Predictive Outcomes of Deep Learning Measurement of the Anterior Glottic Angle in Bilateral Vocal Fold Immobility

Objective (1) To compare maximum glottic opening angle (anterior glottic angle, AGA) in patients with bilateral vocal fold immobility (BVFI), unilateral vocal fold immobility (UVFI) and normal larynges (NL), and (2) to correlate maximum AGA with patient‐reported outcome measures. Methods Patients wi...

Full description

Saved in:
Bibliographic Details
Published in:The Laryngoscope 2023-09, Vol.133 (9), p.2285-2291
Main Authors: DeVore, Elliana Kirsh, Adamian, Nat, Jowett, Nate, Wang, Tiffany, Song, Phillip, Franco, Ramon, Naunheim, Matthew Roberts
Format: Article
Language:English
Subjects:
artificial intelligence
Computer vision
Deep learning
Laryngoscopy
Patients
patient‐reported outcome measures
vocal cords
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c3573-9e2ad435c477179c6fd91f2f5263faf9a61e72e14323b54f86d38e097ebc36873
cites cdi_FETCH-LOGICAL-c3573-9e2ad435c477179c6fd91f2f5263faf9a61e72e14323b54f86d38e097ebc36873
container_end_page 2291
container_issue 9
container_start_page 2285
container_title The Laryngoscope
container_volume 133
creator DeVore, Elliana Kirsh
Adamian, Nat
Jowett, Nate
Wang, Tiffany
Song, Phillip
Franco, Ramon
Naunheim, Matthew Roberts
description Objective (1) To compare maximum glottic opening angle (anterior glottic angle, AGA) in patients with bilateral vocal fold immobility (BVFI), unilateral vocal fold immobility (UVFI) and normal larynges (NL), and (2) to correlate maximum AGA with patient‐reported outcome measures. Methods Patients wisth BVFI, UVFI, and NL were retrospectively studied. An open‐source deep learning‐based computer vision tool for vocal fold tracking was used to analyze videolaryngoscopy. Minimum and maximum AGA were calculated and correlated with three patient‐reported outcomes measures. Results Two hundred and fourteen patients were included. Mean maximum AGA was 29.91° (14.40° SD), 42.59° (12.37° SD), and 57.08° (11.14° SD) in BVFI (N = 70), UVFI (N = 70), and NL (N = 72) groups, respectively (p 
doi_str_mv 10.1002/lary.30473
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2731718043</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2848253197</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3573-9e2ad435c477179c6fd91f2f5263faf9a61e72e14323b54f86d38e097ebc36873</originalsourceid><addsrcrecordid>eNp9kc9rFDEYhoNY7LZ68Q-QgBcpTE3yzUxmjmu1P2BLRVT0FLKZLzUlM9kmGWX_e7Nu7cGDlwTyPjyE9yXkJWennDHx1uu4PQVWS3hCFrwBXtV93zwlixJC1TXi2yE5SumOMS6hYc_IIbQgWs7Egtx_jDg4k91PpDdzNmHERIOl7xE3dIU6Tm66pdeo0xxxxCnvwvwD6XLKGF2I9MKHnJ0pD7ceqZvoO-d1ybSnX4Mp53nwA70ax7B23uXtc3JgtU_44uE-Jl_OP3w-u6xWNxdXZ8tVZaCRUPUo9FBDY2opuexNa4eeW2Eb0YLVttctRymQ1yBg3dS2awfokPUS1wbaTsIxebP3bmK4nzFlNbpk0Hs9YZiTEhK45B2roaCv_0Hvwhyn8jsluroTpdJ-JzzZUyaGlCJatYluLN0rztRuCLUbQv0ZosCvHpTzesThEf3bfAH4HvjlPG7_o1Kr5afve-lvTIGSnQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2848253197</pqid></control><display><type>article</type><title>Predictive Outcomes of Deep Learning Measurement of the Anterior Glottic Angle in Bilateral Vocal Fold Immobility</title><source>Wiley-Blackwell Read &amp; Publish Collection</source><creator>DeVore, Elliana Kirsh ; Adamian, Nat ; Jowett, Nate ; Wang, Tiffany ; Song, Phillip ; Franco, Ramon ; Naunheim, Matthew Roberts</creator><creatorcontrib>DeVore, Elliana Kirsh ; Adamian, Nat ; Jowett, Nate ; Wang, Tiffany ; Song, Phillip ; Franco, Ramon ; Naunheim, Matthew Roberts</creatorcontrib><description>Objective (1) To compare maximum glottic opening angle (anterior glottic angle, AGA) in patients with bilateral vocal fold immobility (BVFI), unilateral vocal fold immobility (UVFI) and normal larynges (NL), and (2) to correlate maximum AGA with patient‐reported outcome measures. Methods Patients wisth BVFI, UVFI, and NL were retrospectively studied. An open‐source deep learning‐based computer vision tool for vocal fold tracking was used to analyze videolaryngoscopy. Minimum and maximum AGA were calculated and correlated with three patient‐reported outcomes measures. Results Two hundred and fourteen patients were included. Mean maximum AGA was 29.91° (14.40° SD), 42.59° (12.37° SD), and 57.08° (11.14° SD) in BVFI (N = 70), UVFI (N = 70), and NL (N = 72) groups, respectively (p &lt; 0.001). Patients requiring operative airway intervention for BVFI had an average maximum AGA of 24.94° (10.66° SD), statistically different from those not requiring intervention (p = 0.0001). There was moderate negative correlation between Dyspnea Index scores and AGA (Spearman r = −0.345, p = 0.0003). Maximum AGA demonstrated high discriminatory ability for BVFI diagnosis (AUC 0.92, 95% CI 0.81–0.97, p &lt; 0.001) and moderate ability to predict need for operative airway intervention (AUC 0.77, 95% CI 0.64–0.89, p &lt; 0.001). Conclusions A computer vision tool for quantitative assessment of the AGA from videolaryngoscopy demonstrated ability to discriminate between patients with BVFI, UVFI, and normal controls and predict need for operative airway intervention. This tool may be useful for assessment of other neurological laryngeal conditions and may help guide decision‐making in laryngeal surgery. Level of Evidence III Laryngoscope, 133:2285–2291, 2023 The objective of this research was to apply a computer vision tool for assessment of anterior glottic angle (AGA) in patients with bilateral vocal fold immobility (BVFI), and to compare the AGA in BVFI with that of unilateral vocal fold immobility (UVFI) and normal larynges (NL) as measured by the algorithm. The computer vision tool was able to quantitatively assessof the AGA from videolaryngoscopy, demonstrating ability to discriminate between patients with BVFI, UVFI, and normal controls, as well as to predict need for operative airway intervention. This tool may be useful for assessment of other neurological laryngeal conditions and may help guide decision‐making in laryngeal surgery.</description><identifier>ISSN: 0023-852X</identifier><identifier>EISSN: 1531-4995</identifier><identifier>DOI: 10.1002/lary.30473</identifier><identifier>PMID: 36326102</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley &amp; Sons, Inc</publisher><subject>artificial intelligence ; Computer vision ; Deep learning ; Laryngoscopy ; Patients ; patient‐reported outcome measures ; vocal cords</subject><ispartof>The Laryngoscope, 2023-09, Vol.133 (9), p.2285-2291</ispartof><rights>2022 The American Laryngological, Rhinological and Otological Society, Inc.