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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 2024:10.1007/s10029-024-03069-x. [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] [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.
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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
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Juneja D. Artificial intelligence: Applications in critical care gastroenterology. Artif Intell Gastrointest Endosc 2024; 5:89138. [DOI: 10.37126/aige.v5.i1.89138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/07/2023] [Accepted: 12/26/2023] [Indexed: 02/20/2024] Open
Abstract
Gastrointestinal (GI) complications frequently necessitate intensive care unit (ICU) admission. Additionally, critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions. However, these patients form a vulnerable group, who are at risk for developing side effects and complications. Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients. Artificial intelligence (AI) is a rapidly evolving technology with several potential applications in healthcare settings. ICUs produce a large amount of data, which may be employed for creation of AI algorithms, and provide a lucrative opportunity for application of AI. However, the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.
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Affiliation(s)
- Deven Juneja
- Department of Critical Care Medicine, Max Super Speciality Hospital, New Delhi 110017, India
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Tsutsumi K, Goshtasbi K, Ahmed KH, Khosravi P, Tawk K, Haidar YM, Tjoa T, Armstrong WB, Abouzari M. Artificial neural network prediction of post-thyroidectomy outcome. Clin Otolaryngol 2023. [PMID: 37096572 DOI: 10.1111/coa.14066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 02/13/2023] [Accepted: 04/09/2023] [Indexed: 04/26/2023]
Abstract
OBJECTIVES The goal of this study was to develop a deep neural network (DNN) for predicting surgical/medical complications and unplanned reoperations following thyroidectomy. DESIGN, SETTING, AND PARTICIPANTS The 2005-2017 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried to extract patients who underwent thyroidectomy. A DNN consisting of 10 layers was developed with an 80:20 breakdown for training and testing. MAIN OUTCOME MEASURES Three primary outcomes of interest, including occurrence of surgical complications, medical complications, and unplanned reoperation were predicted. RESULTS Of the 21 550 patients who underwent thyroidectomy, medical complications, surgical complications and reoperation occurred in 1723 (8.0%), 943 (4.38%) and 2448 (11.36%) patients, respectively. The DNN performed with an area under the curve of receiver operating characteristics of .783 (medical complications), .709 (surgical complications) and .703 (reoperations). Accuracy, specificity and negative predictive values of the model for all outcome variables ranged 78.2%-97.2%, while sensitivity and positive predictive values ranged 11.6%-62.5%. Variables with high permutation importance included sex, inpatient versus outpatient and American Society of Anesthesiologists class. CONCLUSIONS We predicted surgical/medical complications and unplanned reoperation following thyroidectomy via development of a well-performing ML algorithm. We have also developed a web-based application available on mobile devices to demonstrate the predictive capacity of our models in real time.
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Affiliation(s)
- Kotaro Tsutsumi
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Khodayar Goshtasbi
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Khwaja H Ahmed
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Pooya Khosravi
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Karen Tawk
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Yarah M Haidar
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Tjoson Tjoa
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - William B Armstrong
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Mehdi Abouzari
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
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Taha A, Enodien B, Frey DM, Taha-Mehlitz S. The Development of Artificial Intelligence in Hernia Surgery: A Scoping Review. Front Surg 2022; 9:908014. [PMID: 35693313 PMCID: PMC9178189 DOI: 10.3389/fsurg.2022.908014] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/09/2022] [Indexed: 01/07/2023] Open
Abstract
Background Artificial intelligence simulates human intelligence in machines that have undergone programming to make them think like human beings and imitate their activities. Artificial intelligence has dominated the medical sector to perform various patient diagnosis activities and improve communication between professionals and patients. The main goal of this study is to perform a scoping review to evaluate the development of artificial intelligence in all forms of hernia surgery except the diaphragm and upside-down hernia. Methods The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR) to guide the structuring of the manuscript and fulfill all the requirements of every subheading. The sources used to gather data are the PubMed, Cochrane, and EMBASE databases, IEEE and Google and Google Scholar search engines. AMSTAR tool is the most appropriate for assessing the methodological quality of the included studies. Results The study exclusively included twenty articles, whereby seven focused on artificial intelligence in inguinal hernia surgery, six focused on abdominal hernia surgery, five on incisional hernia surgery, and two on AI in medical imaging and robotics in hernia surgery. Conclusion The outcomes of this study reveal a significant literature gap on artificial intelligence in hernia surgery. The results also indicate that studies focus on inguinal hernia surgery more than any other types of hernia surgery since the articles addressing the topic are more. The study implies that more research is necessary for the field to develop and enjoy the benefits associated with AI. Thus, this situation will allow the integration of AI in activities like medical imaging and surgeon training.
