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Critical element prediction of tracheal intubation difficulty: Automatic Mallampati classification by jointly using handcrafted and attention-based deep features
Preoperative assessment of the difficulty of tracheal intubation is of great importance in anesthesia practice because failed intubation can lead to severe complications and even death. The Mallampati score is widely used as a critical assessment criterion in combination with other measures to asses...
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Published in: | Computers in biology and medicine 2022-11, Vol.150, p.106182, Article 106182 |
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description | Preoperative assessment of the difficulty of tracheal intubation is of great importance in anesthesia practice because failed intubation can lead to severe complications and even death. The Mallampati score is widely used as a critical assessment criterion in combination with other measures to assess the difficulty of tracheal intubation. The performance of existing methods for Mallampati classification with artificial intelligence (AI) is unreliable to the extent that the current clinical judgment of the Mallampati score relies entirely on doctors' experience. In this paper, we propose a new method for automatic Mallampati classification. Our method extracts deep features that are more favorable for the Mallampati classification task by introducing an attention mechanism into the basic deep convolutional neural network (DCNN) and then further improves the classification performance by jointly using attention-based deep features with handcrafted features. We conducted experiments on a dataset consisting of 321 oral images collected online. The proposed method has a classification accuracy of 97.50%, a sensitivity of 96.52%, a specificity of 98.05%, and an F1 score of 96.52% after five-fold cross-validation. The experimental results show that our proposed method is superior to other methods, can assist doctors in determining Mallampati class objectively and accurately, and provide an essential reference element for assessing the difficulty of tracheal intubation.
Our method extracts deep features that are more favorable for the Mallampati classification task by introducing the coordinate attention (CA) module into the basic deep convolutional neural network, and then further improves Mallampati classification performance by jointly using attention-based deep features with handcrafted features. [Display omitted]
•Introducing an attention mechanism to DCNN to extract attention-based deep features.•Training DCNN using a transfer learning approach when extracting deep features.•Extracting two handcrafted features, namely HOG and LBP.•Joint handcrafted and attention-based deep features for the Mallampati classification.•Our proposed method achieves the best results on the larger dataset we constructed. |
doi_str_mv | 10.1016/j.compbiomed.2022.106182 |
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Our method extracts deep features that are more favorable for the Mallampati classification task by introducing the coordinate attention (CA) module into the basic deep convolutional neural network, and then further improves Mallampati classification performance by jointly using attention-based deep features with handcrafted features. [Display omitted]
•Introducing an attention mechanism to DCNN to extract attention-based deep features.•Training DCNN using a transfer learning approach when extracting deep features.•Extracting two handcrafted features, namely HOG and LBP.•Joint handcrafted and attention-based deep features for the Mallampati classification.•Our proposed method achieves the best results on the larger dataset we constructed.</description><identifier>ISSN: 0010-4825</identifier><identifier>ISSN: 1879-0534</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/j.compbiomed.2022.106182</identifier><identifier>PMID: 36242810</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Algorithms ; Anesthesia ; Artificial Intelligence ; Artificial neural networks ; Attention mechanism ; Attention task ; Classification ; Clinical medicine ; Complications ; Datasets ; Deep convolutional neural network (DCNN) ; Deep features ; Deep learning ; Feature extraction ; Handcrafted features ; Image classification ; Intubation ; Intubation, Intratracheal - methods ; Laryngoscopy - methods ; Machine learning ; Mallampati classification ; Methods ; Mouth ; Neural networks ; Semantics ; Support vector machines</subject><ispartof>Computers in biology and medicine, 2022-11, Vol.150, p.106182, Article 106182</ispartof><rights>2022 Elsevier Ltd</rights><rights>Copyright © 2022 Elsevier Ltd. All rights reserved.</rights><rights>2022. Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c402t-534d4a4a68db4a9101294ebeaac2a2c8d17b379f3b91779ad760ed266abd69503</citedby><cites>FETCH-LOGICAL-c402t-534d4a4a68db4a9101294ebeaac2a2c8d17b379f3b91779ad760ed266abd69503</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36242810$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Fan</creatorcontrib><creatorcontrib>Xu, Yuelei</creatorcontrib><creatorcontrib>Zhou, Zhaoyun</creatorcontrib><creatorcontrib>Zhang, Han</creatorcontrib><creatorcontrib>Yang, Ke</creatorcontrib><title>Critical element prediction of tracheal intubation difficulty: Automatic Mallampati classification by jointly using handcrafted and attention-based deep features</title><title>Computers in biology and medicine</title><addtitle>Comput Biol Med</addtitle><description>Preoperative assessment of the difficulty of tracheal intubation is of great importance in anesthesia practice because failed intubation can lead to severe complications and even death. The Mallampati score is widely used as a critical assessment criterion in combination with other measures to assess the difficulty of tracheal intubation. The performance of existing methods for Mallampati classification with artificial intelligence (AI) is unreliable to the extent that the current clinical judgment of the Mallampati score relies entirely on doctors' experience. In this paper, we propose a new method for automatic Mallampati classification. Our method extracts deep features that are more favorable for the Mallampati classification task by introducing an attention mechanism into the basic deep convolutional neural network (DCNN) and then further improves the classification performance by jointly using attention-based deep features with handcrafted features. We conducted experiments on a dataset consisting of 321 oral images collected online. The proposed method has a classification accuracy of 97.50%, a sensitivity of 96.52%, a specificity of 98.05%, and an F1 score of 96.52% after five-fold cross-validation. The experimental results show that our proposed method is superior to other methods, can assist doctors in determining Mallampati class objectively and accurately, and provide an essential reference element for assessing the difficulty of tracheal intubation.
