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Harris J, Ahluwalia V, Xu K, Romeo D, Fritz C, Rajasekaran K. The efficacy of the National Surgical Quality Improvement Program surgical risk calculator in head and neck surgery: A meta-analysis. Head Neck 2024; 46:1718-1726. [PMID: 38576311 DOI: 10.1002/hed.27765] [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: 12/18/2023] [Revised: 03/16/2024] [Accepted: 03/26/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND The National Surgical Quality Improvement Program surgical risk calculator (SRC) estimates the risk for postoperative complications. This meta-analysis assesses the efficacy of the SRC in the field of head and neck surgery. METHODS A systematic review identified studies comparing the SRC's predictions to observed outcomes following head and neck surgeries. Predictive accuracy was assessed using receiver operating characteristic curves (AUCs) and Brier scoring. RESULTS Nine studies totaling 1774 patients were included. The SRC underpredicted the risk of all outcomes (including any complication [observed (ob) = 35.9%, predicted (pr) = 21.8%] and serious complication [ob = 28.7%, pr = 17.0%]) except mortality (ob = 0.37%, pr = 1.55%). The observed length of stay was more than twice the predicted length (p < 0.02). Discrimination was acceptable for postoperative pneumonia (AUC = 0.778) and urinary tract infection (AUC = 0.782) only. Predictive accuracy was low for all outcomes (Brier scores ≥0.01) and comparable for patients with and without free-flap reconstructions. CONCLUSION The SRC is an ineffective instrument for predicting outcomes in head and neck surgery.
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Affiliation(s)
- Jacob Harris
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Vinayak Ahluwalia
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Katherine Xu
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Dominic Romeo
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christian Fritz
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Karthik Rajasekaran
- Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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2
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Wojtera B, Szewczyk M, Pieńkowski P, Golusiński W. Artificial intelligence in head and neck surgery: Potential applications and future perspectives. J Surg Oncol 2024; 129:1051-1055. [PMID: 38419212 DOI: 10.1002/jso.27616] [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: 02/01/2024] [Revised: 02/05/2024] [Accepted: 02/11/2024] [Indexed: 03/02/2024]
Abstract
Artificial intelligence (AI) has the potential to improve the surgical treatment of patients with head and neck cancer. AI algorithms can analyse a wide range of data, including images, voice, molecular expression and raw clinical data. In the field of oncology, there are numerous AI practical applications, including diagnostics and treatment. AI can also develop predictive models to assess prognosis, overall survival, the likelihood of occult metastases, risk of complications and hospital length of stay.
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Affiliation(s)
- Bartosz Wojtera
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan University of Medical Sciences, Poznań, Poland
| | - Mateusz Szewczyk
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan University of Medical Sciences, Poznań, Poland
| | - Piotr Pieńkowski
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan University of Medical Sciences, Poznań, Poland
| | - Wojciech Golusiński
- Department of Head and Neck Surgery, Greater Poland Cancer Centre, Poznan University of Medical Sciences, Poznań, Poland
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3
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Alter IL, Chan K, Lechien J, Rameau A. An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review. Eur Arch Otorhinolaryngol 2024; 281:2723-2731. [PMID: 38393353 DOI: 10.1007/s00405-024-08512-4] [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: 10/22/2023] [Accepted: 01/25/2024] [Indexed: 02/25/2024]
Abstract
PURPOSE Despite the robust expansion of research surrounding artificial intelligence (AI) and machine learning (ML) and their applications to medicine, these methodologies often remain opaque and inaccessible to many otolaryngologists. Especially, with the increasing ubiquity of large-language models (LLMs), such as ChatGPT and their potential implementation in clinical practice, clinicians may benefit from a baseline understanding of some aspects of AI. In this narrative review, we seek to clarify underlying concepts, illustrate applications to otolaryngology, and highlight future directions and limitations of these tools. METHODS Recent literature regarding AI principles and otolaryngologic applications of ML and LLMs was reviewed via search in PubMed and Google Scholar. RESULTS Significant recent strides have been made in otolaryngology research utilizing AI and ML, across all subspecialties, including neurotology, head and neck oncology, laryngology, rhinology, and sleep surgery. Potential applications suggested by recent publications include screening and diagnosis, predictive tools, clinical decision support, and clinical workflow improvement via LLMs. Ongoing concerns regarding AI in medicine include ethical concerns around bias and data sharing, as well as the "black box" problem and limitations in explainability. CONCLUSIONS Potential implementations of AI in otolaryngology are rapidly expanding. While implementation in clinical practice remains theoretical for most of these tools, their potential power to influence the practice of otolaryngology is substantial. LEVEL OF EVIDENCE: 4
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Affiliation(s)
- Isaac L Alter
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA
| | - Karly Chan
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA
| | - Jérome Lechien
- Department of Otorhinolaryngology, Head and Neck Surgery, Hôpital Foch, School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Paris, France
- Department of Human Anatomy and Experimental Oncology, Faculty of Medicine, UMONS Research Institute for Health and Sciences Technology, University of Mons (UMons), Mons, Belgium
| | - Anaïs Rameau
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA.
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4
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Devault-Tousignant C, Harvie M, Bissada E, Christopoulos A, Tabet P, Guertin L, Bahig H, Ayad T. The use of artificial intelligence in reconstructive surgery for head and neck cancer: a systematic review. Eur Arch Otorhinolaryngol 2024:10.1007/s00405-024-08663-4. [PMID: 38662215 DOI: 10.1007/s00405-024-08663-4] [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: 02/25/2024] [Accepted: 04/05/2024] [Indexed: 04/26/2024]
Abstract
OBJECTIVES The popularity of artificial intelligence (AI) in head and neck cancer (HNC) management is increasing, but postoperative complications remain prevalent and are the main factor that impact prognosis after surgery. Hence, recent studies aim to assess new AI models to evaluate their ability to predict free flap complications more effectively than traditional algorithms. This systematic review aims to summarize current evidence on the utilization of AI models to predict complications following reconstructive surgery for HNC. METHODS A combination of MeSH terms and keywords was used to cover the following three subjects: "HNC," "artificial intelligence," and "free flap or reconstructive surgery." The electronic literature search was performed in three relevant databases: Medline (Ovid), Embase (Ovid), and Cochrane. Quality appraisal of the included study was conducted using the TRIPOD Statement. RESULTS The review included a total of 5 manuscripts (n = 5) for a total of 7524 patients. Across studies, the highest area under the receiver operating characteristic (AUROC) value achieved was 0.824 by the Auto-WEKA model. However, only 20% of reported AUROCs exceeded 0.70. One study concluded that most AI models were comparable or inferior in performance to conventional logistic regression. The highest predictors of complications were flap type, smoking status, tumour location, and age. DISCUSSION Some models showed promising results. Predictors identified across studies were different than those found in existing literature, showing the added value of AI models. However, the algorithms showed inconsistent results, underlying the need for better-powered studies with larger databases before clinical implementation.
