Embase database using keywords ("artificial intelligence" OR "machine learning" OR "computer-aided" OR "convolutional neural network") AND ("inflammatory bowel disease" OR "ulcerative colitis" OR "Crohn's") AND ("endoscopy" or "colonoscopy" or "capsule endoscopy" or "device assisted enteroscopy") between 1975 and September 2023 and identified 62 original articles for detailed review. Review articles, consensus guidelines, case reports/series, editorials, letter to the editor, non-peer-reviewed pre-prints and conference abstracts were excluded. The quality of the included studies was assessed using the MI-CLAIM checklist. RESULTS The accuracy of AI models (25 studies) to assess ulcerative colitis (UC) endoscopic activity ranged between 86.54% and 94.5%. AI-assisted capsule endoscopy reading (12 studies) substantially reduced analyzable images and reading time with excellent accuracy (90.5% to 99.9%). AI-assisted analysis of colonoscopic images can help differentiate IBD from non-IBD, UC from non-UC and UC from Crohn's disease (CD) (three studies) with 72.1%, 98.3% and > 90% accuracy, respectively. AI models based on non-invasive clinical and radiologic parameters could predict endoscopic activity (three studies). AI-assisted virtual chromoendoscopy (four studies) could predict histologic remission and long-term outcomes. Computer-assisted detection (CADe) of dysplasia (two studies) is feasible along with AI-based differentiation of high from low-grade IBD neoplasia (79% accuracy). AI is effective in linking electronic medical record data (two studies) with colonoscopic videos to facilitate widespread machine learning. CONCLUSION AI-assisted IBD endoscopy has the potential to impact clinical management by automated detection and characterization of endoscopic lesions. Large, multi-center, prospective studies and commercially available IBD-specific endoscopic AI algorithms are warranted.
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Affiliation(s)
- Partha Pal
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India.
| | - Kanapuram Pooja
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Zaheer Nabi
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Rajesh Gupta
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Manu Tandan
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Guduru Venkat Rao
- Surgical Gastroenterology, Asian Institute of Gastroenterology, Hyderabad 500 082, India
| | - Nageshwar Reddy
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
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Hartoonian S, Hosseini M, Yousefi I, Mahdian M, Ghazizadeh Ahsaie M. Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024:S2212-4403(23)01566-3. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another. RESULTS The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection. CONCLUSION Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.
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Affiliation(s)
- Serlie Hartoonian
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matine Hosseini
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iman Yousefi
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Datres M, Paolazzi E, Chierici M, Pozzi M, Colangelo A, Dorian Donzella M, Jurman G. Endoscopy-based IBD identification by a quantized deep learning pipeline. BioData Min 2023; 16:33. [PMID: 38001537 PMCID: PMC10675910 DOI: 10.1186/s13040-023-00350-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 11/18/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Discrimination between patients affected by inflammatory bowel diseases and healthy controls on the basis of endoscopic imaging is an challenging problem for machine learning models. Such task is used here as the testbed for a novel deep learning classification pipeline, powered by a set of solutions enhancing characterising elements such as reproducibility, interpretability, reduced computational workload, bias-free modeling and careful image preprocessing. RESULTS First, an automatic preprocessing procedure is devised, aimed to remove artifacts from clinical data, feeding then the resulting images to an aggregated per-patient model to mimic the clinicians decision process. The predictions are based on multiple snapshots obtained through resampling, reducing the risk of misleading outcomes by removing the low confidence predictions. Each patient's outcome is explained by returning the images the prediction is based upon, supporting clinicians in verifying diagnoses without the need for evaluating the full set of endoscopic images. As a major theoretical contribution, quantization is employed to reduce the complexity and the computational cost of the model, allowing its deployment on small power devices with an almost negligible 3% performance degradation. Such quantization procedure holds relevance not only in the context of per-patient models but also for assessing its feasibility in providing real-time support to clinicians even in low-resources environments. The pipeline is demonstrated on a private dataset of endoscopic images of 758 IBD patients and 601 healthy controls, achieving Matthews Correlation Coefficient 0.9 as top performance on test set. CONCLUSION We highlighted how a comprehensive pre-processing pipeline plays a crucial role in identifying and removing artifacts from data, solving one of the principal challenges encountered when working with clinical data. Furthermore, we constructively showed how it is possible to emulate clinicians decision process and how it offers significant advantages, particularly in terms of explainability and trust within the healthcare context. Last but not least, we proved that quantization can be a useful tool to reduce the time and resources consumption with an acceptable degradation of the model performs. The quantization study proposed in this work points up the potential development of real-time quantized algorithms as valuable tools to support clinicians during endoscopy procedures.