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Affiliation(s)
- Anas Taha
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
- Correspondence: Anas Taha
| | - Bassey Enodien
- Department of Surgery, GZO- Hospital, Wetzikon, Switzerland
| | - Daniel M. Frey
- Department of Surgery, GZO- Hospital, Wetzikon, Switzerland
| | - Stephanie Taha-Mehlitz
- Clarunis, Department of Visceral Surgery, University Center for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
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Hossain A, Chowdhury SI, Sarker S, Ahsan MS. Artificial neural network for the prediction model of glomerular filtration rate to estimate the normal or abnormal stages of kidney using gamma camera. Ann Nucl Med 2021; 35:1342-1352. [PMID: 34491539 DOI: 10.1007/s12149-021-01676-7] [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: 05/30/2021] [Accepted: 08/31/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Chronic kidney disease (CKD) is evaluated based on glomerular filtration rate (GFR) using a gamma camera in the nuclear medicine center or hospital in a routine procedure, but the gamma camera does not provide the accurate stages of the diseases. Therefore, this research aimed to find out the normal or abnormal stages of CKD based on the value of GFR using an artificial neural network (ANN). METHODS Two hundred fifty (Training 188, Testing 62) kidney patients who underwent the ultrasonography test to diagnose the renal test in our nuclear medical centre were scanned using gamma camera. The patients were injected with 99mTc-DTPA before the scanning procedure. After pushing the syringe into the patient's vein, the pre-syringe and post syringe radioactive counts were calculated using the gamma camera. The artificial neural network uses the softmax function with cross-entropy loss to diagnose CKD normal or abnormal labels depending on the value of GFR in the output layer. RESULTS The results showed that the accuracy of the proposed ANN model was 99.20% for K-fold cross-validation. The sensitivity and specificity were 99.10% and 99.20%, respectively. The Area under the curve (AUC) was 0.9994. CONCLUSION The proposed model using an artificial neural network can classify the normal or abnormal stages of CKD. After implementing the proposed model clinically, it may upgrade the gamma camera to diagnose the normal or abnormal stages of the CKD with an appropriate GFR value.
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Affiliation(s)
- Alamgir Hossain
- Department of Physics, University of Rajshahi, Rajshahi, 6205, Bangladesh. .,Kyushu University, Fukuoka, Japan.
| | - Shariful Islam Chowdhury
- Institute of Nuclear Medicine and Allied Sciences, Bangladesh Atomic Energy Commission, Rajshahi, 6000, Bangladesh
| | - Shupti Sarker
- Department of Physics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Mostofa Shamim Ahsan
- Institute of Nuclear Medicine and Allied Sciences, Bangladesh Atomic Energy Commission, Rajshahi, 6000, Bangladesh
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Gao J, Merchant AM. A Machine Learning Approach in Predicting Mortality Following Emergency General Surgery. Am Surg 2021; 87:1379-1385. [PMID: 34378431 DOI: 10.1177/00031348211038568] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
BACKGROUND There is a significant mortality burden associated with emergency general surgery (EGS) procedures. The objective of this study was to develop and validate the use of a machine learning approach to predict mortality following EGS. METHODS The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent EGS between 2012 and 2017. We developed a machine learning algorithm to predict mortality following EGS and compared its performance with existing risk-prediction models of American Society of Anesthesiologists (ASA) classification, American College of Surgeon Surgical Risk Calculator (ACS-SRC), and the modified frailty index (mFI) using the area under receiver operative curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS The machine learning algorithm had a very high performance for predicting mortality following EGS, and it had superior performance compared to the ASA classification, ACS-SRC, and the mFI, as measured by the AUC, sensitivity, specificity, PPV, and NPV. DISCUSSION Machine learning approaches may be a promising tool to predict outcomes for EGS, aiding clinicians in surgical decision-making and counseling of patients and family, improving clinical outcomes by identifying modifiable risk factors than can be optimized, and decreasing treatment costs through resource allocation.
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Affiliation(s)
- Jeff Gao
- Department of Surgery, 12286Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Aziz M Merchant
- Department of Surgery, 12286Rutgers New Jersey Medical School, Newark, NJ, USA
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