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Affiliation(s)
- Cyril Devault-Tousignant
- Faculty of Medicine, McGill University, 3605 de la Montagne Street, Montreal, QC, H3G 2M1, Canada.
| | - Myriam Harvie
- Faculty of Medicine, University of Montreal, Montreal, QC, Canada
| | - Eric Bissada
- Division of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
| | - Apostolos Christopoulos
- Division of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
| | - Paul Tabet
- Division of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
| | - Louis Guertin
- Division of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
| | - Houda Bahig
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
- Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
| | - Tareck Ayad
- Division of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
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5
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Namavarian A, Gabinet-Equihua A, Deng Y, Khalid S, Ziai H, Deutsch K, Huang J, Gilbert RW, Goldstein DP, Yao CMKL, Irish JC, Enepekides DJ, Higgins KM, Rudzicz F, Eskander A, Xu W, de Almeida JR. Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS-NSQIP. Laryngoscope 2024. [PMID: 38651539 DOI: 10.1002/lary.31443] [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: 01/27/2024] [Revised: 03/17/2024] [Accepted: 03/27/2024] [Indexed: 04/25/2024]
Abstract
OBJECTIVE Accurate prediction of hospital length of stay (LOS) following surgical management of oral cavity cancer (OCC) may be associated with improved patient counseling, hospital resource utilization and cost. The objective of this study was to compare the performance of statistical models, a machine learning (ML) model, and The American College of Surgeons National Surgical Quality Improvement Program's (ACS-NSQIP) calculator in predicting LOS following surgery for OCC. MATERIALS AND METHODS A retrospective multicenter database study was performed at two major academic head and neck cancer centers. Patients with OCC who underwent major free flap reconstructive surgery between January 2008 and June 2019 surgery were selected. Data were pooled and split into training and validation datasets. Statistical and ML models were developed, and performance was evaluated by comparing predicted and actual LOS using correlation coefficient values and percent accuracy. RESULTS Totally 837 patients were selected with mean patient age being 62.5 ± 11.7 [SD] years and 67% being male. The ML model demonstrated the best accuracy (validation correlation 0.48, 4-day accuracy 70%), compared with the statistical models: multivariate analysis (0.45, 67%) and least absolute shrinkage and selection operator (0.42, 70%). All were superior to the ACS-NSQIP calculator's performance (0.23, 59%). CONCLUSION We developed statistical and ML models that predicted LOS following major free flap reconstructive surgery for OCC. Our models demonstrated superior predictive performance to the ACS-NSQIP calculator. The ML model identified several novel predictors of LOS. These models must be validated in other institutions before being used in clinical practice. LEVEL OF EVIDENCE Level 3 Laryngoscope, 2024.
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Affiliation(s)
- Amirpouyan Namavarian
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
| | | | - Yangqing Deng
- Department of Biostatistics, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Shuja Khalid
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Hedyeh Ziai
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Konrado Deutsch
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Jingyue Huang
- Department of Biostatistics, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Ralph W Gilbert
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - David P Goldstein
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Christopher M K L Yao
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Jonathan C Irish
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Danny J Enepekides
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Kevin M Higgins
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Frank Rudzicz
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- International Centre for Surgical Safety, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Antoine Eskander
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Wei Xu
- Department of Biostatistics, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - John R de Almeida
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Sinai Health System, Toronto, Ontario, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada
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6
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Wang SY, Barrette LX, Ng JJ, Sangal NR, Cannady SB, Brody RM, Bur AM, Brant JA. Predicting reoperation and readmission for head and neck free flap patients using machine learning. Head Neck 2024. [PMID: 38357827 DOI: 10.1002/hed.27690] [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: 06/06/2023] [Revised: 01/17/2024] [Accepted: 02/05/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND To develop machine learning (ML) models predicting unplanned readmission and reoperation among patients undergoing free flap reconstruction for head and neck (HN) surgery. METHODS Data were extracted from the 2012-2019 NSQIP database. eXtreme Gradient Boosting (XGBoost) was used to develop ML models predicting 30-day readmission and reoperation based on demographic and perioperative factors. Models were validated using 2019 data and evaluated. RESULTS Four-hundred and sixty-six (10.7%) of 4333 included patients were readmitted within 30 days of initial surgery. The ML model demonstrated 82% accuracy, 63% sensitivity, 85% specificity, and AUC of 0.78. Nine-hundred and four (18.3%) of 4931 patients underwent reoperation within 30 days of index surgery. The ML model demonstrated 62% accuracy, 51% sensitivity, 64% specificity, and AUC of 0.58. CONCLUSION XGBoost was used to predict 30-day readmission and reoperation for HN free flap patients. Findings may be used to assist clinicians and patients in shared decision-making and improve data collection in future database iterations.
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Affiliation(s)
- Stephanie Y Wang
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Louis-Xavier Barrette
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jinggang J Ng
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Neel R Sangal
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Steven B Cannady
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robert M Brody
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Corporal Michael J. Crescenz VAMC, Philadelphia, Pennsylvania, USA
| | - Andrés M Bur
- Department of Otolaryngology - Head and Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Jason A Brant
- Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Corporal Michael J. Crescenz VAMC, Philadelphia, Pennsylvania, USA
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7
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Hidaka T, Miyamoto S, Furuse K, Oshima A, Matsuura K, Higashino T. Machine learning approach to predict tracheal necrosis after total pharyngolaryngectomy. Head Neck 2024; 46:408-416. [PMID: 38088269 DOI: 10.1002/hed.27598] [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: 07/31/2023] [Revised: 11/06/2023] [Accepted: 12/03/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND Tracheal necrosis is a potentially severe complication of total pharyngolarynjectomy (TPL), sometimes combined with total esophagectomy. The risk factors for tracheal necrosis after TPL without total esophagectomy remain unknown. METHODS We retrospectively reviewed data of 395 patients who underwent TPL without total esophagectomy. Relevant factors associated with tracheal necrosis were evaluated using random forest machine learning and traditional multivariable logistic regression models. RESULTS Tracheal necrosis occurred in 25 (6.3%) patients. Both the models identified almost the same factors relevant to tracheal necrosis. History of radiotherapy was the most important predicting and significant risk factor in both models. Paratracheal lymph node dissection and total thyroidectomy with TPL were also relevant. Random forest model was able to predict tracheal necrosis with an accuracy of 0.927. CONCLUSIONS Random forest is useful in predicting tracheal necrosis. Countermeasures should be considered when creating a tracheostoma, particularly in patients with identified risk factors.
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Affiliation(s)
- Takeaki Hidaka
- Department of Plastic and Reconstructive Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Shimpei Miyamoto
- Department of Plastic, Reconstructive and Aesthetic Surgery, Graduate School of Medicine, The University of Tokyo, Hongo, Japan
| | - Kiichi Furuse
- Department of Plastic and Reconstructive Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Azusa Oshima
- Department of Plastic and Reconstructive Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Kazuto Matsuura
- Department of Head and Neck Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Takuya Higashino
- Department of Plastic and Reconstructive Surgery, National Cancer Center Hospital East, Kashiwa, Japan
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8
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Velmahos CS, Paschalidis A, Paranjape CN. The Not-So-Distant Future or Just Hype? Utilizing Machine Learning to Predict 30-Day Post-Operative Complications in Laparoscopic Colectomy Patients. Am Surg 2023; 89:5648-5654. [PMID: 36992631 DOI: 10.1177/00031348231167397] [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: 03/31/2023]
Abstract
BACKGROUND Complex machine learning (ML) models have revolutionized predictions in clinical care. However, for laparoscopic colectomy (LC), prediction of morbidity by ML has not been adequately analyzed nor compared against traditional logistic regression (LR) models. METHODS All LC patients, between 2017 and 2019, in the National Surgical Quality Improvement Program (NSQIP) were identified. A composite outcome of 17 variables defined any post-operative morbidity. Seven of the most common complications were additionally analyzed. Three ML models (Random Forests, XGBoost, and L1-L2-RFE) were compared with LR. RESULTS Random Forests, XGBoost, and L1-L2-RFE predicted 30-day post-operative morbidity with average area under the curve (AUC): .709, .712, and .712, respectively. LR predicted morbidity with AUC = .712. Septic shock was predicted with AUC ≤ .9, by ML and LR. CONCLUSION There was negligible difference in the predictive ability of ML and LR in post-LC morbidity prediction. Possibly, the computational power of ML cannot be realized in limited datasets.
