Detection of oral cancer and oral potentially malignant disorders using artificial intelligence‐based image analysis
Background We aimed to construct an artificial intelligence‐based model for detecting oral cancer and dysplastic leukoplakia using oral cavity images captured with a single‐lens reflex camera. Subjects and methods We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OS...
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| Veröffentlicht in: | Head & neck 2024-09, Vol.46 (9), p.2253-2260 |
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| creator | Kouketsu, Atsumu Doi, Chiaki Tanaka, Hiroaki Araki, Takashi Nakayama, Rina Toyooka, Tsuguyoshi Hiyama, Satoshi Iikubo, Masahiro Osaka, Ken Sasaki, Keiichi Nagai, Hirokazu Sugiura, Tsuyoshi Yamauchi, Kensuke Kuroda, Kanako Yanagisawa, Yuta Miyashita, Hitoshi Kajita, Tomonari Iwama, Ryosuke Kurobane, Tsuyoshi Takahashi, Tetsu |
| description | Background
We aimed to construct an artificial intelligence‐based model for detecting oral cancer and dysplastic leukoplakia using oral cavity images captured with a single‐lens reflex camera.
Subjects and methods
We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OSCC), leukoplakia, and other oral mucosal diseases. An object detection model was constructed using a Single Shot Multibox Detector to detect oral diseases and their locations using images. The model was trained using 523 images of oral cancer, and its performance was evaluated using images of oral cancer (n = 66), leukoplakia (n = 49), and other oral diseases (n = 405).
Results
For the detection of only OSCC versus OSCC and leukoplakia, the model demonstrated a sensitivity of 93.9% versus 83.7%, a negative predictive value of 98.8% versus 94.5%, and a specificity of 81.2% versus 81.2%.
Conclusions
Our proposed model is a potential diagnostic tool for oral diseases. |
| doi_str_mv | 10.1002/hed.27843 |
| format | Article |
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We aimed to construct an artificial intelligence‐based model for detecting oral cancer and dysplastic leukoplakia using oral cavity images captured with a single‐lens reflex camera.
Subjects and methods
We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OSCC), leukoplakia, and other oral mucosal diseases. An object detection model was constructed using a Single Shot Multibox Detector to detect oral diseases and their locations using images. The model was trained using 523 images of oral cancer, and its performance was evaluated using images of oral cancer (n = 66), leukoplakia (n = 49), and other oral diseases (n = 405).
Results
For the detection of only OSCC versus OSCC and leukoplakia, the model demonstrated a sensitivity of 93.9% versus 83.7%, a negative predictive value of 98.8% versus 94.5%, and a specificity of 81.2% versus 81.2%.
Conclusions
Our proposed model is a potential diagnostic tool for oral diseases.</description><identifier>ISSN: 1043-3074</identifier><identifier>ISSN: 1097-0347</identifier><identifier>EISSN: 1097-0347</identifier><identifier>DOI: 10.1002/hed.27843</identifier><identifier>PMID: 38860703</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Artificial intelligence ; deep learning ; Head & neck cancer ; Image processing ; Leukokeratosis ; Oral cancer ; Oral carcinoma ; Oral cavity ; Oral diseases ; Oral squamous cell carcinoma ; Squamous cell carcinoma</subject><ispartof>Head & neck, 2024-09, Vol.46 (9), p.2253-2260</ispartof><rights>2024 The Author(s). published by Wiley Periodicals LLC.</rights><rights>2024 The Author(s). Head & Neck published by Wiley Periodicals LLC.</rights><rights>2024. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2783-98f5c939d4d36b7b39007608392d47a8eb03dd7e4fc3ee0f9fd0142fa70c659a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fhed.27843$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fhed.27843$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1416,27923,27924,45573,45574</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38860703$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kouketsu, Atsumu</creatorcontrib><creatorcontrib>Doi, Chiaki</creatorcontrib><creatorcontrib>Tanaka, Hiroaki</creatorcontrib><creatorcontrib>Araki, Takashi</creatorcontrib><creatorcontrib>Nakayama, Rina</creatorcontrib><creatorcontrib>Toyooka, Tsuguyoshi</creatorcontrib><creatorcontrib>Hiyama, Satoshi</creatorcontrib><creatorcontrib>Iikubo, Masahiro</creatorcontrib><creatorcontrib>Osaka, Ken</creatorcontrib><creatorcontrib>Sasaki, Keiichi</creatorcontrib><creatorcontrib>Nagai, Hirokazu</creatorcontrib><creatorcontrib>Sugiura, Tsuyoshi</creatorcontrib><creatorcontrib>Yamauchi, Kensuke</creatorcontrib><creatorcontrib>Kuroda, Kanako</creatorcontrib><creatorcontrib>Yanagisawa, Yuta</creatorcontrib><creatorcontrib>Miyashita, Hitoshi</creatorcontrib><creatorcontrib>Kajita, Tomonari</creatorcontrib><creatorcontrib>Iwama, Ryosuke</creatorcontrib><creatorcontrib>Kurobane, Tsuyoshi</creatorcontrib><creatorcontrib>Takahashi, Tetsu</creatorcontrib><title>Detection of oral cancer and oral potentially malignant disorders using artificial intelligence‐based image analysis</title><title>Head & neck</title><addtitle>Head Neck</addtitle><description>Background
We aimed to construct an artificial intelligence‐based model for detecting oral cancer and dysplastic leukoplakia using oral cavity images captured with a single‐lens reflex camera.
