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Petrella RJ. The AI Future of Emergency Medicine. Ann Emerg Med 2024; 84:139-153. [PMID: 38795081 DOI: 10.1016/j.annemergmed.2024.01.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 05/27/2024]
Abstract
In the coming years, artificial intelligence (AI) and machine learning will likely give rise to profound changes in the field of emergency medicine, and medicine more broadly. This article discusses these anticipated changes in terms of 3 overlapping yet distinct stages of AI development. It reviews some fundamental concepts in AI and explores their relation to clinical practice, with a focus on emergency medicine. In addition, it describes some of the applications of AI in disease diagnosis, prognosis, and treatment, as well as some of the practical issues that they raise, the barriers to their implementation, and some of the legal and regulatory challenges they create.
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Affiliation(s)
- Robert J Petrella
- Emergency Departments, CharterCARE Health Partners, Providence and North Providence, RI; Emergency Department, Boston VA Medical Center, Boston, MA; Emergency Departments, Steward Health Care System, Boston and Methuen, MA; Harvard Medical School, Boston, MA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA; Department of Medicine, Brigham and Women's Hospital, Boston, MA.
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Oikonomou EK, Khera R. Artificial intelligence-enhanced patient evaluation: bridging art and science. Eur Heart J 2024:ehae415. [PMID: 38976371 DOI: 10.1093/eurheartj/ehae415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 04/23/2024] [Accepted: 06/18/2024] [Indexed: 07/10/2024] Open
Abstract
The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, 06520-8017 CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, 06520-8017 CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, 195 Church St, 6th Floor, New Haven, CT 06510, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, 100 College Street, New Haven, 06511 CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06510 CT, USA
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Lee YM, Stretton B, Tan S, Gupta A, Kovoor J, Bacchi S, Lim W, Chan WO. Captive markets and medical artificial intelligence. J Med Imaging Radiat Oncol 2024; 68:278-281. [PMID: 38563301 DOI: 10.1111/1754-9485.13648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 03/21/2024] [Indexed: 04/04/2024]
Affiliation(s)
- Yong Min Lee
- Ophthalmology Department, Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Brandon Stretton
- Ophthalmology Department, Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Sheryn Tan
- Ophthalmology Department, Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Aashray Gupta
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Gold Coast University Hospital, Gold Coast, Queensland, Australia
| | - Joshua Kovoor
- Ophthalmology Department, Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Stephen Bacchi
- Ophthalmology Department, Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Flinders University, Adelaide, South Australia, Australia
| | - Wanyin Lim
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Department of Radiology, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Weng Onn Chan
- Ophthalmology Department, Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
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Chickering MJ, Frank E, Caplan AL. Standing on the Shoulders of Giant Artificial Intelligence Bots: Artificial Intelligence Can and Therefore Must Now Elevate Equity in Health Professional Education. AJPM FOCUS 2024; 3:100168. [PMID: 38162400 PMCID: PMC10756954 DOI: 10.1016/j.focus.2023.100168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Affiliation(s)
| | - Erica Frank
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Arthur L. Caplan
- Grossman School of Medicine, New York University, New York, New York
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Hu W, Yii FSL, Chen R, Zhang X, Shang X, Kiburg K, Woods E, Vingrys A, Zhang L, Zhu Z, He M. A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images. Transl Vis Sci Technol 2023; 12:14. [PMID: 37440249 PMCID: PMC10353749 DOI: 10.1167/tvst.12.7.14] [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: 03/24/2023] [Accepted: 06/08/2023] [Indexed: 07/14/2023] Open
Abstract
Purpose The purpose of this study was to perform a systematic review and meta-analysis to synthesize evidence from studies using deep learning (DL) to predict cardiovascular disease (CVD) risk from retinal images. Methods A systematic literature search was performed in MEDLINE, Scopus, and Web of Science up to June 2022. We extracted data pertaining to predicted outcomes, model development, and validation and model performance metrics. Included studies were graded using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. Model performance was pooled across eligible studies using a random-effects meta-analysis model. Results A total of 26 studies were included in the analysis. There were 42 CVD risk-related outcomes predicted from retinal images were identified, including 33 CVD risk factors, 4 cardiac imaging biomarkers, 2 CVD risk scores, the presence of CVD, and incident CVD. Three studies that aimed to predict the development of future CVD events reported an area under the receiver operating curve (AUROC) between 0.68 and 0.81. Models that used retinal images as input data had a pooled mean absolute error of 3.19 years (95% confidence interval [CI] = 2.95-3.43) for age prediction; a pooled AUROC of 0.96 (95% CI = 0.95-0.97) for gender classification; a pooled AUROC of 0.80 (95% CI = 0.73-0.86) for diabetes detection; and a pooled AUROC of 0.86 (95% CI = 0.81-0.92) for the detection of chronic kidney disease. We observed a high level of heterogeneity and variation in study designs. Conclusions Although DL models appear to have reasonably good performance when it comes to predicting CVD risk, further work is necessary to evaluate the real-world applicability and predictive accuracy. Translational Relevance DL-based CVD risk assessment from retinal images holds great promise to be translated to clinical practice as a novel approach for CVD risk assessment, given its simple, quick, and noninvasive nature.
