A contemporary review of machine learning in otolaryngology–head and neck surgery

2019 ◽  
Vol 130 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Matthew G. Crowson ◽  
Jonathan Ranisau ◽  
Antoine Eskander ◽  
Aaron Babier ◽  
Bin Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Head And Neck ◽  
Neck Surgery ◽  
Head And Neck Surgery

scholarly journals Machine learning models to predict length of stay and discharge destination in complex head and neck surgery

Head & Neck ◽  
2020 ◽  
Author(s):  
Khodayar Goshtasbi ◽  
Tyler M. Yasaka ◽  
Mehdi Zandi‐Toghani ◽  
Hamid R. Djalilian ◽  
William B. Armstrong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Head And Neck ◽  
Neck Surgery ◽  
Head And Neck Surgery ◽  
Discharge Destination ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Above and Beyond Age: Prediction of Major Postoperative Adverse Events in Head and Neck Surgery

2021 ◽  
pp. 000348942110412
Author(s):  
Marco A. Mascarella ◽  
Nikesh Muthukrishnan ◽  
Farhad Maleki ◽  
Marie-Jeanne Kergoat ◽  
Keith Richardson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Adverse Events ◽  
Head And Neck ◽  
Multivariate Regression ◽  
Free Tissue Transfer ◽  
Neck Surgery ◽  
Attributable Fraction ◽  
Head And Neck Surgery ◽  
Supervised Machine Learning ◽  
C Statistic

Objective: Major postoperative adverse events (MPAEs) following head and neck surgery are not infrequent and lead to significant morbidity. The objective of this study was to ascertain which factors are most predictive of MPAEs in patients undergoing head and neck surgery. Methods: A cohort study was carried out based on data from patients registered in the National Surgical Quality Improvement Program (NSQIP) from 2006 to 2018. All patients undergoing non-ambulatory head and neck surgery based on Current Procedural Terminology codes were included. Perioperative factors were evaluated to predict MPAEs within 30-days of surgery. Age was classified as both a continuous and categorical variable. Retained factors were classified by attributable fraction and C-statistic. Multivariate regression and supervised machine learning models were used to quantify the contribution of age as a predictor of MPAEs. Results: A total of 43 701 operations were analyzed with 5106 (11.7%) MPAEs. The results of supervised machine learning indicated that prolonged surgeries, anemia, free tissue transfer, weight loss, wound classification, hypoalbuminemia, wound infection, tracheotomy (concurrent with index head and neck surgery), American Society of Anesthesia (ASA) class, and sex as most predictive of MPAEs. On multivariate regression, ASA class (21.3%), hypertension on medication (15.8%), prolonged operative time (15.3%), sex (13.1%), preoperative anemia (12.8%), and free tissue transfer (9%) had the largest attributable fractions associated with MPAEs. Age was independently associated with MPAEs with an attributable fraction ranging from 0.6% to 4.3% with poor predictive ability (C-statistic 0.60). Conclusion: Surgical, comorbid, and frailty-related factors were most predictive of short-term MPAEs following head and neck surgery. Age alone contributed a small attributable fraction and poor prediction of MPAEs. Level of evidence: 3

Machine Learning in Otolaryngology-Head and Neck surgery: A Systematic Review Protocol

2020 ◽  
Author(s):  
Chang Woo Lee ◽  
Angelos Mantelakis ◽  
Bhavesh Vijay Tailor ◽  
Ankur Khajuria
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Head And Neck ◽  
Neck Surgery ◽  
Head And Neck Surgery ◽  
Machine Learning Algorithms ◽  
Original Research ◽  
Learning Approaches ◽  
Operative Outcomes ◽  
Applications Of Machine Learning

Abstract Background: Machine learning describes a subfield of artificial intelligence which utilises statistical algorithms to identify patterns in large datasets. Based on previous learning, inferences or predictions can be made given novel data. Alongside its promising potential to revolutionise consumer technology, there has been growing interest in the application of machine learning algorithms to medical practice. The aim of this study is to evaluate the applications of machine learning in Otolaryngology-Head and Neck surgery.Methods: A systematic search of EMBASE, MEDLINE and CENTRAL will be conducted from January 1990 to June 2020. Studies utilising machine learning as a tool for diagnosis, or to predict disease prognosis or post-operative outcomes in the field of Otolaryngology-Head and Neck surgery will be included. The primary outcome of interest is the accuracy of machine learning models for clinical diagnosis, disease prognostication, and in predicting post-operative outcomes. This protocol adheres to the Preferred Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines.Discussion: To our knowledge, this will be the first systematic review to assimilate and critically appraise original research on the applications of machine learning across the field of Otolaryngology-Head and Neck surgery. This review has the potential to inform the current state of this technology and guide future study of machine learning approaches within the specialty.Systematic review registration: PROSPERO CRD42020192493

Application of the Laser in the Head and Neck Surgery

1987 ◽  
Vol 7 (3) ◽  
pp. 173-174
Author(s):  
Issei Ichimiya ◽  
Yuichi Kurono ◽  
Goro Mogi
Keyword(s):  
Head And Neck ◽  
Neck Surgery ◽  
Head And Neck Surgery
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