Our method extracts deep features that are more favorable for the Mallampati classification task by introducing the coordinate attention (CA) module into the basic deep convolutional neural network, and then further improves Mallampati classification performance by jointly using attention-based deep features with handcrafted features. [Display omitted]
•Introducing an attention mechanism to DCNN to extract attention-based deep features.•Training DCNN using a transfer learning approach when extracting deep features.•Extracting two handcrafted features, namely HOG and LBP.•Joint handcrafted and attention-based deep features for the Mallampati classification.•Our proposed method achieves the best results on the larger dataset we constructed.</description><subject>Algorithms</subject><subject>Anesthesia</subject><subject>Artificial Intelligence</subject><subject>Artificial neural networks</subject><subject>Attention mechanism</subject><subject>Attention task</subject><subject>Classification</subject><subject>Clinical medicine</subject><subject>Complications</subject><subject>Datasets</subject><subject>Deep convolutional neural network (DCNN)</subject><subject>Deep features</subject><subject>Deep learning</subject><subject>Feature extraction</subject><subject>Handcrafted features</subject><subject>Image classification</subject><subject>Intubation</subject><subject>Intubation, Intratracheal - methods</subject><subject>Laryngoscopy - methods</subject><subject>Machine learning</subject><subject>Mallampati classification</subject><subject>Methods</subject><subject>Mouth</subject><subject>Neural networks</subject><subject>Semantics</subject><subject>Support vector machines</subject><issn>0010-4825</issn><issn>1879-0534</issn><issn>1879-0534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqFkc9u1DAQxi1ERbeFV0CWuHDJYjvOP25lRQtSUS9wtsb2hDpK4mA7lfZxeFO8TSskLpw8-uY3M575CKGc7Tnj9Ydhb_y0aOcntHvBhMhyzVvxgux423QFq0r5kuwY46yQrajOyUWMA2NMspK9IudlLaRoOduR34fgkjMwUhxxwjnRJaB1Jjk_U9_TFMDcY067Oa0aHmXr-t6ZdUzHj_RqTX7KsqHfYBxhWnJMzQgxusxsvD7Swef68UjX6Oaf9B5mawL0CS3NIYWU8uSMFhpi1iziQnuEtAaMr8lZD2PEN0_vJflx_fn74Utxe3fz9XB1WxjJRCryxlaChLq1WkKXzyQ6iRoBjABhWssbXTZdX-qON00HtqkZWlHXoG3dVay8JO-3vkvwv1aMSU0uGsxLzejXqEQjKsEkF2VG3_2DDn4Nc_7dieq6qtkathtlgo8xYK-W4CYIR8WZOtmoBvXXRnWyUW025tK3TwNWfco9Fz77loFPG4D5Ig8Og4rG4WyydQFNUta7_0_5AxwDtyc</recordid><startdate>202211</startdate><enddate>202211</enddate><creator>Zhang, Fan</creator><creator>Xu, Yuelei</creator><creator>Zhou, Zhaoyun</creator><creator>Zhang, Han</creator><creator>Yang, Ke</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>M7Z</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>202211</creationdate><title>Critical element prediction of tracheal intubation difficulty: Automatic Mallampati classification by jointly using handcrafted and attention-based deep features</title><author>Zhang, Fan ; Xu, Yuelei ; Zhou, Zhaoyun ; Zhang, Han ; Yang, Ke</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c402t-534d4a4a68db4a9101294ebeaac2a2c8d17b379f3b91779ad760ed266abd69503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Anesthesia</topic><topic>Artificial Intelligence</topic><topic>Artificial neural networks</topic><topic>Attention mechanism</topic><topic>Attention task</topic><topic>Classification</topic><topic>Clinical medicine</topic><topic>Complications</topic><topic>Datasets</topic><topic>Deep convolutional neural network (DCNN)</topic><topic>Deep features</topic><topic>Deep learning</topic><topic>Feature extraction</topic><topic>Handcrafted features</topic><topic>Image classification</topic><topic>Intubation</topic><topic>Intubation, Intratracheal - methods</topic><topic>Laryngoscopy - methods</topic><topic>Machine learning</topic><topic>Mallampati classification</topic><topic>Methods</topic><topic>Mouth</topic><topic>Neural networks</topic><topic>Semantics</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Fan</creatorcontrib><creatorcontrib>Xu, Yuelei</creatorcontrib><creatorcontrib>Zhou, Zhaoyun</creatorcontrib><creatorcontrib>Zhang, Han</creatorcontrib><creatorcontrib>Yang, Ke</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Databases</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Biochemistry