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Affiliation(s)
- Wenyi Hu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Fabian S. L. Yii
- Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
- Curle Ophthalmology Laboratory, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK
| | - Ruiye Chen
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Xinyu Zhang
- Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xianwen Shang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Katerina Kiburg
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Ekaterina Woods
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Algis Vingrys
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, Australia
| | - Lei Zhang
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
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Taribagil P, Hogg HDJ, Balaskas K, Keane PA. Integrating artificial intelligence into an ophthalmologist’s workflow: obstacles and opportunities. EXPERT REVIEW OF OPHTHALMOLOGY 2023. [DOI: 10.1080/17469899.2023.2175672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Affiliation(s)
- Priyal Taribagil
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - HD Jeffry Hogg
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Department of Population Health Science, Population Health Science Institute, Newcastle University, Newcastle upon Tyne, UK
- Department of Ophthalmology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Freeman Road, Newcastle upon Tyne, UK
| | - Konstantinos Balaskas
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Medical Retina, Institute of Ophthalmology, University College of London Institute of Ophthalmology, London, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Medical Retina, Institute of Ophthalmology, University College of London Institute of Ophthalmology, London, UK
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Sarkis R, Burri O, Royer-Chardon C, Schyrr F, Blum S, Costanza M, Cherix S, Piazzon N, Barcena C, Bisig B, Nardi V, Sarro R, Ambrosini G, Weigert M, Spertini O, Blum S, Deplancke B, Seitz A, de Leval L, Naveiras O. MarrowQuant 2.0: A Digital Pathology Workflow Assisting Bone Marrow Evaluation in Experimental and Clinical Hematology. Mod Pathol 2023; 36:100088. [PMID: 36788087 DOI: 10.1016/j.modpat.2022.100088] [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: 07/15/2022] [Revised: 11/22/2022] [Accepted: 12/15/2022] [Indexed: 01/11/2023]
Abstract
Bone marrow (BM) cellularity assessment is a crucial step in the evaluation of BM trephine biopsies for hematologic and nonhematologic disorders. Clinical assessment is based on a semiquantitative visual estimation of the hematopoietic and adipocytic components by hematopathologists, which does not provide quantitative information on other stromal compartments. In this study, we developed and validated MarrowQuant 2.0, an efficient, user-friendly digital hematopathology workflow integrated within QuPath software, which serves as BM quantifier for 5 mutually exclusive compartments (bone, hematopoietic, adipocytic, and interstitial/microvasculature areas and other) and derives the cellularity of human BM trephine biopsies. Instance segmentation of individual adipocytes is realized through the adaptation of the machine-learning-based algorithm StarDist. We calculated BM compartments and adipocyte size distributions of hematoxylin and eosin images obtained from 250 bone specimens, from control subjects and patients with acute myeloid leukemia or myelodysplastic syndrome, at diagnosis and follow-up, and measured the agreement of cellularity estimates by MarrowQuant 2.0 against visual scores from 4 hematopathologists. The algorithm was capable of robust BM compartment segmentation with an average mask accuracy of 86%, maximal for bone (99%), hematopoietic (92%), and adipocyte (98%) areas. MarrowQuant 2.0 cellularity score and hematopathologist estimations were highly correlated (R2 = 0.92-0.98, intraclass correlation coefficient [ICC] = 0.98; interobserver ICC = 0.96). BM compartment segmentation quantitatively confirmed the reciprocity of the hematopoietic and adipocytic compartments. MarrowQuant 2.0 performance was additionally tested for cellularity assessment of specimens prospectively collected from clinical routine diagnosis. After special consideration for the choice of the cellularity equation in specimens with expanded stroma, performance was similar in this setting (R2 = 0.86, n = 42). Thus, we conclude that these validation experiments establish MarrowQuant 2.0 as a reliable tool for BM cellularity assessment. We expect this workflow will serve as a clinical research tool to explore novel biomarkers related to BM stromal components and may contribute to further validation of future digitalized diagnostic hematopathology workstreams.
