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

scholarly journals Noise in Otolaryngology – Head and Neck Surgery operating rooms: a systematic review

2021 ◽  
Vol 50 (1) ◽  
Author(s):  
Gianluca Sampieri ◽  
Amirpouyan Namavarian ◽  
Marc Levin ◽  
Justine Philteos ◽  
Jong Wook Lee ◽  
...  
Keyword(s):  
Systematic Review ◽  
Head And Neck ◽  
Patient Outcomes ◽  
Noise Level ◽  
Neck Surgery ◽  
Operating Rooms ◽  
Head And Neck Surgery ◽  
Noise Levels ◽  
Surgical Performance ◽  
The Mean

Abstract Objective Noise in operating rooms (OR) can have negative effects on both patients and surgical care workers. Noise can also impact surgical performance, team communication, and patient outcomes. Such implications of noise have been studied in orthopedics, neurosurgery, and urology. High noise levels have also been demonstrated in Otolaryngology-Head and Neck Surgery (OHNS) procedures. Despite this, no previous study has amalgamated the data on noise across all OHNS ORs to determine how much noise is present during OHNS surgeries. This study aims to review all the literature on noise associated with OHNS ORs and procedures. Methods Ovid Medline, EMBASE Classic, Pubmed, SCOPUS and Cochrane databases were searched following PRISMA guidelines. Data was collected on noise measurement location and surgery type. Descriptive results and statistical analysis were completed using Stata. Results This search identified 2914 articles. Final inclusion consisted of 22 studies. The majority of articles analyzed noise level exposures during mastoid surgery (18/22, 82%). The maximum noise level across all OHNS ORs and OHNS cadaver studies were 95.5 a-weighted decibels (dBA) and 106.6 c-weighted decibels (dBC), respectively (P = 0.2068). The mean noise level across all studies was significantly higher in OHNS cadaver labs (96.9 dBA) compared to OHNS ORs (70.1 dBA) (P = 0.0038). When analyzed together, the mean noise levels were 84.9 dBA. Conclusions This systematic review demonstrates that noise exposure in OHNS surgery exceeds safety thresholds. Further research is needed to understand how noise may affect team communication, surgical performance and patient outcomes in OHNS ORs. Graphical abstract

scholarly journals Current Advances in Robotics for Head and Neck Surgery—A Systematic Review

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1398
Author(s):  
Felix Boehm ◽  
Rene Graesslin ◽  
Marie-Nicole Theodoraki ◽  
Leon Schild ◽  
Jens Greve ◽  
...  
Keyword(s):  
Systematic Review ◽  
Head And Neck ◽  
Large Scale ◽  
Case Series ◽  
Clinical Effectiveness ◽  
Neck Surgery ◽  
Head And Neck Surgery ◽  
Daily Routine ◽  
Surgical Robots ◽  
Standard Procedures

Background. In the past few years, surgical robots have recently entered the medical field, particularly in urology, gynecology, and general surgery. However, the clinical effectiveness and safety of robot-assisted surgery (RAS) in the field of head and neck surgery has not been clearly established. In this review, we evaluate to what extent RAS can potentially be applied in head and neck surgery, in which fields it is already daily routine and what advantages can be seen in comparison to conventional surgery. Data sources. For this purpose, we conducted a systematic review of trials published between 2000 and 2021, as well as currently ongoing trials registered in clinicaltrials.gov. The results were structured according to anatomical regions, for the topics “Costs,” “current clinical trials,” and “robotic research” we added separate sections for the sake of clarity. Results. Our findings show a lack of large-scale systematic randomized trials on the use of robots in head and neck surgery. Most studies include small case series or lack a control arm which enables a comparison with established standard procedures. Conclusion. The question of financial reimbursement is still not answered and the systems on the market still require some specific improvements for the use in head and neck surgery.

scholarly journals Late Free Flap Failure in Head and Neck Reconstruction: A Systematic Review

