Neoplasms in the Nasal Cavity Identified and Tracked with an Artificial Intelligence-Assisted Nasal Endoscopic Diagnostic System
We aim to construct an artificial intelligence (AI)-assisted nasal endoscopy diagnostic system capable of preliminary differentiation and identification of nasal neoplasia properties, as well as intraoperative tracking, providing an important basis for nasal endoscopic surgery. We retrospectively an...
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| Veröffentlicht in: | Bioengineering (Basel) 2024-12, Vol.12 (1), p.10 |
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| creator | Xu, Xiayue Yun, Boxiang Zhao, Yumin Jin, Ling Zong, Yanning Yu, Guanzhen Zhao, Chuanliang Fan, Kai Zhang, Xiaolin Tan, Shiwang Zhang, Zimu Wang, Yan Li, Qingli Yu, Shaoqing |
| description | We aim to construct an artificial intelligence (AI)-assisted nasal endoscopy diagnostic system capable of preliminary differentiation and identification of nasal neoplasia properties, as well as intraoperative tracking, providing an important basis for nasal endoscopic surgery.
We retrospectively analyzed 1050 video data of nasal endoscopic surgeries involving four types of nasal neoplasms. Using Deep Snake, U-Net, and Att-Res2-UNet, we developed a nasal neoplastic detection network based on endoscopic images. After deep learning, the optimal network was selected as the initialization model and trained to optimize the SiamMask online tracking algorithm.
The Att-Res2-UNet network demonstrated the highest accuracy and precision, with the most accurate recognition results. The overall accuracy of the model established by us achieved an overall accuracy similar to that of residents (0.9707 ± 0.00984), while slightly lower than that of rhinologists (0.9790 ± 0.00348). SiamMask's segmentation range was consistent with rhinologists, with a 99% compliance rate and a neoplasm probability value ≥ 0.5.
This study successfully established an AI-assisted nasal endoscopic diagnostic system that can preliminarily identify nasal neoplasms from endoscopic images and automatically track them in real time during surgery, enhancing the efficiency of endoscopic diagnosis and surgery. |
| doi_str_mv | 10.3390/bioengineering12010010 |
| format | Article |
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We retrospectively analyzed 1050 video data of nasal endoscopic surgeries involving four types of nasal neoplasms. Using Deep Snake, U-Net, and Att-Res2-UNet, we developed a nasal neoplastic detection network based on endoscopic images. After deep learning, the optimal network was selected as the initialization model and trained to optimize the SiamMask online tracking algorithm.
The Att-Res2-UNet network demonstrated the highest accuracy and precision, with the most accurate recognition results. The overall accuracy of the model established by us achieved an overall accuracy similar to that of residents (0.9707 ± 0.00984), while slightly lower than that of rhinologists (0.9790 ± 0.00348). SiamMask's segmentation range was consistent with rhinologists, with a 99% compliance rate and a neoplasm probability value ≥ 0.5.
This study successfully established an AI-assisted nasal endoscopic diagnostic system that can preliminarily identify nasal neoplasms from endoscopic images and automatically track them in real time during surgery, enhancing the efficiency of endoscopic diagnosis and surgery.</description><identifier>ISSN: 2306-5354</identifier><identifier>EISSN: 2306-5354</identifier><identifier>DOI: 10.3390/bioengineering12010010</identifier><identifier>PMID: 39851283</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Accuracy ; Algorithms ; Artificial intelligence ; Cancer ; computer-assisted surgery ; Datasets ; Deep learning ; diagnosis ; Diagnostic systems ; Endoscopy ; Hospitals ; Image processing ; Image segmentation ; Machine learning ; Marking and tracking techniques ; Medical imaging ; nasal cavity ; Neoplasms ; Nose ; Optimization ; Patients ; Polyps ; Surgery ; Tracking ; Tumors ; Video data</subject><ispartof>Bioengineering (Basel), 2024-12, Vol.12 (1), p.10</ispartof><rights>2024 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>2024 by the authors. 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2530-20738868ea0bb5721404791989655d5bee7ef512f734c530176deec25a916d083</cites><orcidid>0000-0001-5063-8801 ; 0000-0003-2208-4535</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/PMC11762390/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11762390/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39851283$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Xu, Xiayue</creatorcontrib><creatorcontrib>Yun, Boxiang</creatorcontrib><creatorcontrib>Zhao, Yumin</creatorcontrib><creatorcontrib>Jin, Ling</creatorcontrib><creatorcontrib>Zong, Yanning</creatorcontrib><creatorcontrib>Yu, Guanzhen</creatorcontrib><creatorcontrib>Zhao, Chuanliang</creatorcontrib><creatorcontrib>Fan, Kai</creatorcontrib><creatorcontrib>Zhang, Xiaolin</creatorcontrib><creatorcontrib>Tan, Shiwang</creatorcontrib><creatorcontrib>Zhang, Zimu</creatorcontrib><creatorcontrib>Wang, Yan</creatorcontrib><creatorcontrib>Li, Qingli</creatorcontrib><creatorcontrib>Yu, Shaoqing</creatorcontrib><title>Neoplasms in the Nasal Cavity Identified and Tracked with an Artificial Intelligence-Assisted Nasal Endoscopic Diagnostic System</title><title>Bioengineering (Basel)</title><addtitle>Bioengineering (Basel)</addtitle><description>We aim to construct an artificial intelligence (AI)-assisted nasal endoscopy diagnostic system capable of preliminary differentiation and identification of nasal neoplasia properties, as well as intraoperative tracking, providing an important basis for nasal endoscopic surgery.