Subjects and methods
We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OSCC), leukoplakia, and other oral mucosal diseases. An object detection model was constructed using a Single Shot Multibox Detector to detect oral diseases and their locations using images. The model was trained using 523 images of oral cancer, and its performance was evaluated using images of oral cancer (n = 66), leukoplakia (n = 49), and other oral diseases (n = 405).
Results
For the detection of only OSCC versus OSCC and leukoplakia, the model demonstrated a sensitivity of 93.9% versus 83.7%, a negative predictive value of 98.8% versus 94.5%, and a specificity of 81.2% versus 81.2%.
Conclusions
Our proposed model is a potential diagnostic tool for oral diseases.</description><subject>Artificial intelligence</subject><subject>deep learning</subject><subject>Head & neck cancer</subject><subject>Image processing</subject><subject>Leukokeratosis</subject><subject>Oral cancer</subject><subject>Oral carcinoma</subject><subject>Oral cavity</subject><subject>Oral diseases</subject><subject>Oral squamous cell carcinoma</subject><subject>Squamous cell carcinoma</subject><issn>1043-3074</issn><issn>1097-0347</issn><issn>1097-0347</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp1kcFu1DAQhi0EoqVw4AWQJS5wSDvJuLF9RG2hSJW4wDly7PHiKmsvdgLaG4_QZ-yT4CXtBYmTx6NPn2bmZ-x1C6ctQHf2ndxpJ5XAJ-y4BS0bQCGfHmqBDYIUR-xFKbcAgL3onrMjVKoHCXjMfl7STHYOKfLkecpm4tZES5mb6Nb_Ls0U52Cmac-3ZgqbaOLMXSgpO8qFLyXEDTd5Dj7YivEQZ5oqR9Vz__tuNIUcD1uzoSo1076E8pI982Yq9OrhPWHfPl59vbhubr58-nzx4aaxdR9stPLnVqN2wmE_yhE1gOxBoe6ckEbRCOicJOEtEoHX3kErOm8k2P5cGzxh71bvLqcfC5V52IZi63QmUlrKgND3UgO2WNG3_6C3acl13gOltGp7BbJS71fK5lRKJj_scl0t74cWhkMYQw1j-BtGZd88GJdxW7uP5OP1K3C2Ar_CRPv_m4brq8tV-Qd6PZV7</recordid><startdate>202409</startdate><enddate>202409</enddate><creator>Kouketsu, Atsumu</creator><creator>Doi, Chiaki</creator><creator>Tanaka, Hiroaki</creator><creator>Araki, Takashi</creator><creator>Nakayama, Rina</creator><creator>Toyooka, Tsuguyoshi</creator><creator>Hiyama, Satoshi</creator><creator>Iikubo, Masahiro</creator><creator>Osaka, Ken</creator><creator>Sasaki, Keiichi</creator><creator>Nagai, Hirokazu</creator><creator>Sugiura, Tsuyoshi</creator><creator>Yamauchi, Kensuke</creator><creator>Kuroda, Kanako</creator><creator>Yanagisawa, Yuta</creator><creator>Miyashita, Hitoshi</creator><creator>Kajita, Tomonari</creator><creator>Iwama, Ryosuke</creator><creator>Kurobane, Tsuyoshi</creator><creator>Takahashi, Tetsu</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>WIN</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QP</scope><scope>7TK</scope><scope>K9.</scope><scope>7X8</scope></search><sort><creationdate>202409</creationdate><title>Detection of oral cancer and oral potentially malignant disorders using artificial intelligence‐based image analysis</title><author>Kouketsu, Atsumu ; Doi, Chiaki ; Tanaka, Hiroaki ; Araki, Takashi ; Nakayama, Rina ; Toyooka, Tsuguyoshi ; Hiyama, Satoshi ; Iikubo, Masahiro ; Osaka, Ken ; Sasaki, Keiichi ; Nagai, Hirokazu ; Sugiura, Tsuyoshi ; Yamauchi, Kensuke ; Kuroda, Kanako ; Yanagisawa, Yuta ; Miyashita, Hitoshi ; Kajita, Tomonari ; Iwama, Ryosuke ; Kurobane, Tsuyoshi ; Takahashi, Tetsu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2783-98f5c939d4d36b7b39007608392d47a8eb03dd7e4fc3ee0f9fd0142fa70c659a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Artificial intelligence</topic><topic>deep learning</topic><topic>Head & neck cancer</topic><topic>Image processing</topic><topic>Leukokeratosis</topic><topic>Oral cancer</topic><topic>Oral carcinoma</topic><topic>Oral cavity</topic><topic>Oral diseases</topic><topic>Oral squamous cell carcinoma</topic><topic>Squamous