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9
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Hassan AM, Biaggi-Ondina A, Asaad M, Morris N, Liu J, Selber JC, Butler CE. Artificial Intelligence Modeling to Predict Periprosthetic Infection and Explantation following Implant-Based Reconstruction. Plast Reconstr Surg 2023; 152:929-938. [PMID: 36862958 DOI: 10.1097/prs.0000000000010345] [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: 03/04/2023]
Abstract
BACKGROUND Despite improvements in prosthesis design and surgical techniques, periprosthetic infection and explantation rates following implant-based reconstruction (IBR) remain relatively high. Artificial intelligence is an extremely powerful predictive tool that involves machine learning (ML) algorithms. We sought to develop, validate, and evaluate the use of ML algorithms to predict complications of IBR. METHODS A comprehensive review of patients who underwent IBR from January of 2018 to December of 2019 was conducted. Nine supervised ML algorithms were developed to predict periprosthetic infection and explantation. Patient data were randomly divided into training (80%) and testing (20%) sets. RESULTS The authors identified 481 patients (694 reconstructions) with a mean ± SD age of 50.0 ± 11.5 years, mean ± SD body mass index of 26.7 ± 4.8 kg/m 2 , and median follow-up time of 16.1 months (range, 11.9 to 3.2 months). Periprosthetic infection developed in 113 of the reconstructions (16.3%), and explantation was required with 82 (11.8%) of them. ML demonstrated good discriminatory performance in predicting periprosthetic infection and explantation (area under the receiver operating characteristic curve, 0.73 and 0.78, respectively), and identified nine and 12 significant predictors of periprosthetic infection and explantation, respectively. CONCLUSIONS ML algorithms trained using readily available perioperative clinical data accurately predict periprosthetic infection and explantation following IBR. The authors' findings support incorporating ML models into perioperative assessment of patients undergoing IBR to provide data-driven, patient-specific risk assessment to aid individualized patient counseling, shared decision-making, and presurgical optimization.
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Affiliation(s)
- Abbas M Hassan
- From the Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center
| | - Andrea Biaggi-Ondina
- From the Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center
| | - Malke Asaad
- From the Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center
| | - Natalie Morris
- From the Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center
| | - Jun Liu
- From the Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center
| | - Jesse C Selber
- From the Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center
| | - Charles E Butler
- From the Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center
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10
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Knoedler L, Knoedler S, Allam O, Remy K, Miragall M, Safi AF, Alfertshofer M, Pomahac B, Kauke-Navarro M. Application possibilities of artificial intelligence in facial vascularized composite allotransplantation-a narrative review. Front Surg 2023; 10:1266399. [PMID: 38026484 PMCID: PMC10646214 DOI: 10.3389/fsurg.2023.1266399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 09/26/2023] [Indexed: 12/01/2023] Open
Abstract
Facial vascularized composite allotransplantation (FVCA) is an emerging field of reconstructive surgery that represents a dogmatic shift in the surgical treatment of patients with severe facial disfigurements. While conventional reconstructive strategies were previously considered the goldstandard for patients with devastating facial trauma, FVCA has demonstrated promising short- and long-term outcomes. Yet, there remain several obstacles that complicate the integration of FVCA procedures into the standard workflow for facial trauma patients. Artificial intelligence (AI) has been shown to provide targeted and resource-effective solutions for persisting clinical challenges in various specialties. However, there is a paucity of studies elucidating the combination of FVCA and AI to overcome such hurdles. Here, we delineate the application possibilities of AI in the field of FVCA and discuss the use of AI technology for FVCA outcome simulation, diagnosis and prediction of rejection episodes, and malignancy screening. This line of research may serve as a fundament for future studies linking these two revolutionary biotechnologies.
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Affiliation(s)
- Leonard Knoedler
- Department of Plastic, Hand- and Reconstructive Surgery, University Hospital Regensburg, Regensburg, Germany
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, United States
| | - Samuel Knoedler
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, United States
| | - Omar Allam
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, United States
| | - Katya Remy
- Department of Oral and Maxillofacial Surgery, University Hospital Regensburg, Regensburg, Germany
| | - Maximilian Miragall
- Department of Oral and Maxillofacial Surgery, University Hospital Regensburg, Regensburg, Germany
| | - Ali-Farid Safi
- Craniologicum, Center for Cranio-Maxillo-Facial Surgery, Bern, Switzerland
- Faculty of Medicine, University of Bern, Bern, Switzerland
| | - Michael Alfertshofer
- Division of Hand, Plastic and Aesthetic Surgery, Ludwig-Maximilians University Munich, Munich, Germany
| | - Bohdan Pomahac
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, United States
| | - Martin Kauke-Navarro
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT, United States
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11
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Loperfido A, Celebrini A, Marzetti A, Bellocchi G. Current role of artificial intelligence in head and neck cancer surgery: a systematic review of literature. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:933-940. [PMID: 37970203 PMCID: PMC10645467 DOI: 10.37349/etat.2023.00174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 07/19/2023] [Indexed: 11/17/2023] Open
Abstract
Aim Artificial intelligence (AI) is a new field of science in which computers will provide decisions-supporting tools to help doctors make difficult clinical choices. Recent AI applications in otolaryngology include head and neck oncology, rhinology, neurotology, and laryngology. The aim of this systematic review is to describe the potential uses of AI in head and neck oncology with a special focus on the surgical field. Methods The authors performed a systematic review, in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, in the main medical databases, including PubMed, Scopus, and Cochrane Library, considering all original studies published until February 2023 about the role of AI in head and neck cancer surgery. The search strategy included a combination of the following terms: "artificial intelligence" or "machine learning" and "head and neck cancer". Results Overall, 303 papers were identified and after duplicate removal (12 papers) and excluding papers not written in English (1 paper) and off-topic (4 papers), papers were assessed for eligibility; finally, only 12 papers were included. Three main fields of clinical interest were identified: the most widely investigated included the role of AI in surgical margins assessment (7 papers); the second most frequently evaluated topic was complications assessment (4 papers); finally, only one paper dealt with the indication of salvage laryngectomy after primary radiotherapy. Conclusions The authors report the first systematic review in the literature concerning the role of AI in head and neck cancer surgery. An increasing influx of AI applications to clinical problems in otolaryngology is expected, so specialists should be increasingly prepared to manage the constant changes. It will always remain critical for clinicians to use their skills and knowledge to critically evaluate the additional information provided by AI and make the final decisions on each patient.
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Affiliation(s)
| | | | - Andrea Marzetti
- Department of Otolaryngology Head and Neck Surgery, Fabrizio Spaziani Hospital, 03100 Frosinone, Italy
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12
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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Choi E, Leonard KW, Jassal JS, Levin AM, Ramachandra V, Jones LR. Artificial Intelligence in Facial Plastic Surgery: A Review of Current Applications, Future Applications, and Ethical Considerations. Facial Plast Surg 2023; 39:454-459. [PMID: 37353051 DOI: 10.1055/s-0043-1770160] [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: 06/25/2023] Open
Abstract
From virtual chat assistants to self-driving cars, artificial intelligence (AI) is often heralded as the technology that has and will continue to transform this generation. Among widely adopted applications in other industries, its potential use in medicine is being increasingly explored, where the vast amounts of data present in electronic health records and need for continuous improvements in patient care and workflow efficiency present many opportunities for AI implementation. Indeed, AI has already demonstrated capabilities for assisting in tasks such as documentation, image classification, and surgical outcome prediction. More specifically, this technology can be harnessed in facial plastic surgery, where the unique characteristics of the field lends itself well to specific applications. AI is not without its limitations, however, and the further adoption of AI in medicine and facial plastic surgery must necessarily be accompanied by discussion on the ethical implications and proper usage of AI in healthcare. In this article, we review current and potential uses of AI in facial plastic surgery, as well as its ethical ramifications.
