1
|
Lima DL, Kasakewitch J, Nguyen DQ, Nogueira R, Cavazzola LT, Heniford BT, Malcher F. Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review. Hernia 2024; 28:1405-1412. [PMID: 38761300 DOI: 10.1007/s10029-024-03069-x] [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: 02/27/2024] [Accepted: 04/29/2024] [Indexed: 05/20/2024]
Abstract
INTRODUCTION This systematic review aims to evaluate the use of machine learning and artificial intelligence in hernia surgery. METHODS The PRISMA guidelines were followed throughout this systematic review. The ROBINS-I and Rob 2 tools were used to perform qualitative assessment of all studies included in this review. Recommendations were then summarized for the following pre-defined key items: protocol, research question, search strategy, study eligibility, data extraction, study design, risk of bias, publication bias, and statistical analysis. RESULTS A total of 13 articles were ultimately included for this review, describing the use of machine learning and deep learning for hernia surgery. All studies were published from 2020 to 2023. Articles varied regarding the population studied, type of machine learning or Deep Learning Model (DLM) used, and hernia type. Of the thirteen included studies, all included either inguinal, ventral, or incisional hernias. Four studies evaluated recognition of surgical steps during inguinal hernia repair videos. Two studies predicted outcomes using image-based DMLs. Seven studies developed and validated deep learning algorithms to predict outcomes and identify factors associated with postoperative complications. CONCLUSION The use of ML for abdominal wall reconstruction has been shown to be a promising tool for predicting outcomes and identifying factors that could lead to postoperative complications.
Collapse
Affiliation(s)
- D L Lima
- Department of Surgery, Montefiore Medical Center, New York, NY, USA.
| | - J Kasakewitch
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - D Q Nguyen
- Albert Einstein, College of Medicine, New York, USA
| | - R Nogueira
- Department of Surgery, Montefiore Medical Center, New York, NY, USA
| | - L T Cavazzola
- Federal University of Rio Grande Do Sul, Porto Alegre, Brazil
| | - B T Heniford
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - F Malcher
- Division of General Surgery, NYU Langone, New York, USA
| |
Collapse
|
2
|
Holland AM, Lorenz WR, Cavanagh JC, Smart NJ, Ayuso SA, Scarola GT, Kercher KW, Jorgensen LN, Janis JE, Fischer JP, Heniford BT. Comparison of Medical Research Abstracts Written by Surgical Trainees and Senior Surgeons or Generated by Large Language Models. JAMA Netw Open 2024; 7:e2425373. [PMID: 39093561 PMCID: PMC11297395 DOI: 10.1001/jamanetworkopen.2024.25373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 06/04/2024] [Indexed: 08/04/2024] Open
Abstract
Importance Artificial intelligence (AI) has permeated academia, especially OpenAI Chat Generative Pretrained Transformer (ChatGPT), a large language model. However, little has been reported on its use in medical research. Objective To assess a chatbot's capability to generate and grade medical research abstracts. Design, Setting, and Participants In this cross-sectional study, ChatGPT versions 3.5 and 4.0 (referred to as chatbot 1 and chatbot 2) were coached to generate 10 abstracts by providing background literature, prompts, analyzed data for each topic, and 10 previously presented, unassociated abstracts to serve as models. The study was conducted between August 2023 and February 2024 (including data analysis). Exposure Abstract versions utilizing the same topic and data were written by a surgical trainee or a senior physician or generated by chatbot 1 and chatbot 2 for comparison. The 10 training abstracts were written by 8 surgical residents or fellows, edited by the same senior surgeon, at a high-volume hospital in the Southeastern US with an emphasis on outcomes-based research. Abstract comparison was then based on 10 abstracts written by 5 surgical trainees within the first 6 months of their research year, edited by the same senior author. Main Outcomes and Measures The primary outcome measurements were the abstract grades using 10- and 20-point scales and ranks (first to fourth). Abstract versions by chatbot 1, chatbot 2, junior residents, and the senior author were compared and judged by blinded surgeon-reviewers as well as both chatbot models. Five academic attending surgeons from Denmark, the UK, and the US, with extensive experience in surgical organizations, research, and abstract