</rights><rights>2023 The American Laryngological, Rhinological and Otological Society, Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3573-9e2ad435c477179c6fd91f2f5263faf9a61e72e14323b54f86d38e097ebc36873</citedby><cites>FETCH-LOGICAL-c3573-9e2ad435c477179c6fd91f2f5263faf9a61e72e14323b54f86d38e097ebc36873</cites><orcidid>0000-0002-5242-0264 ; 0000-0003-0206-5441 ; 0000-0002-7034-9238 ; 0000-0002-4549-6017 ; 0000-0003-2056-4658 ; 0000-0002-3927-3984</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36326102$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>DeVore, Elliana Kirsh</creatorcontrib><creatorcontrib>Adamian, Nat</creatorcontrib><creatorcontrib>Jowett, Nate</creatorcontrib><creatorcontrib>Wang, Tiffany</creatorcontrib><creatorcontrib>Song, Phillip</creatorcontrib><creatorcontrib>Franco, Ramon</creatorcontrib><creatorcontrib>Naunheim, Matthew Roberts</creatorcontrib><title>Predictive Outcomes of Deep Learning Measurement of the Anterior Glottic Angle in Bilateral Vocal Fold Immobility</title><title>The Laryngoscope</title><addtitle>Laryngoscope</addtitle><description>Objective (1) To compare maximum glottic opening angle (anterior glottic angle, AGA) in patients with bilateral vocal fold immobility (BVFI), unilateral vocal fold immobility (UVFI) and normal larynges (NL), and (2) to correlate maximum AGA with patient‐reported outcome measures. Methods Patients wisth BVFI, UVFI, and NL were retrospectively studied. An open‐source deep learning‐based computer vision tool for vocal fold tracking was used to analyze videolaryngoscopy. Minimum and maximum AGA were calculated and correlated with three patient‐reported outcomes measures. Results Two hundred and fourteen patients were included. Mean maximum AGA was 29.91° (14.40° SD), 42.59° (12.37° SD), and 57.08° (11.14° SD) in BVFI (N = 70), UVFI (N = 70), and NL (N = 72) groups, respectively (p &lt; 0.001). Patients requiring operative airway intervention for BVFI had an average maximum AGA of 24.94° (10.66° SD), statistically different from those not requiring intervention (p = 0.0001). There was moderate negative correlation between Dyspnea Index scores and AGA (Spearman r = −0.345, p = 0.0003). Maximum AGA demonstrated high discriminatory ability for BVFI diagnosis (AUC 0.92, 95% CI 0.81–0.97, p &lt; 0.001) and moderate ability to predict need for operative airway intervention (AUC 0.77, 95% CI 0.64–0.89, p &lt; 0.001). Conclusions A computer vision tool for quantitative assessment of the AGA from videolaryngoscopy demonstrated ability to discriminate between patients with BVFI, UVFI, and normal controls and predict need for operative airway intervention. This tool may be useful for assessment of other neurological laryngeal conditions and may help guide decision‐making in laryngeal surgery. Level of Evidence III Laryngoscope, 133:2285–2291, 2023 The objective of this research was to apply a computer vision tool for assessment of anterior glottic angle (AGA) in patients with bilateral vocal fold immobility (BVFI), and to compare the AGA in BVFI with that of unilateral vocal fold immobility (UVFI) and normal larynges (NL) as measured by the algorithm. The computer vision tool was able to quantitatively assessof the AGA from videolaryngoscopy, demonstrating ability to discriminate between patients with BVFI, UVFI, and normal controls, as well as to predict need for operative airway intervention. This tool may be useful for assessment of other neurological laryngeal conditions and may help guide decision‐making in laryngeal surgery.