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Affiliation(s)
- Elizabeth Choi
- Wayne State University School of Medicine, Detroit, Michigan
| | - Kyle W Leonard
- Department of Otolaryngology, Henry Ford Hospital, Detroit, Michigan
| | - Japnam S Jassal
- Department of Otolaryngology, Henry Ford Hospital, Detroit, Michigan
| | - Albert M Levin
- Department of Public Health Science, Henry Ford Health, Detroit, Michigan
- Center for Bioinformatics, Henry Ford Health, Detroit, Michigan
| | - Vikas Ramachandra
- Department of Public Health Science, Henry Ford Health, Detroit, Michigan
- Center for Bioinformatics, Henry Ford Health, Detroit, Michigan
| | - Lamont R Jones
- Department of Otolaryngology, Henry Ford Hospital, Detroit, Michigan
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14
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Hassan AM, Biaggi AP, Asaad M, Andejani DF, Liu J, Offodile Nd AC, Selber JC, Butler CE. Development and Assessment of Machine Learning Models for Individualized Risk Assessment of Mastectomy Skin Flap Necrosis. Ann Surg 2023; 278:e123-e130. [PMID: 35129476 DOI: 10.1097/sla.0000000000005386] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To develop, validate, and evaluate ML algorithms for predicting MSFN. BACKGROUND MSFN is a devastating complication that causes significant distress to patients and physicians by prolonging recovery time, compromising surgical outcomes, and delaying adjuvant therapy. METHODS We conducted comprehensive review of all consecutive patients who underwent mastectomy and immediate implant-based reconstruction from January 2018 to December 2019. Nine supervised ML algorithms were developed to predict MSFN. Patient data were partitioned into training (80%) and testing (20%) sets. RESULTS We identified 694 mastectomies with immediate implant-based reconstruction in 481 patients. The patients had a mean age of 50 ± 11.5 years, years, a mean body mass index of 26.7 ± 4.8 kg/m 2 , and a median follow-up time of 16.1 (range, 11.9-23.2) months. MSFN developed in 6% (n = 40) of patients. The random forest model demonstrated the best discriminatory performance (area under curve, 0.70), achieved a mean accuracy of 89% (95% confidence interval, 83-94), and identified 10 predictors of MSFN. Decision curve analysis demonstrated that ML models have a superior net benefit regardless of the probability threshold. Higher body mass index, older age, hypertension, subpectoral device placement, nipple-sparing mastectomy, axillary nodal dissection, and no acellular dermal matrix use were all independently associated with a higher risk of MSFN. CONCLUSIONS ML algorithms trained on readily available perioperative clinical data can accurately predict the occurrence of MSFN and aid in individualized patient counseling, preoperative optimization, and surgical planning to reduce the risk of this devastating complication.
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Affiliation(s)
- Abbas M Hassan
- Department of Plastic & Reconstructive Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX
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15
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Lee ZH, Ismail T, Shuck JW, Chang EI. Innovative Strategies in Microvascular Head and Neck Reconstruction. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1194. [PMID: 37512006 PMCID: PMC10384542 DOI: 10.3390/medicina59071194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/13/2023] [Accepted: 06/13/2023] [Indexed: 07/30/2023]
Abstract
The field of reconstructive microsurgery has witnessed considerable advancements over the years, driven by improvements in technology, imaging, surgical instruments, increased understanding of perforator anatomy, and experience with microsurgery. However, within the subset of microvascular head and neck reconstruction, novel strategies are needed to improve and optimize both patient aesthetics and post-operative function. Given the disfiguring defects that are encountered following trauma or oncologic resections, the reconstructive microsurgeon must always aim to innovate new approaches, reject historic premises, and challenge established paradigms to further achieve improvement in both aesthetic and functional outcomes. The authors aim to provide an up-to-date review of innovations in head and neck reconstruction for oncologic defects.
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Affiliation(s)
- Z-Hye Lee
- Department of Plastic Surgery, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Tarek Ismail
- Department of Plastic, Reconstructive, Aesthetic and Hand Surgery, University Hospital of Basel, 4031 Basel, Switzerland
| | - John W Shuck
- Department of Plastic Surgery, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Edward I Chang
- Department of Plastic Surgery, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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16
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Asaad M, Lu SC, Hassan AM, Kambhampati P, Mitchell D, Chang EI, Yu P, Hanasono MM, Sidey-Gibbons C. The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction. Ann Surg Oncol 2023; 30:2343-2352. [PMID: 36719569 DOI: 10.1245/s10434-022-13053-3] [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: 04/29/2022] [Accepted: 12/22/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND Machine learning has been increasingly used for surgical outcome prediction, yet applications in head and neck reconstruction are not well-described. In this study, we developed and evaluated the performance of ML algorithms in predicting postoperative complications in head and neck free-flap reconstruction. METHODS We conducted a comprehensive review of patients who underwent microvascular head and neck reconstruction between January 2005 and December 2018. Data were used to develop and evaluate nine supervised ML algorithms in predicting overall complications, major recipient-site complication, and total flap loss. RESULTS We identified 4000 patients who met inclusion criteria. Overall, 33.7% of patients experienced a complication, 26.5% experienced a major recipient-site complication, and 1.7% suffered total flap loss. The k-nearest neighbors algorithm demonstrated the best overall performance for predicting any complication (AUROC = 0.61, sensitivity = 0.60). Regularized regression had the best performance for predicting major recipient-site complications (AUROC = 0.68, sensitivity = 0.66), and decision trees were the best predictors of total flap loss (AUROC = 0.66, sensitivity = 0.50). CONCLUSIONS ML accurately identified patients at risk of experiencing postsurgical complications, including total flap loss. Predictions from ML models may provide insight in the perioperative setting and facilitate shared decision making.
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Affiliation(s)
- Malke Asaad
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sheng-Chieh Lu
- Department of Symptom Research, MD Anderson Center for INSPiRED Cancer Care, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Abbas M Hassan
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Praneeth Kambhampati
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Baylor College of Medicine, Houston, TX, USA
| | - David Mitchell
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- McGovern Medical School, Houston, TX, USA.
| | - Edward I Chang
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peirong Yu
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Matthew M Hanasono
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - C Sidey-Gibbons
- Department of Symptom Research, MD Anderson Center for INSPiRED Cancer Care, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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17
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Lu SC, Knafl M, Turin A, Offodile AC, Ravi V, Sidey-Gibbons C. Machine Learning Models Using Routinely Collected Clinical Data Offer Robust and Interpretable Predictions of 90-Day Unplanned Acute Care Use for Cancer Immunotherapy Patients. JCO Clin Cancer Inform 2023; 7:e2200123. [PMID: 37001039 PMCID: PMC10281452 DOI: 10.1200/cci.22.00123] [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: 08/17/2022] [Revised: 11/23/2022] [Accepted: 01/20/2023] [Indexed: 04/03/2023] Open
Abstract
PURPOSE Clinical management of patients receiving immune checkpoint inhibitors (ICIs) could be informed using accurate predictive tools to identify patients at risk of short-term acute care utilization (ACU). We used routinely collected data to develop and assess machine learning (ML) algorithms to predict unplanned ACU within 90 days of ICI treatment initiation. METHODS We used aggregated electronic health record data from 7,960 patients receiving ICI treatments to train and assess eight ML algorithms. We developed the models using pre-SARS-COV-19 COVID-19 data generated between January 2016 and February 2020. We validated our algorithms using data collected between March 2020 and June 2022 (peri-COVID-19 sample). We assessed performance using area under the receiver operating characteristic curves (AUROC), sensitivity, specificity, and calibration plots. We derived intuitive explanations of predictions using variable importance and Shapley additive explanation analyses. We assessed the marginal performance of ML models compared with that of univariate and multivariate logistic regression (LR) models. RESULTS Most algorithms significantly outperformed the univariate and multivariate LR models. The extreme gradient boosting trees (XGBT) algorithm demonstrated the best overall performance (AUROC, 0.70; sensitivity, 0.53; specificity, 0.74) on the peri-COVID-19 sample. The algorithm performance was stable across both pre- and peri-COVID-19 samples, as well as ICI regimen and cancer groups. Type of ICI agents, oxygen saturation, diastolic blood pressure, albumin level, platelet count, immature granulocytes, absolute monocyte, chloride level, red cell distribution width, and alcohol intake were the top 10 key predictors used by the XGBT algorithm. CONCLUSION Machine learning algorithms trained using routinely collected data outperformed traditional statistical models when predicting 90-day ACU. The XGBT algorithm has the potential to identify high-ACU risk patients and enable preventive interventions to avoid ACU.
