Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science
Background: Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We...
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| Veröffentlicht in: | Journal of clinical medicine 2022-08, Vol.11 (17), p.4998 |
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| creator | Knoedler, Leonard Baecher, Helena Kauke-Navarro, Martin Prantl, Lukas Machens, Hans-Günther Scheuermann, Philipp Palm, Christoph Baumann, Raphael Kehrer, Andreas Panayi, Adriana C. Knoedler, Samuel |
| description | Background: Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). Methods: Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. Results: The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%). Conclusions: Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon’s workflow. |
| doi_str_mv | 10.3390/jcm11174998 |
| format | Article |
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Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). Methods: Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. Results: The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%). Conclusions: Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon’s workflow.</description><identifier>ISSN: 2077-0383</identifier><identifier>EISSN: 2077-0383</identifier><identifier>DOI: 10.3390/jcm11174998</identifier><identifier>PMID: 36078928</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Asymmetry ; Automation ; Bell's palsy ; Classification ; Clinical medicine ; Disease ; Localization ; Machine learning ; Mouth ; Neural networks ; Patients ; Physiology ; Social research ; Surgery ; Symmetry</subject><ispartof>Journal of clinical medicine, 2022-08, Vol.11 (17), p.4998</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c386t-20157a15280491922353d72056dfb61f1cbc8c6234bf6a016730974b1e1decff3</citedby><cites>FETCH-LOGICAL-c386t-20157a15280491922353d72056dfb61f1cbc8c6234bf6a016730974b1e1decff3</cites><orcidid>0000-0003-4053-9855 ; 0000-0001-9468-2871 ; 0000-0003-2454-2499 ; 0000-0001-9472-7662</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9457271/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9457271/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids></links><search><creatorcontrib>Knoedler, Leonard</creatorcontrib><creatorcontrib>Baecher, Helena</creatorcontrib><creatorcontrib>Kauke-Navarro, Martin</creatorcontrib><creatorcontrib>Prantl, Lukas</creatorcontrib><creatorcontrib>Machens, Hans-Günther</creatorcontrib><creatorcontrib>Scheuermann, Philipp</creatorcontrib><creatorcontrib>Palm, Christoph</creatorcontrib><creatorcontrib>Baumann, Raphael</creatorcontrib><creatorcontrib>Kehrer, Andreas</creatorcontrib><creatorcontrib>Panayi, Adriana C.</creatorcontrib><creatorcontrib>Knoedler, Samuel</creatorcontrib><title>Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science</title><title>Journal of clinical medicine</title><description>Background: Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). Methods: Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. Results: The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%). Conclusions: Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon’s workflow.</description><subject>Asymmetry</subject><subject>Automation</subject><subject>Bell's palsy</subject><subject>Classification</subject><subject>Clinical medicine</subject><subject>Disease</subject><subject>Localization</subject><subject>Machine learning</subject><subject>Mouth</subject><subject>Neural networks</subject><subject>Patients</subject><subject>Physiology</subject><subject>Social research</subject><subject>Surgery</subject><subject>Symmetry</subject><issn>2077-0383</issn><issn>2077-0383</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpdkd9LHDEQx0NReqI-9R8I9EWQq_mxu8n6IByH2oLS4tnnkE1mrzl2N2eSVQ7_-eZQ5M55mBlmPnyZ4YvQN0p-cF6Ti5XpKaWiqGv5BR0xIsSUcMkPdvoJOo1xRXJIWTAqvqIJr4iQNZNH6PXRv-hgI9b4ATqnmw6wHix-0Gtn8WxMvtcJLL4N2rphiRebmKDHbsA32jjd4T-6i5uck4Mhxcv98WIMSwgbfA-QIp77fj0mCHhhMmzgBB22GYPT93qM_t5cP85_Tu9-3_6az-6mhssqTRmhpdC0ZJIUNa0Z4yW3gpGysm1T0ZaaxkhTMV40baUJrQQntSgaCtSCaVt-jK7edNdj04M1-dCgO7UOrtdho7x2an8zuH9q6Z9VXZSCCZoFzt4Fgn8aISbVu2ig6_QAfowqM0xuLdii3z-hKz-GIb-3pShnBSEiU-dvlAk-xgDtxzGUqK2vasdX_h_V-JPV</recordid><startdate>20220825</startdate><enddate>20220825</enddate><creator>Knoedler, Leonard</creator><creator>Baecher, Helena</creator><creator>Kauke-Navarro, Martin</creator><creator>Prantl, Lukas</creator><creator>Machens, Hans-Günther</creator><creator>Scheuermann, Philipp</creator><creator>Palm, Christoph</creator><creator>Baumann, Raphael</creator><creator>Kehrer, Andreas</creator><creator>Panayi, Adriana C.</creator><creator>Knoedler, Samuel</creator><general>MDPI AG</general><general>MDPI</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-4053-9855</orcidid><orcidid>https://orcid.org/0000-0001-9468-2871</orcidid><orcidid>https://orcid.org/0000-0003-2454-2499</orcidid><orcidid>https://orcid.org/0000-0001-9472-7662</orcidid></search><sort><creationdate>20220825</creationdate><title>Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science</title><author>Knoedler, Leonard ; Baecher, Helena ; Kauke-Navarro, Martin ; Prantl, Lukas ; Machens, Hans-Günther ; Scheuermann, Philipp ; Palm, Christoph ; Baumann, Raphael ; Kehrer, Andreas ; Panayi, Adriana C. ; Knoedler, Samuel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c386t-20157a15280491922353d72056dfb61f1cbc8c6234bf6a016730974b1e1decff3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Asymmetry</topic><topic>Automation</topic><topic>Bell's palsy</topic><topic>Classification</topic><topic>Clinical medicine</topic><topic>Disease</topic><topic>Localization</topic><topic>Machine learning</topic><topic>Mouth</topic><topic>Neural networks</topic><topic>Patients</topic><topic>Physiology</topic><topic>Social research</topic><topic>Surgery</topic><topic>Symmetry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Knoedler, Leonard</creatorcontrib><creatorcontrib>Baecher, Helena</creatorcontrib><creatorcontrib>Kauke-Navarro, Martin</creatorcontrib><creatorcontrib>Prantl, Lukas</creatorcontrib><creatorcontrib>Machens, Hans-Günther</creatorcontrib><creatorcontrib>Scheuermann, Philipp</creatorcontrib><creatorcontrib>Palm, Christoph</creatorcontrib><creatorcontrib>Baumann, Raphael</creatorcontrib><creatorcontrib>Kehrer, Andreas</creatorcontrib><creatorcontrib>Panayi, Adriana C.</creatorcontrib><creatorcontrib>Knoedler, Samuel</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of clinical medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Knoedler, Leonard</au><au>Baecher, Helena</au><au>Kauke-Navarro, Martin</au><au>Prantl, Lukas</au><au>Machens, Hans-Günther</au><au>Scheuermann, Philipp</au><au>Palm, Christoph</au><au>Baumann, Raphael</au><au>Kehrer, Andreas</au><au>Panayi, Adriana C.</au><au>Knoedler, Samuel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science</atitle><jtitle>Journal of clinical medicine</jtitle><date>2022-08-25</date><risdate>2022</risdate><volume>11</volume><issue>17</issue><spage>4998</spage><pages>4998-</pages><issn>2077-0383</issn><eissn>2077-0383</eissn><abstract>Background: Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). Methods: Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. Results: The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%). 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| subjects | Asymmetry Automation Bell's palsy Classification Clinical medicine Disease Localization Machine learning Mouth Neural networks Patients Physiology Social research Surgery Symmetry |
| title | Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science |
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