</description><subject>artificial intelligence</subject><subject>Computer vision</subject><subject>Deep learning</subject><subject>Laryngoscopy</subject><subject>Patients</subject><subject>patient‐reported outcome measures</subject><subject>vocal cords</subject><issn>0023-852X</issn><issn>1531-4995</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kc9rFDEYhoNY7LZ68Q-QgBcpTE3yzUxmjmu1P2BLRVT0FLKZLzUlM9kmGWX_e7Nu7cGDlwTyPjyE9yXkJWennDHx1uu4PQVWS3hCFrwBXtV93zwlixJC1TXi2yE5SumOMS6hYc_IIbQgWs7Egtx_jDg4k91PpDdzNmHERIOl7xE3dIU6Tm66pdeo0xxxxCnvwvwD6XLKGF2I9MKHnJ0pD7ceqZvoO-d1ybSnX4Mp53nwA70ax7B23uXtc3JgtU_44uE-Jl_OP3w-u6xWNxdXZ8tVZaCRUPUo9FBDY2opuexNa4eeW2Eb0YLVttctRymQ1yBg3dS2awfokPUS1wbaTsIxebP3bmK4nzFlNbpk0Hs9YZiTEhK45B2roaCv_0Hvwhyn8jsluroTpdJ-JzzZUyaGlCJatYluLN0rztRuCLUbQv0ZosCvHpTzesThEf3bfAH4HvjlPG7_o1Kr5afve-lvTIGSnQ</recordid><startdate>202309</startdate><enddate>202309</enddate><creator>DeVore, Elliana Kirsh</creator><creator>Adamian, Nat</creator><creator>Jowett, Nate</creator><creator>Wang, Tiffany</creator><creator>Song, Phillip</creator><creator>Franco, Ramon</creator><creator>Naunheim, Matthew Roberts</creator><general>John Wiley &amp; Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-5242-0264</orcidid><orcidid>https://orcid.org/0000-0003-0206-5441</orcidid><orcidid>https://orcid.org/0000-0002-7034-9238</orcidid><orcidid>https://orcid.org/0000-0002-4549-6017</orcidid><orcidid>https://orcid.org/0000-0003-2056-4658</orcidid><orcidid>https://orcid.org/0000-0002-3927-3984</orcidid></search><sort><creationdate>202309</creationdate><title>Predictive Outcomes of Deep Learning Measurement of the Anterior Glottic Angle in Bilateral Vocal Fold Immobility</title><author>DeVore, Elliana Kirsh ; Adamian, Nat ; Jowett, Nate ; Wang, Tiffany ; Song, Phillip ; Franco, Ramon ; Naunheim, Matthew Roberts</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3573-9e2ad435c477179c6fd91f2f5263faf9a61e72e14323b54f86d38e097ebc36873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>artificial intelligence</topic><topic>Computer vision</topic><topic>Deep learning</topic><topic>Laryngoscopy</topic><topic>Patients</topic><topic>patient‐reported outcome measures</topic><topic>vocal cords</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>DeVore, Elliana Kirsh</creatorcontrib><creatorcontrib>Adamian, Nat</creatorcontrib><creatorcontrib>Jowett, Nate</creatorcontrib><creatorcontrib>Wang, Tiffany</creatorcontrib><creatorcontrib>Song, Phillip</creatorcontrib><creatorcontrib>Franco, Ramon</creatorcontrib><creatorcontrib>Naunheim, Matthew Roberts</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>The Laryngoscope</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>DeVore, Elliana Kirsh</au><au>Adamian, Nat</au><au>Jowett, Nate</au><au>Wang, Tiffany</au><au>Song, Phillip</au><au>Franco, Ramon</au><au>Naunheim, Matthew Roberts</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predictive Outcomes of Deep Learning Measurement of the Anterior Glottic Angle in Bilateral Vocal Fold Immobility</atitle><jtitle>The Laryngoscope</jtitle><addtitle>Laryngoscope</addtitle><date>2023-09</date><risdate>2023</risdate><volume>133</volume><issue>9</issue><spage>2285</spage><epage>2291</epage><pages>2285-2291</pages><issn>0023-852X</issn><eissn>1531-4995</eissn><abstract>Objective (1) To compare maximum glottic opening angle (anterior glottic angle, AGA) in patients with bilateral vocal fold immobility (BVFI), unilateral vocal fold immobility (UVFI) and normal larynges (NL), and (2) to correlate maximum AGA with patient‐reported outcome measures. Methods Patients wisth BVFI, UVFI, and NL were retrospectively studied. An open‐source deep learning‐based computer vision tool for vocal fold tracking was used to analyze videolaryngoscopy. Minimum and maximum AGA were calculated and correlated with three patient‐reported outcomes measures. Results Two hundred and fourteen patients were included. Mean maximum AGA was 29.91° (14.40° SD), 