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Affiliation(s)
- Sheng-Chieh Lu
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Mark Knafl
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | | | - Vinod Ravi
- The University of Texas MD Anderson Cancer Center, Houston, TX
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Irgang L, Barth H, Holmén M. Data-Driven Technologies as Enablers for Value Creation in the Prevention of Surgical Site Infections: a Systematic Review. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 7:1-41. [PMID: 36910913 PMCID: PMC9995622 DOI: 10.1007/s41666-023-00129-2] [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: 04/05/2022] [Revised: 01/16/2023] [Accepted: 02/03/2023] [Indexed: 03/02/2023]
Abstract
Despite the advances in modern medicine, the use of data-driven technologies (DDTs) to prevent surgical site infections (SSIs) remains a major challenge. Scholars recognise that data management is the next frontier in infection prevention, but many aspects related to the benefits and advantages of using DDTs to mitigate SSI risk factors remain unclear and underexplored in the literature. This study explores how DDTs enable value creation in the prevention of SSIs. This study follows a systematic literature review approach and the PRISMA statement to analyse peer-reviewed articles from seven databases. Fifty-nine articles were included in the review and were analysed through a descriptive and a thematic analysis. The findings suggest a growing interest in DDTs in SSI prevention in the last 5 years, and that machine learning and smartphone applications are widely used in SSI prevention. DDTs are mainly applied to prevent SSIs in clean and clean-contaminated surgeries and often used to manage patient-related data in the postoperative stage. DDTs enable the creation of nine categories of value that are classified in four dimensions: cost/sacrifice, functional/instrumental, experiential/hedonic, and symbolic/expressive. This study offers a unique and systematic overview of the value creation aspects enabled by DDT applications in SSI prevention and suggests that additional research is needed in four areas: value co-creation and product-service systems, DDTs in contaminated and dirty surgeries, data legitimation and explainability, and data-driven interventions. Supplementary Information The online version contains supplementary material available at 10.1007/s41666-023-00129-2.
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Affiliation(s)
- Luís Irgang
- School of Business, Innovation and Sustainability - Department of Engineering and Innovation, Halmstad University, Halmstad, Sweden
| | - Henrik Barth
- School of Business, Innovation and Sustainability - Department of Engineering and Innovation, Halmstad University, Halmstad, Sweden
| | - Magnus Holmén
- School of Business, Innovation and Sustainability - Department of Engineering and Innovation, Halmstad University, Halmstad, Sweden
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Miller LE, Goedicke W, Crowson MG, Rathi VK, Naunheim MR, Agarwala AV. Using Machine Learning to Predict Operating Room Case Duration: A Case Study in Otolaryngology. Otolaryngol Head Neck Surg 2023; 168:241-247. [PMID: 35133897 DOI: 10.1177/01945998221076480] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/07/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Optimizing operating room (OR) efficiency depends on accurate case duration estimates. Machine learning (ML) methods have been used to predict OR case durations in other subspecialties. We hypothesize that ML methods improve projected case lengths over existing non-ML techniques for otolaryngology-head and neck surgery cases. METHODS Deidentified patient information from otolaryngology surgical cases at 1 academic institution were reviewed from 2016 to 2020. Variables collected included patient, surgeon, procedure, and facility data known preoperatively so as to capture all realistic contributors. Available case data were divided into a training and testing data set. Several ML algorithms were evaluated based on best performance of predicted case duration when compared to actual case duration. Performance of all models was compared by the average root mean squared error and mean absolute error (MAE). RESULTS In total, 50,888 otolaryngology surgical cases were evaluated with an average case duration of 98.3 ± 86.9 minutes. Most cases were general otolaryngology (n = 16,620). Case features closely associated with OR duration included procedure performed, surgeon, subspecialty of case, and postoperative destination of the patient. The best-performing ML models were CatBoost and XGBoost, which reduced operative time MAE by 9.6 minutes and 8.5 minutes compared to current methods, respectively. DISCUSSION The incorporation of other easily identifiable features beyond procedure performed and surgeon meaningfully improved our operative duration prediction accuracy. CatBoost provided the best-performing ML model. IMPLICATIONS FOR PRACTICE ML algorithms to predict OR case time duration in otolaryngology can improve case duration accuracy and result in financial benefit.
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Affiliation(s)
- Lauren E Miller
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - William Goedicke
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Matthew G Crowson
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Vinay K Rathi
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Matthew R Naunheim
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Aalok V Agarwala
- Department of Anesthesia, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
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Hassan AM, Rajesh A, Asaad M, Jonas NA, Coert JH, Mehrara BJ, Butler CE. A Surgeon's Guide to Artificial Intelligence-Driven Predictive Models. Am Surg 2023; 89:11-19. [PMID: 35588764 PMCID: PMC9674797 DOI: 10.1177/00031348221103648] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Artificial intelligence (AI) focuses on processing and interpreting complex information as well as identifying relationships and patterns among complex data. Artificial intelligence- and machine learning (ML)-driven predictions have shown promising potential in influencing real-time decisions and improving surgical outcomes by facilitating screening, diagnosis, risk assessment, preoperative planning, and shared decision-making. Fundamental understanding of the algorithms, as well as their development and interpretation, is essential for the evolution of AI in surgery. In this article, we provide surgeons with a fundamental understanding of AI-driven predictive models through an overview of common ML and deep learning algorithms, model development, performance metrics and interpretation. This would serve as a basis for understanding ML-based research, while fostering new ideas and innovations for furthering the reach of this emerging discipline.
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Affiliation(s)
- Abbas M. Hassan
- Department of Plastic & Reconstructive Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Aashish Rajesh
- Department of Surgery, University of Texas Health Science Center, San Antonio, TX, USA
| | - Malke Asaad
- Department of Plastic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Nelson A. Jonas
- Department of Plastic & Reconstructive Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - J. Henk Coert
- Department of Plastic and Reconstructive Surgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Babak J. Mehrara
- Department of Plastic & Reconstructive Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Charles E. Butler
- Department of Plastic & Reconstructive Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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21
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Nguyen MP, Morshed RA, Dalle Ore CL, Cummins DD, Saggi S, Chen WC, Choudhury A, Ravi A, Raleigh DR, Magill ST, McDermott MW, Theodosopoulos PV. Supervised machine learning algorithms demonstrate proliferation index correlates with long-term recurrence after complete resection of WHO grade I meningioma. J Neurosurg 2023; 138:86-94. [PMID: 36303473 DOI: 10.3171/2022.4.jns212516] [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: 10/31/2021] [Accepted: 04/25/2022] [Indexed: 01/04/2023]
Abstract
OBJECTIVE Meningiomas are the most common primary intracranial tumor, and resection is a mainstay of treatment. It is unclear what duration of imaging follow-up is reasonable for WHO grade I meningiomas undergoing complete resection. This study examined recurrence rates, timing of recurrence, and risk factors for recurrence in patients undergoing a complete resection (as defined by both postoperative MRI and intraoperative impression) of WHO grade I meningiomas. METHODS The authors conducted a retrospective, single-center study examining recurrence risk for adult patients with a single intracranial meningioma that underwent complete resection. Uni- and multivariate nominal logistic regression and Cox proportional hazards analyses were performed to identify variables associated with recurrence and time to recurrence. Two supervised machine learning algorithms were then implemented to confirm factors within the cohort that were associated with recurrence. RESULTS The cohort consisted of 823 patients who met inclusion criteria, and 56 patients (6.8%) had recurrence on imaging follow-up. The median age of the cohort was 56 years, and 77.4% of patients were female. The median duration of head imaging follow-up for the entire cohort was 2.7 years, but for the subgroup of patients who had a recurrence, the median follow-up was 10.1 years. Estimated 1-, 5-, 10-, and 15-year recurrence-free survival rates were 99.8% (95% confidence interval [CI] 98.8%-99.9%), 91.0% (95% CI 87.7%-93.6%), 83.6% (95% CI 78.6%-87.6%), and 77.3% (95% CI 69.7%-83.4%), respectively, for the entire cohort. On multivariate analysis, MIB-1 index (odds ratio [OR] per 1% increase: 1.34, 95% CI 1.13-1.58, p = 0.0003) and follow-up duration (OR per year: 1.12, 95% CI 1.03-1.21, p = 0.012) were both associated with recurrence. Gradient-boosted decision tree and random forest analyses both identified MIB-1 index as the main factor associated with recurrence, aside from length of imaging follow-up. For tumors with an MIB-1 index < 8, recurrences were documented up to 8 years after surgery. For tumors with an MIB-1 index ≥ 8, recurrences were documented up to 12 years following surgery. CONCLUSIONS Long-term imaging follow-up is important even after a complete resection of a meningioma. Higher MIB-1 labeling index is associated with greater risk of recurrence. Imaging screening for at least 8 years in patients with an MIB-1 index < 8 and at least 12 years for those with an MIB-1 index ≥ 8 may be needed to detect long-term recurrences.