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Affiliation(s)
- Massimiliano Datres
- Fondazione Bruno Kessler, via Sommarive, 18, Trento, I-38123, Italy
- University of Trento, via Calepina, 14, Trento, I-38122, Italy
| | - Elisa Paolazzi
- Fondazione Bruno Kessler, via Sommarive, 18, Trento, I-38123, Italy
- University of Trento, via Calepina, 14, Trento, I-38122, Italy
| | - Marco Chierici
- Fondazione Bruno Kessler, via Sommarive, 18, Trento, I-38123, Italy
| | - Matteo Pozzi
- Fondazione Bruno Kessler, via Sommarive, 18, Trento, I-38123, Italy
- University of Trento, via Calepina, 14, Trento, I-38122, Italy
| | | | | | - Giuseppe Jurman
- Fondazione Bruno Kessler, via Sommarive, 18, Trento, I-38123, Italy.
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Zulqarnain F, Rhoads SF, Syed S. Machine and deep learning in inflammatory bowel disease. Curr Opin Gastroenterol 2023; 39:294-300. [PMID: 37144491 PMCID: PMC10256313 DOI: 10.1097/mog.0000000000000945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
PURPOSE OF REVIEW The Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption of biologic agents; however, the advent of artificial intelligence technologies like machine learning and deep learning presents another watershed moment in IBD treatment. Interest in these methods in IBD research has increased over the past 10 years, and they offer a promising path to better clinical outcomes for IBD patients. RECENT FINDINGS Developing new tools to evaluate IBD and inform clinical management is challenging because of the expansive volume of data and requisite manual interpretation of data. Recently, machine and deep learning models have been used to streamline diagnosis and evaluation of IBD by automating review of data from several diagnostic modalities with high accuracy. These methods decrease the amount of time that clinicians spend manually reviewing data to formulate an assessment. SUMMARY Interest in machine and deep learning is increasing in medicine, and these methods are poised to revolutionize the way that we treat IBD. Here, we highlight the recent advances in using these technologies to evaluate IBD and discuss the ways that they can be leveraged to improve clinical outcomes.
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Affiliation(s)
- Fatima Zulqarnain
- Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, Virginia, USA
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Guimarães P, Finkler H, Reichert MC, Zimmer V, Grünhage F, Krawczyk M, Lammert F, Keller A, Casper M. Artificial-intelligence-based decision support tools for the differential diagnosis of colitis. Eur J Clin Invest 2023; 53:e13960. [PMID: 36721878 DOI: 10.1111/eci.13960] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 12/19/2022] [Accepted: 01/21/2023] [Indexed: 02/02/2023]
Abstract
BACKGROUND Whereas Artificial Intelligence (AI) based tools have recently been introduced in the field of gastroenterology, application in inflammatory bowel disease (IBD) is in its infancies. We established AI-based algorithms to distinguish IBD from infectious and ischemic colitis using endoscopic images and clinical data. METHODS First, we trained and tested a Convolutional Neural Network (CNN) using 1796 real-world images from 494 patients, presenting with three diseases (IBD [n = 212], ischemic colitis [n = 157], and infectious colitis [n = 125]). Moreover, we evaluated a Gradient Boosted Decision Trees (GBDT) algorithm using five clinical parameters as well as a hybrid approach (CNN + GBDT). Patients and images were randomly split into two completely independent datasets. The proposed approaches were benchmarked against each other and three expert endoscopists on the test set. RESULTS For the image-based CNN, the GBDT algorithm and the hybrid approach global accuracies were .709, .792, and .766, respectively. Positive predictive values were .602, .702, and .657. Global areas under the receiver operating characteristics (ROC) and precision recall (PR) curves were .727/.585, .888/.823, and .838/.733, respectively. Global accuracy did not differ between CNN and endoscopists (.721), but the clinical parameter-based GBDT algorithm outperformed CNN and expert image classification. CONCLUSIONS Decision support systems exclusively based on endoscopic image analysis for the differential diagnosis of colitis, representing a complex clinical challenge, seem not yet to be ready for primetime and more diverse image datasets may be necessary to improve performance in future development. The clinical value of the proposed clinical parameters algorithm should be evaluated in prospective cohorts.
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Affiliation(s)
- Pedro Guimarães
- Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.,Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Helen Finkler
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany
| | | | - Vincent Zimmer
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany.,Department of Medicine, Knappschaft Hospital Saar, Püttlingen, Germany
| | - Frank Grünhage
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany
| | - Marcin Krawczyk
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany
| | - Frank Lammert
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany.,Chair for Health Sciences, Hannover Medical School (MHH), Hannover, Germany
| | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.,Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, California, USA
| | - Markus Casper
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany
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