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Affiliation(s)
- Rita Sarkis
- Laboratory of Regenerative Hematopoiesis, Institute of Bioengineering & ISREC, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Biomedical Sciences, University of Lausanne (UNIL), Lausanne, Switzerland; Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Olivier Burri
- BioImaging and Optics Core Facility, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Claire Royer-Chardon
- Institute of Pathology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Frédérica Schyrr
- Laboratory of Regenerative Hematopoiesis, Institute of Bioengineering & ISREC, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sophie Blum
- Laboratory of Regenerative Hematopoiesis, Institute of Bioengineering & ISREC, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mariangela Costanza
- Hematology Service, Departments of Oncology and Laboratory Medicine, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Stephane Cherix
- Department of Orthopaedics and Traumatology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nathalie Piazzon
- Institute of Pathology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Carmen Barcena
- Institute of Pathology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland; Department of Pathology, Hospital 12 de Octubre, Madrid, Spain
| | - Bettina Bisig
- Institute of Pathology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Valentina Nardi
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts
| | - Rossella Sarro
- Institute of Pathology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland; Institute of Pathology, Ente Ospedaliero Cantonale (EOC), Locarno, Switzerland
| | - Giovanna Ambrosini
- Bioinformatics Competence Center (BICC), UNIL/EPFL Lausanne, Switzerland
| | - Martin Weigert
- Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Olivier Spertini
- Hematology Service, Departments of Oncology and Laboratory Medicine, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Sabine Blum
- Hematology Service, Departments of Oncology and Laboratory Medicine, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Arne Seitz
- BioImaging and Optics Core Facility, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Laurence de Leval
- Institute of Pathology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Olaia Naveiras
- Laboratory of Regenerative Hematopoiesis, Institute of Bioengineering & ISREC, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Hematology Service, Departments of Oncology and Laboratory Medicine, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
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Jiang D, Chen ZX, Ma FX, Gong YY, Pu T, Chen JM, Liu XQ, Zhao YJ, Xie K, Hou H, Wang C, Geng XP, Liu FB. Online calculator for predicting the risk of malignancy in patients with pancreatic cystic neoplasms: A multicenter, retrospective study. World J Gastroenterol 2022; 28:5469-5482. [PMID: 36312834 PMCID: PMC9611704 DOI: 10.3748/wjg.v28.i37.5469] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/25/2022] [Accepted: 09/07/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Efficient and practical methods for predicting the risk of malignancy in patients with pancreatic cystic neoplasms (PCNs) are lacking.
AIM To establish a nomogram-based online calculator for predicting the risk of malignancy in patients with PCNs.
METHODS In this study, the clinicopathological data of target patients in three medical centers were analyzed. The independent sample t-test, Mann–Whitney U test or chi-squared test were used as appropriate for statistical analysis. After univariable and multivariable logistic regression analysis, five independent factors were screened and incorporated to develop a calculator for predicting the risk of malignancy. Finally, the concordance index (C-index), calibration, area under the curve, decision curve analysis and clinical impact curves were used to evaluate the performance of the calculator.
RESULTS Enhanced mural nodules [odds ratio (OR): 4.314; 95% confidence interval (CI): 1.618–11.503, P = 0.003], tumor diameter ≥ 40 mm (OR: 3.514; 95%CI: 1.138–10.849, P = 0.029), main pancreatic duct dilatation (OR: 3.267; 95%CI: 1.230–8.678, P = 0.018), preoperative neutrophil-to-lymphocyte ratio ≥ 2.288 (OR: 2.702; 95%CI: 1.008–7.244, P = 0.048], and preoperative serum CA19-9 concentration ≥ 34 U/mL (OR: 3.267; 95%CI: 1.274–13.007, P = 0.018) were independent risk factors for a high risk of malignancy in patients with PCNs. In the training cohort, the nomogram achieved a C-index of 0.824 for predicting the risk of malignancy. The predictive ability of the model was then validated in an external cohort (C-index: 0.893). Compared with the risk factors identified in the relevant guidelines, the current model showed better predictive performance and clinical utility.
CONCLUSION The calculator demonstrates optimal predictive performance for identifying the risk of malignancy, potentially yielding a personalized method for patient selection and decision-making in clinical practice.
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Affiliation(s)
- Dong Jiang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
| | - Zi-Xiang Chen
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
| | - Fu-Xiao Ma
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
| | - Yu-Yong Gong
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
| | - Tian Pu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
| | - Jiang-Ming Chen
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
| | - Xue-Qian Liu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
| | - Yi-Jun Zhao
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
| | - Kun Xie
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
| | - Hui Hou
- Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
| | - Cheng Wang
- Department of General Surgery, The First Affiliated Hospital of University of Science and Technology of China, Hefei 230000, Anhui Province, China
| | - Xiao-Ping Geng
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
| | - Fu-Bao Liu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
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