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Affiliation(s)
- Minh P Nguyen
- 1Department of Neurological Surgery, University of California, San Francisco.,2School of Medicine, University of California, San Francisco
| | - Ramin A Morshed
- 1Department of Neurological Surgery, University of California, San Francisco
| | - Cecilia L Dalle Ore
- 1Department of Neurological Surgery, University of California, San Francisco
| | - Daniel D Cummins
- 1Department of Neurological Surgery, University of California, San Francisco.,2School of Medicine, University of California, San Francisco
| | - Satvir Saggi
- 1Department of Neurological Surgery, University of California, San Francisco.,2School of Medicine, University of California, San Francisco
| | - William C Chen
- 3Department of Radiation Oncology, University of California, San Francisco
| | - Abrar Choudhury
- 2School of Medicine, University of California, San Francisco
| | - Akshay Ravi
- 4Department of Hospital Medicine, University of California, San Francisco, California
| | - David R Raleigh
- 3Department of Radiation Oncology, University of California, San Francisco
| | - Stephen T Magill
- 5Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; and
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22
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Bourdillon AT, Shah HP, Cohen O, Hajek MA, Mehra S. Novel Machine Learning Model to Predict Interval of Oral Cancer Recurrence for Surveillance Stratification. Laryngoscope 2022. [DOI: 10.1002/lary.30351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/29/2022] [Accepted: 08/01/2022] [Indexed: 12/24/2022]
Affiliation(s)
| | - Hemali P. Shah
- Yale University School of Medicine New Haven Connecticut U.S.A
| | - Oded Cohen
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery Yale University School of Medicine New Haven Connecticut U.S.A
| | - Michael A. Hajek
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery Yale University School of Medicine New Haven Connecticut U.S.A
| | - Saral Mehra
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery Yale University School of Medicine New Haven Connecticut U.S.A
- Yale Cancer Center New Haven Connecticut U.S.A
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23
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Saggi S, Winkler EA, Ammanuel SG, Morshed RA, Garcia JH, Young JS, Semonche A, Fullerton HJ, Kim H, Cooke DL, Hetts SW, Abla A, Lawton MT, Gupta N. Machine learning for predicting hemorrhage in pediatric patients with brain arteriovenous malformation. J Neurosurg Pediatr 2022; 30:203-209. [PMID: 35916099 DOI: 10.3171/2022.4.peds21470] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 04/11/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Ruptured brain arteriovenous malformations (bAVMs) in a child are associated with substantial morbidity and mortality. Prior studies investigating predictors of hemorrhagic presentation of a bAVM during childhood are limited. Machine learning (ML), which has high predictive accuracy when applied to large data sets, can be a useful adjunct for predicting hemorrhagic presentation. The goal of this study was to use ML in conjunction with a traditional regression approach to identify predictors of hemorrhagic presentation in pediatric patients based on a retrospective cohort study design. METHODS Using data obtained from 186 pediatric patients over a 19-year study period, the authors implemented three ML algorithms (random forest models, gradient boosted decision trees, and AdaBoost) to identify features that were most important for predicting hemorrhagic presentation. Additionally, logistic regression analysis was used to ascertain significant predictors of hemorrhagic presentation as a comparison. RESULTS All three ML models were consistent in identifying bAVM size and patient age at presentation as the two most important factors for predicting hemorrhagic presentation. Age at presentation was not identified as a significant predictor of hemorrhagic presentation in multivariable logistic regression. Gradient boosted decision trees/AdaBoost and random forest models identified bAVM location and a concurrent arterial aneurysm as the third most important factors, respectively. Finally, logistic regression identified a left-sided bAVM, small bAVM size, and the presence of a concurrent arterial aneurysm as significant risk factors for hemorrhagic presentation. CONCLUSIONS By using an ML approach, the authors found predictors of hemorrhagic presentation that were not identified using a conventional regression approach.
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Affiliation(s)
- Satvir Saggi
- 1Department of Neurological Surgery, University of California, San Francisco
| | - Ethan A Winkler
- 1Department of Neurological Surgery, University of California, San Francisco
| | - Simon G Ammanuel
- 1Department of Neurological Surgery, University of California, San Francisco
| | - Ramin A Morshed
- 1Department of Neurological Surgery, University of California, San Francisco
| | - Joseph H Garcia
- 1Department of Neurological Surgery, University of California, San Francisco
| | - Jacob S Young
- 1Department of Neurological Surgery, University of California, San Francisco
| | - Alexa Semonche
- 1Department of Neurological Surgery, University of California, San Francisco
| | - Heather J Fullerton
- 2Pediatric Stroke and Cerebrovascular Disease Center, Department of Neurology, University of California, San Francisco
| | - Helen Kim
- 3Center for Cerebrovascular Research, Department of Anesthesia and Perioperative Care, University of California, San Francisco
| | - Daniel L Cooke
- 4Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Steven W Hetts
- 4Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Adib Abla
- 1Department of Neurological Surgery, University of California, San Francisco
| | - Michael T Lawton
- 5Department of Neurological Surgery, Barrow Neurological Institute, Phoenix, Arizona; and
| | - Nalin Gupta
- 1Department of Neurological Surgery, University of California, San Francisco.,6Department of Pediatrics, University of California, San Francisco, California
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24
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Politano SF, Balchander D, Cabrera CI, Thuener JE, Teknos TN, Rezaee RP, Li S, Fowler N, Tamaki A. Impact of intraoperative ischemia time on acute complications of head and neck microvascular free tissue transfer: A systematic review and meta-analysis. Am J Otolaryngol 2022; 43:103467. [PMID: 35429849 DOI: 10.1016/j.amjoto.2022.103467] [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: 03/27/2022] [Accepted: 04/05/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND To evaluate the relationship between intraoperative ischemia time with acute microvascular free tissue transfer (MFTT) complications in head and neck reconstruction. METHODS A systematic review using PRISMA guidelines was performed. Inclusion encompassed all available studies published and indexed using PubMed, Medline, and Embase. Meta-analyses were performed using the Cochrane Review Manager tool. Methodological Index for Non-Randomized Studies (MINORS), the Revised Cochrane risk-of-bias tool for randomized trials, and the NIH Study Quality Assessment Tool for non-randomized prospective studies. RESULTS A total of 430 citations were reviewed. 25 were included in our overall qualitative analysis, and 14 for meta-analysis. When comparing ischemia times for flaps with and without complications, no significant difference was observed (p = 0.98). Additionally, in a separate cohort, no significant effect was realized when correlating ischemia time to overall rates and total complications. CONCLUSION Ischemia time was not significantly different between those with and without acute flap complications.
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Affiliation(s)
- Stephen F Politano
- Department of Otolaryngology- Head and Neck Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Case Western Reserve University School of Medicine, Cleveland, OH, USA.
| | | | - Claudia I Cabrera
- Department of Otolaryngology- Head and Neck Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Jason E Thuener
- Department of Otolaryngology- Head and Neck Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Theodoros N Teknos
- Department of Otolaryngology- Head and Neck Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Rod P Rezaee
- Department of Otolaryngology- Head and Neck Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Shawn Li
- Department of Otolaryngology- Head and Neck Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Nicole Fowler
- Department of Otolaryngology- Head and Neck Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Akina Tamaki
- Department of Otolaryngology- Head and Neck Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Case Western Reserve University School of Medicine, Cleveland, OH, USA
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25
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Shi YC, Li J, Li SJ, Li ZP, Zhang HJ, Wu ZY, Wu ZY. Flap failure prediction in microvascular tissue reconstruction using machine learning algorithms. World J Clin Cases 2022; 10:3729-3738. [PMID: 35647170 PMCID: PMC9100718 DOI: 10.12998/wjcc.v10.i12.3729] [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: 12/14/2021] [Revised: 02/11/2022] [Accepted: 03/06/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Microvascular tissue reconstruction is a well-established, commonly used technique for a wide variety of the tissue defects. However, flap failure is associated with an additional hospital stay, medical cost burden, and mental stress. Therefore, understanding of the risk factors associated with this event is of utmost importance.