evaluation served as reviewers. Results Surgeon-reviewers were unable to differentiate between abstract versions. Each reviewer ranked an AI-generated version first at least once. Abstracts demonstrated no difference in their median (IQR) 10-point scores (resident, 7.0 [6.0-8.0]; senior author, 7.0 [6.0-8.0]; chatbot 1, 7.0 [6.0-8.0]; chatbot 2, 7.0 [6.0-8.0]; P = .61), 20-point scores (resident, 14.0 [12.0-7.0]; senior author, 15.0 [13.0-17.0]; chatbot 1, 14.0 [12.0-16.0]; chatbot 2, 14.0 [13.0-16.0]; P = .50), or rank (resident, 3.0 [1.0-4.0]; senior author, 2.0 [1.0-4.0]; chatbot 1, 3.0 [2.0-4.0]; chatbot 2, 2.0 [1.0-3.0]; P = .14). The abstract grades given by chatbot 1 were comparable to the surgeon-reviewers' grades. However, chatbot 2 graded more favorably than the surgeon-reviewers and chatbot 1. Median (IQR) chatbot 2-reviewer grades were higher than surgeon-reviewer grades of all 4 abstract versions (resident, 14.0 [12.0-17.0] vs 16.9 [16.0-17.5]; P = .02; senior author, 15.0 [13.0-17.0] vs 17.0 [16.5-18.0]; P = .03; chatbot 1, 14.0 [12.0-16.0] vs 17.8 [17.5-18.5]; P = .002; chatbot 2, 14.0 [13.0-16.0] vs 16.8 [14.5-18.0]; P = .04). When comparing the grades of the 2 chatbots, chatbot 2 gave higher median (IQR) grades for abstracts than chatbot 1 (resident, 14.0 [13.0-15.0] vs 16.9 [16.0-17.5]; P = .003; senior author, 13.5 [13.0-15.5] vs 17.0 [16.5-18.0]; P = .004; chatbot 1, 14.5 [13.0-15.0] vs 17.8 [17.5-18.5]; P = .003; chatbot 2, 14.0 [13.0-15.0] vs 16.8 [14.5-18.0]; P = .01). Conclusions and Relevance In this cross-sectional study, trained chatbots generated convincing medical abstracts, undifferentiable from resident or senior author drafts. Chatbot 1 graded abstracts similarly to surgeon-reviewers, while chatbot 2 was less stringent. These findings may assist surgeon-scientists in successfully implementing AI in medical research.
Collapse
Affiliation(s)
- Alexis M. Holland
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina
| | - William R. Lorenz
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina
| | - Jack C. Cavanagh
- Department of Economics, Massachusetts Institute of Technology, Cambridge
| | - Neil J. Smart
- Division of Colorectal Surgery, Department of Surgery, Royal Devon & Exeter Hospital, Exeter, Devon, United Kingdom
| | - Sullivan A. Ayuso
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina
| | - Gregory T. Scarola
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina
| | - Kent W. Kercher
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina
| | - Lars N. Jorgensen
- Department of Clinical Medicine, University of Copenhagen, Bispedjerg & Frederiksberg Hospital, Copenhagen, Denmark
| | - Jeffrey E. Janis
- Division of Plastic and Reconstructive Surgery, The Ohio State University Wexner Medical Center, Columbus
| | - John P. Fischer
- Division of Plastic Surgery, University of Pennsylvania Health System, Philadelphia
| | - B. Todd Heniford
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina
| |
Collapse
|
3
|
Lorenz WR, Holland AM, Mead BS, Scarola GT, Augenstein VA, Heniford BT. Factors Associated With Respiratory Failure After Open Ventral Hernia Repair: An Evaluation of the NSQIP Database. Am Surg 2024; 90:1916-1918. [PMID: 38523427 DOI: 10.1177/00031348241241731] [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] [Indexed: 03/26/2024]
Abstract
An analysis of ACS-NSQIP open ventral hernia repair (OVHR) data (2017-2019) was performed. Respiratory failure (RF) occurred in 643 patients (1%) and not in 63,213 (99%) (nRF). Respiratory failure patients were older (63.7 vs 57 years, P < .001) and more comorbid: insulin-dependent diabetes (14.7% vs 5.8%, P < .001), COPD (19.4% vs 5.2%, P < .001), BMI (36.0 vs 32.8, P < .001), and current tobacco use (24.9% vs 17.6%, P < .001). Respiratory failure patients had greater ASA scores (ASA 3: 63.3% vs 47.8%, P < .001), bowel resection (8.2% vs 1.3%, P < .001), component separation (20.1% vs 9.0%, P < .001), operative times (178.4 vs 98.8 minutes, P < .001), complications (deep wound infections 3.6% vs 1.0%, organ space infections 13.2% vs 1.0%, wound dehiscence 3.1% vs 0.6%, acute renal failure 11.7% vs 0.1%), and hospital stay (13.7 vs 2.3 days), with fewer home discharges (44.3% vs 96.4%) (all P < .001). Respiratory failure patients had higher mortality compared to nRF (20.2% vs 0.1%, P < .001). Respiratory failure after OVHR is rare but correlates closely with significant wound, systemic, and social complications. Preoperative management of risk factors would be appropriate in high-risk patients.