42.59° (12.37° SD), and 57.08° (11.14° SD) in BVFI (N = 70), UVFI (N = 70), and NL (N = 72) groups, respectively (p &lt; 0.001). Patients requiring operative airway intervention for BVFI had an average maximum AGA of 24.94° (10.66° SD), statistically different from those not requiring intervention (p = 0.0001). There was moderate negative correlation between Dyspnea Index scores and AGA (Spearman r = −0.345, p = 0.0003). Maximum AGA demonstrated high discriminatory ability for BVFI diagnosis (AUC 0.92, 95% CI 0.81–0.97, p &lt; 0.001) and moderate ability to predict need for operative airway intervention (AUC 0.77, 95% CI 0.64–0.89, p &lt; 0.001). Conclusions A computer vision tool for quantitative assessment of the AGA from videolaryngoscopy demonstrated ability to discriminate between patients with BVFI, UVFI, and normal controls and predict need for operative airway intervention. This tool may be useful for assessment of other neurological laryngeal conditions and may help guide decision‐making in laryngeal surgery. Level of Evidence III Laryngoscope, 133:2285–2291, 2023 The objective of this research was to apply a computer vision tool for assessment of anterior glottic angle (AGA) in patients with bilateral vocal fold immobility (BVFI), and to compare the AGA in BVFI with that of unilateral vocal fold immobility (UVFI) and normal larynges (NL) as measured by the algorithm. The computer vision tool was able to quantitatively assessof the AGA from videolaryngoscopy, demonstrating ability to discriminate between patients with BVFI, UVFI, and normal controls, as well as to predict need for operative airway intervention. This tool may be useful for assessment of other neurological laryngeal conditions and may help guide decision‐making in laryngeal surgery.</abstract><cop>Hoboken, USA</cop><pub>John Wiley &amp; Sons, Inc</pub><pmid>36326102</pmid><doi>10.1002/lary.30473</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0002-5242-0264</orcidid><orcidid>https://orcid.org/0000-0003-0206-5441</orcidid><orcidid>https://orcid.org/0000-0002-7034-9238</orcidid><orcidid>https://orcid.org/0000-0002-4549-6017</orcidid><orcidid>https://orcid.org/0000-0003-2056-4658</orcidid><orcidid>https://orcid.org/0000-0002-3927-3984</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0023-852X
ispartof The Laryngoscope, 2023-09, Vol.133 (9), p.2285-2291
issn 0023-852X
1531-4995
language eng
recordid cdi_proquest_miscellaneous_2731718043
source Wiley-Blackwell Read & Publish Collection
subjects artificial intelligence
Computer vision
Deep learning
Laryngoscopy
Patients
patient‐reported outcome measures
vocal cords
title Predictive Outcomes of Deep Learning Measurement of the Anterior Glottic Angle in Bilateral Vocal Fold Immobility
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T19%3A14%3A19IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Predictive%20Outcomes%20of%20Deep%20Learning%20Measurement%20of%20the%20Anterior%20Glottic%20Angle%20in%20Bilateral%20Vocal%20Fold%20Immobility&rft.jtitle=The%20Laryngoscope&rft.au=DeVore,%20Elliana%20Kirsh&rft.date=2023-09&rft.volume=133&rft.issue=9&rft.spage=2285&rft.epage=2291&rft.pages=2285-2291&rft.issn=0023-852X&rft.eissn=1531-4995&rft_id=info:doi/10.1002/lary.30473&rft_dat=%3Cproquest_cross%3E2848253197%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c3573-9e2ad435c477179c6fd91f2f5263faf9a61e72e14323b54f86d38e097ebc36873%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2848253197&rft_id=info:pmid/36326102&rfr_iscdi=true