AIM To develop machine learning-based predictive models for flap failure to identify the potential factors and screen out high-risk patients.
METHODS Using the data set of 946 consecutive patients, who underwent microvascular tissue reconstruction of free flap reconstruction for head and neck, breast, back, and extremity, we established three machine learning models including random forest classifier, support vector machine, and gradient boosting. Model performances were evaluated by the indicators such as area under the curve of receiver operating characteristic curve, accuracy, precision, recall, and F1 score. A multivariable regression analysis was performed for the most critical variables in the random forest model.
RESULTS Post-surgery, the flap failure event occurred in 34 patients (3.6%). The machine learning models based on various preoperative and intraoperative variables were successfully developed. Among them, the random forest classifier reached the best performance in receiver operating characteristic curve, with an area under the curve score of 0.770 in the test set. The top 10 variables in the random forest were age, body mass index, ischemia time, smoking, diabetes, experience, prior chemotherapy, hypertension, insulin, and obesity. Interestingly, only age, body mass index, and ischemic time were statistically associated with the outcomes.
CONCLUSION Machine learning-based algorithms, especially the random forest classifier, were very important in categorizing patients at high risk of flap failure. The occurrence of flap failure was a multifactor-driven event and was identified with numerous factors that warrant further investigation. Importantly, the successful application of machine learning models may help the clinician in decision-making, understanding the underlying pathologic mechanisms of the disease, and improving the long-term outcome of patients.
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Affiliation(s)
- Yu-Cang Shi
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Jie Li
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Shao-Jie Li
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Zhan-Peng Li
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Hui-Jun Zhang
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Ze-Yong Wu
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Zhi-Yuan Wu
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
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26
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Novel Machine Learning Approach for the Prediction of Hernia Recurrence, Surgical Complication, and 30-Day Readmission after Abdominal Wall Reconstruction. J Am Coll Surg 2022; 234:918-927. [DOI: 10.1097/xcs.0000000000000141] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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27
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Dagli MM, Rajesh A, Asaad M, Butler CE. The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review. Am Surg 2021:31348211065101. [PMID: 34958252 DOI: 10.1177/00031348211065101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Interest in the use of artificial intelligence (AI) and machine learning (ML) in medicine has grown exponentially over the last few years. With its ability to enhance speed, precision, and efficiency, AI has immense potential, especially in the field of surgery. This article aims to provide a comprehensive literature review of artificial intelligence as it applies to surgery and discuss practical examples, current applications, and challenges to the adoption of this technology. Furthermore, we elaborate on the utility of natural language processing and computer vision in improving surgical outcomes, research, and patient care.
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Affiliation(s)
| | - Aashish Rajesh
- Department of Surgery, 14742University of Texas Health Science Center, San Antonio, TX, USA
| | - Malke Asaad
- Department of Plastic & Reconstructive Surgery, 571198the University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles E Butler
- Department of Plastic & Reconstructive Surgery, 571198the University of Texas MD Anderson Cancer Center, Houston, TX, USA
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28
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Wang R, Li KY, Su YX. Prediction of ameloblastoma recurrence using random forest-a machine learning algorithm. Int J Oral Maxillofac Surg 2021; 51:886-891. [PMID: 34920910 DOI: 10.1016/j.ijom.2021.11.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 09/11/2021] [Accepted: 11/30/2021] [Indexed: 11/26/2022]
Abstract
The purpose of this study was to investigate whether ameloblastoma with a high likelihood of recurrence can be predicted using random forest model, a machine learning algorithm. Data were collected from patients treated for ameloblastoma between 1999 and 2019 at the University of Hong Kong. Fourteen clinical parameters were used to grow the decision trees to classify patients with or without ameloblastoma recurrence in the follow-up period. The random forest algorithm was computed 100 times in the training cohort (n = 100) and verified in the testing cohort (n = 50). The receiver operating characteristic curve (ROC) and area under the curve (AUC) were used as the performance measurement of separability. One hundred and fifty patients (76 female, 74 male) were recruited, with a mean follow-up time of 103 months. Recurrence occurred in a total of 25 cases (16.7%) over the 20-year period. The AUC were calculated for the median and mean ROC curves; these were 0.777 and 0.825, respectively. The results showed that random forest model was able to predict recurrence of ameloblastoma with reliable accuracy. The four most important variables influencing ameloblastoma recurrence were the time elapsed from treatment, initial surgical treatment, tumour size, and radiographic presentation. This study provides insights into the detection of high-risk patient groups to monitor recurrence. Further application of random forest to other diseases could greatly benefit clinical decisions.
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Affiliation(s)
- R Wang
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - K Y Li
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Y-X Su
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
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29
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Mantelakis A, Assael Y, Sorooshian P, Khajuria A. Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery. PLASTIC AND RECONSTRUCTIVE SURGERY-GLOBAL OPEN 2021; 9:e3638. [PMID: 34235035 PMCID: PMC8225366 DOI: 10.1097/gox.0000000000003638] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 04/14/2021] [Indexed: 01/25/2023]
Abstract
INTRODUCTION Machine learning (ML) is a set of models and methods that can detect patterns in vast amounts of data and use this information to perform various kinds of decision-making under uncertain conditions. This review explores the current role of this technology in plastic surgery by outlining the applications in clinical practice, diagnostic and prognostic accuracies, and proposed future direction for clinical applications and research. METHODS EMBASE, MEDLINE, CENTRAL and ClinicalTrials.gov were searched from 1990 to 2020. Any clinical studies (including case reports) which present the diagnostic and prognostic accuracies of machine learning models in the clinical setting of plastic surgery were included. Data collected were clinical indication, model utilised, reported accuracies, and comparison with clinical evaluation. RESULTS The database identified 1181 articles, of which 51 articles were included in this review. The clinical utility of these algorithms was to assist clinicians in diagnosis prediction (n=22), outcome prediction (n=21) and pre-operative planning (n=8). The mean accuracy is 88.80%, 86.11% and 80.28% respectively. The most commonly used models were neural networks (n=31), support vector machines (n=13), decision trees/random forests (n=10) and logistic regression (n=9). CONCLUSIONS ML has demonstrated high accuracies in diagnosis and prognostication of burn patients, congenital or acquired facial deformities, and in cosmetic surgery. There are no studies comparing ML to clinician's performance. Future research can be enhanced using larger datasets or utilising data augmentation, employing novel deep learning models, and applying these to other subspecialties of plastic surgery.