Collapse
Affiliation(s)
- William R Lorenz
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - Alexis M Holland
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - Brittany S Mead
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - Gregory T Scarola
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - Vedra A Augenstein
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - B Todd Heniford
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| |
Collapse
|
4
|
Davis JMK, Niazi MKK, Ricker AB, Tavolara TE, Robinson JN, Annanurov B, Smith K, Mantha R, Hwang J, Shrestha R, Iannitti DA, Martinie JB, Baker EH, Gurcan MN, Vrochides D. Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging. J Surg Oncol 2024; 130:93-101. [PMID: 38712939 DOI: 10.1002/jso.27673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 04/13/2024] [Accepted: 04/28/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND AND OBJECTIVES Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on prechemotherapy cross-sectional imaging to predict patients' response to neoadjuvant chemotherapy. METHODS Adult patients with colorectal liver metastasis who underwent surgery after neoadjuvant chemotherapy were included. A DLM was trained on computed tomography images using attention-based multiple-instance learning. A logistic regression model incorporating clinical parameters of the Fong clinical risk score was used for comparison. Both model performances were benchmarked against the Response Evaluation Criteria in Solid Tumors criteria. A receiver operating curve was created and resulting area under the curve (AUC) was determined. RESULTS Ninety-five patients were included, with 33,619 images available for study inclusion. Ninety-five percent of patients underwent 5-fluorouracil-based chemotherapy with oxaliplatin and/or irinotecan. Sixty percent of the patients were categorized as chemotherapy responders (30% reduction in tumor diameter). The DLM had an AUC of 0.77. The AUC for the clinical model was 0.41. CONCLUSIONS Image-based DLM for prediction of response to neoadjuvant chemotherapy in patients with colorectal cancer liver metastases was superior to a clinical-based model. These results demonstrate potential to identify nonresponders to chemotherapy and guide select patients toward earlier curative resection.
Collapse
Affiliation(s)
- Joshua M K Davis
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Muhammad Khalid Khan Niazi
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Ansley B Ricker
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Thomas E Tavolara
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Jordan N Robinson
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Bayram Annanurov
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Kaylee Smith
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Rohit Mantha
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Jimmy Hwang
- Department of Medical Oncology, Atrium Health Carolinas Medical Center, Levine Cancer Institute, Charlotte, North Carolina, USA
| | - Ruchi Shrestha
- Department of Radiology, Atrium Health, Charlotte, North Carolina, USA
| | - David A Iannitti
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - John B Martinie
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Erin H Baker
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| | - Metin N Gurcan
- Center for Artificial Intelligence Research and the Clinical Image Analysis Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Dionisios Vrochides
- Department of Surgery, Atrium Health Carolinas Medical Center, Charlotte, North Carolina, USA
| |
Collapse
|
5
|
Wilson HH, Ma C, Ku D, Scarola GT, Augenstein VA, Colavita PD, Heniford BT. Deep learning model utilizing clinical data alone outperforms image-based model for hernia recurrence following abdominal wall reconstruction with long-term follow up. Surg Endosc 2024; 38:3984-3991. [PMID: 38862826 PMCID: PMC11219459 DOI: 10.1007/s00464-024-10980-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/02/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND Deep learning models (DLMs) using preoperative computed tomography (CT) imaging have shown promise in predicting outcomes following abdominal wall reconstruction (AWR), including component separation, wound complications, and pulmonary failure. This study aimed to apply these methods in predicting hernia recurrence and to evaluate if incorporating additional clinical data would improve the DLM's predictive ability. METHODS Patients were identified from a prospectively maintained single-institution database. Those who underwent AWR with available preoperative CTs were included, and those with < 18 months of follow up were excluded. Patients were separated into a training (80%) set and a testing (20%) set. A DLM was trained on the images only, and another DLM was trained on demographics only: age, sex, BMI, diabetes, and history of tobacco use. A mixed-value DLM incorporated data from both. The DLMs were evaluated by the area under the curve (AUC) in predicting recurrence. RESULTS The models evaluated data from 190 AWR patients with a 14.7% recurrence rate after an average follow up of more than 7 years (mean ± SD: 86 ± 39 months; median [Q1, Q3]: 85.4 [56.1, 113.1]). Patients had a mean age of 57.5 ± 12.3 years and were majority (65.8%) female with a BMI of 34.2 ± 7.9 kg/m2. There were 28.9% with diabetes and 16.8% with a history of tobacco use. The AUCs for the imaging DLM, clinical DLM, and combined DLM were 0.500, 0.667, and 0.604, respectively. CONCLUSIONS The clinical-only DLM outperformed both the image-only DLM and the mixed-value DLM in predicting recurrence. While all three models were poorly predictive of recurrence, the clinical-only DLM was the most predictive. These findings may indicate that imaging characteristics are not as useful for predicting recurrence as they have been for other AWR outcomes. Further research should focus on understanding the imaging characteristics that are identified by these DLMs and expanding the demographic information incorporated in the clinical-only DLM to further enhance the predictive ability of this model.