We retrospectively analyzed 1050 video data of nasal endoscopic surgeries involving four types of nasal neoplasms. Using Deep Snake, U-Net, and Att-Res2-UNet, we developed a nasal neoplastic detection network based on endoscopic images. After deep learning, the optimal network was selected as the initialization model and trained to optimize the SiamMask online tracking algorithm.
The Att-Res2-UNet network demonstrated the highest accuracy and precision, with the most accurate recognition results. The overall accuracy of the model established by us achieved an overall accuracy similar to that of residents (0.9707 ± 0.00984), while slightly lower than that of rhinologists (0.9790 ± 0.00348). SiamMask's segmentation range was consistent with rhinologists, with a 99% compliance rate and a neoplasm probability value ≥ 0.5.
This study successfully established an AI-assisted nasal endoscopic diagnostic system that can preliminarily identify nasal neoplasms from endoscopic images and automatically track them in real time during surgery, enhancing the efficiency of endoscopic diagnosis and surgery.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Cancer</subject><subject>computer-assisted surgery</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>diagnosis</subject><subject>Diagnostic systems</subject><subject>Endoscopy</subject><subject>Hospitals</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Machine learning</subject><subject>Marking and tracking techniques</subject><subject>Medical imaging</subject><subject>nasal cavity</subject><subject>Neoplasms</subject><subject>Nose</subject><subject>Optimization</subject><subject>Patients</subject><subject>Polyps</subject><subject>Surgery</subject><subject>Tracking</subject><subject>Tumors</subject><subject>Video data</subject><issn>2306-5354</issn><issn>2306-5354</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNptUk1v1DAQjRCIVqV_oYrEhUvAH3Fin9BqKe1KVTlQzpZjT7JeEnuxs632xk9n2i1Vi5BG8mjmvad54ymKM0o-cq7Ip85HCIMPAMmHgTJCCcar4phx0lSCi_r1s_yoOM15QxDCmWBN_bY44koKyiQ_Ln5fQ9yOJk-59KGc11Bem2zGcmlu_bwvVw7C7HsPrjTBlTfJ2J-Y3_l5jYVyke6b1iNhFWYYRz9AsFAtcvZ5RuBB7Dy4mG3celt-8WYIMc-Yft8jZHpXvOnNmOH08T0pfnw9v1leVlffLlbLxVVlmeCkYqTlUjYSDOk60TJak7pVVEnVCOFEB9BCj576ltcWCbRtHAByjaKNI5KfFKuDrotmo7fJTybtdTRePxRiGrRBN3YELYCRTjmieCPqxoAURnZEyU6oVrbMoNbng9Z2103gLO4omfGF6MtO8Gs9xFtNcSyGP4gKHx4VUvy1gzzryWeLCzQB4i5rToVq0K8QCH3_D3QTdyngrh5QNcNgiGoOKJtizgn6p2ko0fdHo_9_NEg8e-7lifb3RPgftXzBSA</recordid><startdate>20241225</startdate><enddate>20241225</enddate><creator>Xu, Xiayue</creator><creator>Yun, Boxiang</creator><creator>Zhao, Yumin</creator><creator>Jin, Ling</creator><creator>Zong, Yanning</creator><creator>Yu, Guanzhen</creator><creator>Zhao, Chuanliang</creator><creator>Fan, Kai</creator><creator>Zhang, Xiaolin</creator><creator>Tan, Shiwang</creator><creator>Zhang, Zimu</creator><creator>Wang, Yan</creator><creator>Li, Qingli</creator><creator>Yu, Shaoqing</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>LK8</scope><scope>M7P</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-5063-8801</orcidid><orcidid>https://orcid.org/0000-0003-2208-4535</orcidid></search><sort><creationdate>20241225</creationdate><title>Neoplasms in the Nasal Cavity Identified and Tracked with an Artificial Intelligence-Assisted Nasal Endoscopic Diagnostic System</title><author>Xu, Xiayue ; 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We retrospectively analyzed 1050 video data of nasal endoscopic surgeries involving four types of nasal neoplasms. Using Deep Snake, U-Net, and Att-Res2-UNet, we developed a nasal neoplastic detection network based on endoscopic images. After deep learning, the optimal network was selected as the initialization model and trained to optimize the SiamMask online tracking algorithm.
The Att-Res2-UNet network demonstrated the highest accuracy and precision, with the most accurate recognition results. The overall accuracy of the model established by us achieved an overall accuracy similar to that of residents (0.9707 ± 0.00984), while slightly lower than that of rhinologists (0.9790 ± 0.00348). SiamMask's segmentation range was consistent with rhinologists, with a 99% compliance rate and a neoplasm probability value ≥ 0.5.
This study successfully established an AI-assisted nasal endoscopic diagnostic system that can preliminarily identify nasal neoplasms from endoscopic images and automatically track them in real time during surgery, enhancing the efficiency of endoscopic diagnosis and surgery.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>39851283</pmid><doi>10.3390/bioengineering12010010</doi><orcidid>https://orcid.org/0000-0001-5063-8801</orcidid><orcidid>https://orcid.org/0000-0003-2208-4535</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Algorithms Artificial intelligence Cancer computer-assisted surgery Datasets Deep learning diagnosis Diagnostic systems Endoscopy Hospitals Image processing Image segmentation Machine learning Marking and tracking techniques Medical imaging nasal cavity Neoplasms Nose Optimization Patients Polyps Surgery Tracking Tumors Video data |
| title | Neoplasms in the Nasal Cavity Identified and Tracked with an Artificial Intelligence-Assisted Nasal Endoscopic Diagnostic System |
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