2018 ◽  
Vol 97 (7) ◽  
pp. 213-216 ◽  
Author(s):  
David Forner ◽  
Blair A. Williams ◽  
Fawaz M. Makki ◽  
Jonathan R.B. Trites ◽  
S. Mark Taylor ◽  
...  
Keyword(s):  
Systematic Review ◽  
Head And Neck ◽  
Free Flap ◽  
Neck Surgery ◽  
Head And Neck Surgery ◽  
Head And Neck Reconstruction ◽  
Time To Failure ◽  
Close Monitoring ◽  
Flap Failure ◽  
Vascular Compromise

Our objectives were to review all reported cases of late flap failure in head and neck surgery and describe any relevant patterns. We conducted a systematic review of all published cases of free flap failure after postoperative day 7 in head and neck surgery from January 1990 to January 2018. Data were collected with respect to flap type, site of reconstruction, reason for failure, and time to failure. A total of 45 cases of late free flap failure in the head and neck were identified. Among the 34 cases in which the necessary data were available for analysis, 50% of late failures occurred between postoperative day 7 and 14. Common reasons for failure were abscess and vascular compromise. We conclude that most late flap failures occur in the second postoperative week. In patients with risk factors for flap failure, close monitoring for up to 14 days after surgery could detect flap compromise before the flap is lost.

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 Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review

Diagnostics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 29 ◽  
Author(s):  
Lea Pehrson ◽  
Michael Nielsen ◽  
Carsten Ammitzbøl Lauridsen
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Ct Scans ◽  
Lung Nodules ◽  
Original Research ◽  
Feature Based ◽  
High Level ◽  
Meta Analyses

The aim of this study was to provide an overview of the literature available on machine learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection (LIDC-IDRI) database as a tool for the optimization of detecting lung nodules in thoracic CT scans. This systematic review was compiled according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles concerning algorithms applied to the LIDC-IDRI database were included. The initial search yielded 1972 publications after removing duplicates, and 41 of these articles were included in this study. The articles were divided into two subcategories describing their overall architecture. The majority of feature-based algorithms achieved an accuracy >90% compared to the deep learning (DL) algorithms that achieved an accuracy in the range of 82.2%–97.6%. In conclusion, ML and DL algorithms are able to detect lung nodules with a high level of accuracy, sensitivity, and specificity using ML, when applied to an annotated archive of CT scans of the lung. However, there is no consensus on the method applied to determine the efficiency of ML algorithms.

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 Ent Instructions for Authors

2017 ◽  
Vol 96 (7) ◽  
pp. 271-272
Keyword(s):  
Clinical Practice ◽  
Head And Neck ◽  
Basic Science ◽  
Case Reports ◽  
Neck Surgery ◽  
Head And Neck Surgery ◽  
Original Research ◽  
Letters To The Editor

Ear, Nose & Throat Journal publishes original articles on the clinical practice and basic science of otolaryngology–head and neck surgery. Authors may submit case reports, reviews, and original research, as well as brief papers suitable for one of the regularly published ENT Clinics (departments), guest editorials, or letters to the editor.

scholarly journals Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6345
Author(s):  
Floriant Labarrière ◽  
Elizabeth Thomas ◽  
Laurine Calistri ◽  
Virgil Optasanu ◽  
Mathieu Gueugnon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Learning Algorithms ◽  
Assistive Devices ◽  
Machine Learning Algorithms ◽  
Assistive Device ◽  
Learning Approaches ◽  
Study Characteristics ◽  
Locomotion Mode ◽  
Meta Analyses

Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published between 1 January 2000 to 31 July 2020. This systematic review is reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and is registered on Prospero (CRD42020149352). Study characteristics, sensors and algorithms used, accuracy and robustness were also summarized. In total, 1343 records were identified and 58 studies were included in this review. The experimental condition which was most often investigated was level ground walking along with stair and ramp ascent/descent activities. The machine learning algorithms implemented in the included studies reached global mean accuracies of around 90%. However, the robustness of those algorithms seems to be more broadly evaluated, notably, in everyday life. We also propose some guidelines for homogenizing future reports.

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

Sign in / Sign up

Export Citation Format

Share Document