Collapse
Affiliation(s)
- Hadley H Wilson
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Chiyu Ma
- Department of Statistical Science, Duke University, Durham, NC, USA
| | - Dau Ku
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Gregory T Scarola
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Vedra A Augenstein
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Paul D Colavita
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - B Todd Heniford
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA.
| |
Collapse
|
6
|
Mayol J. Transforming Abdominal Wall Surgery With Generative Artificial Intelligence. JOURNAL OF ABDOMINAL WALL SURGERY : JAWS 2023; 2:12419. [PMID: 38312403 PMCID: PMC10831645 DOI: 10.3389/jaws.2023.12419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 11/16/2023] [Indexed: 02/06/2024]
Affiliation(s)
- Julio Mayol
- Hospital Clinico San Carlos, Instituto de Investigación Sanitaria San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| |
Collapse
|
7
|
Wilson HH, Ayuso SA, Rose M, Ku D, Scarola GT, Augenstein VA, Colavita PD, Heniford BT. Defining surgical risk in octogenarians undergoing paraesophageal hernia repair. Surg Endosc 2023; 37:8644-8654. [PMID: 37495845 DOI: 10.1007/s00464-023-10270-z] [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: 04/13/2023] [Accepted: 06/29/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND With an aging population, the utility of surgery in elderly patients, particularly octogenarians, is of increasing interest. The goal of this study was to analyze outcomes of octogenarians versus non-octogenarians undergoing paraesophageal hernia repair (PEHR). METHODS The Nationwide Readmission Database was queried for patients > 18 years old who underwent PEHR from 2016 to 2018. Exclusion criteria included a diagnosis of gastrointestinal malignancy or a concurrent bariatric procedure. Patients ≥ 80 were compared to those 18-79 years old using standard statistical methods, and subgroup analyses of elective and non-elective PEHRs were performed. RESULTS From 2016 to 2018, 46,450 patients were identified with 5425 (11.7%) octogenarians and 41,025 (88.3%) non-octogenarians. Octogenarians were more likely to have a non-elective operation (46.3% vs 18.2%, p < 0.001), and those undergoing non-elective PEHR had a higher mortality (5.5% vs 1.2%, p < 0.001). Outcomes were improved with elective PEHR, but octogenarians still had higher mortality (1.3% vs 0.2%, p < 0.001), longer LOS (3[2, 5] vs 2[1, 3] days, p < 0.001), and higher readmission rates within 30 days (11.1% vs 6.5%, p < 0.001) compared to non-octogenarian elective patients. Multivariable logistic regression showed that being an octogenarian was not independently predictive of mortality (odds ratio (OR) 1.373[95% confidence interval 0.962-1.959], p = 0.081), but a non-elective operation was (OR 3.180[2.492-4.057], p < 0.001). Being an octogenarian was a risk factor for readmission within 30 days (OR 1.512[1.348-1.697], p < 0.001). CONCLUSIONS Octogenarians represented a substantial proportion of patients undergoing PEHR and were more likely to undergo a non-elective operation. Being an octogenarian was not an independent predictor of perioperative mortality, but a non-elective operation was. Octogenarians' morbidity and mortality was reduced in elective procedures but was still higher than non-octogenarians. Elective PEHR in octogenarians is reasonable but should involve a thorough risk-benefit analysis.
Collapse
Affiliation(s)
- Hadley H Wilson
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Sullivan A Ayuso
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Mikayla Rose
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Dau Ku
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Gregory T Scarola
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Vedra A Augenstein
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - Paul D Colavita
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA
| | - B Todd Heniford
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, 1025 Morehead Medical Drive Suite 300, Charlotte, NC, 28204, USA.
| |
Collapse
|