cell carcinoma</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kouketsu, Atsumu</creatorcontrib><creatorcontrib>Doi, Chiaki</creatorcontrib><creatorcontrib>Tanaka, Hiroaki</creatorcontrib><creatorcontrib>Araki, Takashi</creatorcontrib><creatorcontrib>Nakayama, Rina</creatorcontrib><creatorcontrib>Toyooka, Tsuguyoshi</creatorcontrib><creatorcontrib>Hiyama, Satoshi</creatorcontrib><creatorcontrib>Iikubo, Masahiro</creatorcontrib><creatorcontrib>Osaka, Ken</creatorcontrib><creatorcontrib>Sasaki, Keiichi</creatorcontrib><creatorcontrib>Nagai, Hirokazu</creatorcontrib><creatorcontrib>Sugiura, Tsuyoshi</creatorcontrib><creatorcontrib>Yamauchi, Kensuke</creatorcontrib><creatorcontrib>Kuroda, Kanako</creatorcontrib><creatorcontrib>Yanagisawa, Yuta</creatorcontrib><creatorcontrib>Miyashita, Hitoshi</creatorcontrib><creatorcontrib>Kajita, Tomonari</creatorcontrib><creatorcontrib>Iwama, Ryosuke</creatorcontrib><creatorcontrib>Kurobane, Tsuyoshi</creatorcontrib><creatorcontrib>Takahashi, Tetsu</creatorcontrib><collection>Wiley-Blackwell Open Access Titles</collection><collection>Wiley Free Content</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Head & neck</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kouketsu, Atsumu</au><au>Doi, Chiaki</au><au>Tanaka, Hiroaki</au><au>Araki, Takashi</au><au>Nakayama, Rina</au><au>Toyooka, Tsuguyoshi</au><au>Hiyama, Satoshi</au><au>Iikubo, Masahiro</au><au>Osaka, Ken</au><au>Sasaki, Keiichi</au><au>Nagai, Hirokazu</au><au>Sugiura, Tsuyoshi</au><au>Yamauchi, Kensuke</au><au>Kuroda, Kanako</au><au>Yanagisawa, Yuta</au><au>Miyashita, Hitoshi</au><au>Kajita, Tomonari</au><au>Iwama, Ryosuke</au><au>Kurobane, Tsuyoshi</au><au>Takahashi, Tetsu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detection of oral cancer and oral potentially malignant disorders using artificial intelligence‐based image analysis</atitle><jtitle>Head & neck</jtitle><addtitle>Head Neck</addtitle><date>2024-09</date><risdate>2024</risdate><volume>46</volume><issue>9</issue><spage>2253</spage><epage>2260</epage><pages>2253-2260</pages><issn>1043-3074</issn><issn>1097-0347</issn><eissn>1097-0347</eissn><abstract>Background
We aimed to construct an artificial intelligence‐based model for detecting oral cancer and dysplastic leukoplakia using oral cavity images captured with a single‐lens reflex camera.
Subjects and methods
We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OSCC), leukoplakia, and other oral mucosal diseases. An object detection model was constructed using a Single Shot Multibox Detector to detect oral diseases and their locations using images. The model was trained using 523 images of oral cancer, and its performance was evaluated using images of oral cancer (n = 66), leukoplakia (n = 49), and other oral diseases (n = 405).
Results
For the detection of only OSCC versus OSCC and leukoplakia, the model demonstrated a sensitivity of 93.9% versus 83.7%, a negative predictive value of 98.8% versus 94.5%, and a specificity of 81.2% versus 81.2%.
Conclusions
Our proposed model is a potential diagnostic tool for oral diseases.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>38860703</pmid><doi>10.1002/hed.27843</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| source | Wiley Online Library All Journals |
| subjects | Artificial intelligence deep learning Head & neck cancer Image processing Leukokeratosis Oral cancer Oral carcinoma Oral cavity Oral diseases Oral squamous cell carcinoma Squamous cell carcinoma |
| title | Detection of oral cancer and oral potentially malignant disorders using artificial intelligence‐based image analysis |
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