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Affiliation(s)
| | | | | | - Ankur Khajuria
- Kellogg College, University of Oxford
- Department of Surgery and Cancer, Imperial College London, UK
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30
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Han M, Ochoa E, Zhu B, Park AM, Heaton CM, Seth R, Knott PD. Risk Factors for and Cost Implications of Free Flap Take-backs: A Single Institution Review. Laryngoscope 2021; 131:E1821-E1829. [PMID: 33438765 DOI: 10.1002/lary.29382] [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] [Received: 06/22/2020] [Revised: 10/06/2020] [Accepted: 01/03/2021] [Indexed: 01/10/2023]
Abstract
OBJECTIVE/HYPOTHESIS For patients undergoing microvascular free tissue transfer (MFTT), we evaluated risk factors and financial implications of operating room (OR) take-back procedures. STUDY DESIGN Retrospective review at a tertiary care center. METHODS Patients who underwent MFTT for head and neck reconstruction from 2011 to 2018 were identified. We compared hospital length of stay and overall costs associated with OR take-back procedures. Multivariable regression analysis evaluated factors associated with OR take-backs during the same hospitalization. RESULTS A total of 727 free flaps were reviewed, and 70 OR take-backs (9.6%) were identified. Mean total length of stay (LOS) in the ICU was 3.4 days versus 6.7 days for non-take-back and take-back flaps, respectively (P < .001). Mean total LOS on the regular floor was 6.3 days versus 13.1 days, respectively (P < .001). This resulted in a cost differential of $33,507 (94.3% increase relative to non-take-back flaps). The total cost associated with an OR take-back was $39,786. Hematomas were the most common cause of take-backs and wound dehiscence was associated with the highest costs. On multivariable analysis, higher ASA class (OR, 2.06; 95% CI, 1.11-3.99; P = .026) and shorter ischemia times (OR, 0.52; 95% CI, 0.29-0.95; P = .030) were independently associated with increased risk of take-backs. CONCLUSIONS OR take-backs infrequently occur but are associated with a significant increase in financial burden when compared to free flap cases not requiring OR take-back. The large majority of the cost differential lies in a substantial increase of ICU and floor LOS for take-back flaps when compared to non-take-back flaps. LEVEL OF EVIDENCE 4 Laryngoscope, 131:E1821-E1829, 2021.
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Affiliation(s)
- Mary Han
- School of Medicine, University of California, San Francisco, California, U.S.A.,Department of Otolaryngology Head and Neck Surgery, University of California, San Francisco, California, U.S.A
| | - Edgar Ochoa
- School of Medicine, University of California, San Francisco, California, U.S.A.,Department of Otolaryngology Head and Neck Surgery, University of California, San Francisco, California, U.S.A
| | - Bovey Zhu
- Department of Otolaryngology Head and Neck Surgery, University of California, San Francisco, California, U.S.A
| | - Andrea M Park
- Department of Otolaryngology Head and Neck Surgery, University of California, San Francisco, California, U.S.A
| | - Chase M Heaton
- Department of Otolaryngology Head and Neck Surgery, University of California, San Francisco, California, U.S.A
| | - Rahul Seth
- Department of Otolaryngology Head and Neck Surgery, University of California, San Francisco, California, U.S.A
| | - P Daniel Knott
- Department of Otolaryngology Head and Neck Surgery, University of California, San Francisco, California, U.S.A
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31
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Goshtasbi K, Yasaka TM, Zandi-Toghani M, Djalilian HR, Armstrong WB, Tjoa T, Haidar YM, Abouzari M. Machine learning models to predict length of stay and discharge destination in complex head and neck surgery. Head Neck 2020; 43:788-797. [PMID: 33142001 DOI: 10.1002/hed.26528] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 10/13/2020] [Accepted: 10/23/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND This study develops machine learning (ML) algorithms that use preoperative-only features to predict discharge-to-nonhome-facility (DNHF) and length-of-stay (LOS) following complex head and neck surgeries. METHODS Patients undergoing laryngectomy or composite tissue excision followed by free tissue transfer were extracted from the 2005 to 2017 NSQIP database. RESULTS Among the 2786 included patients, DNHF and mean LOS were 421 (15.1%) and 11.7 ± 8.8 days. Four classification models for predicting DNHF with high specificities (range, 0.80-0.84) were developed. The generalized linear and gradient boosting machine models performed best with receiver operating characteristic (ROC), accuracy, and negative predictive value (NPV) of 0.72-0.73, 0.75-0.76, and 0.88-0.89. Four regression models for predicting LOS in days were developed, where all performed similarly with mean absolute error and root mean-squared errors of 3.95-3.98 and 5.14-5.16. Both models were developed into an encrypted web-based interface: https://uci-ent.shinyapps.io/head-neck/. CONCLUSION Novel and proof-of-concept ML models to predict DNHF and LOS were developed and published as web-based interfaces.
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Affiliation(s)
- Khodayar Goshtasbi
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Tyler M Yasaka
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Mehdi Zandi-Toghani
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA
| | - Hamid R Djalilian
- Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.,Department of Biomedical Engineering, University of California, Irvine, California, USA
| | - William B Armstrong
- 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
| | - Yarah M Haidar
- 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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32
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Tama BA, Kim DH, Kim G, Kim SW, Lee S. Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery. Clin Exp Otorhinolaryngol 2020; 13:326-339. [PMID: 32631041 PMCID: PMC7669308 DOI: 10.21053/ceo.2020.00654] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 05/24/2020] [Accepted: 06/09/2020] [Indexed: 12/12/2022] Open
Abstract
This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.
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Affiliation(s)
- Bayu Adhi Tama
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Do Hyun Kim
- Department of Otolaryngology-Head and Neck Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Gyuwon Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Soo Whan Kim
- Department of Otolaryngology-Head and Neck Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Seungchul Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
- Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, Korea
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Pier MM, Pasick LJ, Benito DA, Saini P, Patel S, Joshi AS, Goodman JF, Thakkar P. Adverse Events Associated with Implantable Dopplers during Microvascular Surgery. J Reconstr Microsurg 2020; 37:365-371. [PMID: 32942309 DOI: 10.1055/s-0040-1716858] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Implantable Dopplers (IDs) are widely used for postoperative free flap vascular monitoring. However, IDs may contribute to free flap complications or failure and better understanding of device malfunctions is needed. METHODS The U.S. Food and Drug Administration's Manufacturer and User Facility Device Experience database was queried for all reports of ID adverse events from two leading manufacturers (Cook Vascular and Synovis Life Technologies) in free flap surgery from January 2010 to March 2020. Reports were reviewed and categorized. A comparison of reoperations within select categories was performed using Chi-square analysis. RESULTS Of 209 included reports, the most common device malfunctions were venous anastomotic coupler misalignment (35.4%) and coupler ring detachment (24.4%). Synovis devices were used in 100% of reports of vessel compression and Cook Vascular devices were used in 77.7% of reports of probe detachment. Of 74 patient-related adverse events, the most common were reoperation (47.3%) and vessel occlusion (28.4%). Of five reported events of flap failure, two were associated with loss of ID signal. The proportion of Doppler signal loss events leading to reoperation was significantly greater than the proportion of any other Doppler-related event leading to reoperation. Intraoperative coupler replacement was the most commonly reported intervention (n = 86), and venous anastomosis with hand-suturing occurred in 30 device malfunctions. CONCLUSION This study demonstrates a variety of ID-related malfunctions. One-third of device malfunctions were associated with patient complications, and false-positive Doppler signal loss contributed substantially to the requirement of surgical re-exploration. These are important considerations for surgical teams utilizing IDs in free tissue transfer procedures.
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Affiliation(s)
- Matthew M Pier
- Department of Otolaryngology-Head and Neck Surgery, Drexel University College of Medicine, Philadelphia, Pennsylvania
| | - Luke J Pasick
- Department of Otolaryngology-Head and Neck Surgery, University of Miami Miller School of Medicine, Miami, Florida
| | - Daniel A Benito
- Division of Otolaryngology-Head and Neck Surgery, George Washington University School of Medicine and Health Sciences, Washington, District of Columbia
| | - Prashant Saini
- Division of Otolaryngology-Head and Neck Surgery, George Washington University School of Medicine and Health Sciences, Washington, District of Columbia
| | - Sahil Patel
- Department of Otolaryngology-Head and Neck Surgery, Drexel University College of Medicine, Philadelphia, Pennsylvania
| | - Arjun S Joshi
- Division of Otolaryngology-Head and Neck Surgery, George Washington University School of Medicine and Health Sciences, Washington, District of Columbia
| | - Joseph F Goodman
- Division of Otolaryngology-Head and Neck Surgery, George Washington University School of Medicine and Health Sciences, Washington, District of Columbia
| | - Punam Thakkar
- Division of Otolaryngology-Head and Neck Surgery, George Washington University School of Medicine and Health Sciences, Washington, District of Columbia
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