Abstracts 1</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</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>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Computers in biology and medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Fan</au><au>Xu, Yuelei</au><au>Zhou, Zhaoyun</au><au>Zhang, Han</au><au>Yang, Ke</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Critical element prediction of tracheal intubation difficulty: Automatic Mallampati classification by jointly using handcrafted and attention-based deep features</atitle><jtitle>Computers in biology and medicine</jtitle><addtitle>Comput Biol Med</addtitle><date>2022-11</date><risdate>2022</risdate><volume>150</volume><spage>106182</spage><pages>106182-</pages><artnum>106182</artnum><issn>0010-4825</issn><issn>1879-0534</issn><eissn>1879-0534</eissn><abstract>Preoperative assessment of the difficulty of tracheal intubation is of great importance in anesthesia practice because failed intubation can lead to severe complications and even death. The Mallampati score is widely used as a critical assessment criterion in combination with other measures to assess the difficulty of tracheal intubation. The performance of existing methods for Mallampati classification with artificial intelligence (AI) is unreliable to the extent that the current clinical judgment of the Mallampati score relies entirely on doctors' experience. In this paper, we propose a new method for automatic Mallampati classification. Our method extracts deep features that are more favorable for the Mallampati classification task by introducing an attention mechanism into the basic deep convolutional neural network (DCNN) and then further improves the classification performance by jointly using attention-based deep features with handcrafted features. We conducted experiments on a dataset consisting of 321 oral images collected online. The proposed method has a classification accuracy of 97.50%, a sensitivity of 96.52%, a specificity of 98.05%, and an F1 score of 96.52% after five-fold cross-validation. The experimental results show that our proposed method is superior to other methods, can assist doctors in determining Mallampati class objectively and accurately, and provide an essential reference element for assessing the difficulty of tracheal intubation.
Our method extracts deep features that are more favorable for the Mallampati classification task by introducing the coordinate attention (CA) module into the basic deep convolutional neural network, and then further improves Mallampati classification performance by jointly using attention-based deep features with handcrafted features. [Display omitted]
•Introducing an attention mechanism to DCNN to extract attention-based deep features.•Training DCNN using a transfer learning approach when extracting deep features.•Extracting two handcrafted features, namely HOG and LBP.•Joint handcrafted and attention-based deep features for the Mallampati classification.•Our proposed method achieves the best results on the larger dataset we constructed.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>36242810</pmid><doi>10.1016/j.compbiomed.2022.106182</doi></addata></record> |
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subjects | Algorithms Anesthesia Artificial Intelligence Artificial neural networks Attention mechanism Attention task Classification Clinical medicine Complications Datasets Deep convolutional neural network (DCNN) Deep features Deep learning Feature extraction Handcrafted features Image classification Intubation Intubation, Intratracheal - methods Laryngoscopy - methods Machine learning Mallampati classification Methods Mouth Neural networks Semantics Support vector machines |
title | Critical element prediction of tracheal intubation difficulty: Automatic Mallampati classification by jointly using handcrafted and attention-based deep features |
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