Cone Beam Computerized Tomography Preoperative Guidance under Feature Point Extraction Algorithm Combined with Autologous Concentrated Growth Factors in Maxillary Dental Implantation with Insufficient Bone Mass
It was to assess the efficacy of preoperative guidance using cone beam computed tomography (CBCT) and the application of autologous concentrated growth factors (CGF) for implantation in maxillary teeth with inadequate bone mass. An eigenvector-based multimodal elasticity algorithm was developed. Eig...
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| Veröffentlicht in: | Applied artificial intelligence 2024-12, Vol.38 (1) |
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| container_title | Applied artificial intelligence |
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| creator | Liu, Fang Cui, Wenjing Wang, Tingting Qian, Cheng Yang, Ren Zhang, Rongxiu Xu, Li Hu, Jie Liu, Liang Zhang, Kai |
| description | It was to assess the efficacy of preoperative guidance using cone beam computed tomography (CBCT) and the application of autologous concentrated growth factors (CGF) for implantation in maxillary teeth with inadequate bone mass.
An eigenvector-based multimodal elasticity algorithm was developed. Eighty patients with insufficient bone mass in the maxillary dental region were rolled into control group (n = 40, Bio-Oss bone powder treatment) and observation group (n = 40, autologous CGF and Bio-Oss bone powder treatment). The scale-invariant feature transform (SIFT) algorithm and the hierarchical attribute matching mechanism for elastic registration (HAMMER) were introduced, whose registration time and mutual information value were compared. Therapeutic outcomes of patients were also compared.
The eigenvector-based multimodal elastic algorithm exhibited a significantly shorter registration time versus SIFT and HAMMER algorithms, and superior accuracy, sensitivity, and specificity (p |
| doi_str_mv | 10.1080/08839514.2024.2335415 |
| format | Article |
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An eigenvector-based multimodal elasticity algorithm was developed. Eighty patients with insufficient bone mass in the maxillary dental region were rolled into control group (n = 40, Bio-Oss bone powder treatment) and observation group (n = 40, autologous CGF and Bio-Oss bone powder treatment). The scale-invariant feature transform (SIFT) algorithm and the hierarchical attribute matching mechanism for elastic registration (HAMMER) were introduced, whose registration time and mutual information value were compared. Therapeutic outcomes of patients were also compared.
The eigenvector-based multimodal elastic algorithm exhibited a significantly shorter registration time versus SIFT and HAMMER algorithms, and superior accuracy, sensitivity, and specificity (p < .05). Observation group demonstrated greater alveolar ridge elevation and bone augmentation versus control group, implying superior healing outcomes.
The feature point extraction algorithm is applicable for CBCT image processing in patients with insufficient maxillary bone mass. Autologous CGF exhibited superior therapeutic efficacy.</description><identifier>ISSN: 0883-9514</identifier><identifier>EISSN: 1087-6545</identifier><identifier>DOI: 10.1080/08839514.2024.2335415</identifier><language>eng</language><publisher>Philadelphia: Taylor & Francis</publisher><subject>Algorithms ; Computed tomography ; Dental materials ; Effectiveness ; Eigenvectors ; Growth factors ; Hammers ; Image processing ; Implantation ; Registration ; Tomography</subject><ispartof>Applied artificial intelligence, 2024-12, Vol.38 (1)</ispartof><rights>2024 The Author(s). Published with license by Taylor & Francis Group, LLC. 2024</rights><rights>2024 The Author(s). Published with license by Taylor & Francis Group, LLC. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/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-c399t-3e8d2dc8bae788e920849deb67a3de9599fe715d4c87853b0900eb9ff738a46c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.tandfonline.com/doi/pdf/10.1080/08839514.2024.2335415$$EPDF$$P50$$Ginformaworld$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.tandfonline.com/doi/full/10.1080/08839514.2024.2335415$$EHTML$$P50$$Ginformaworld$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,27479,27901,27902,59116,59117</link.rule.ids></links><search><creatorcontrib>Liu, Fang</creatorcontrib><creatorcontrib>Cui, Wenjing</creatorcontrib><creatorcontrib>Wang, Tingting</creatorcontrib><creatorcontrib>Qian, Cheng</creatorcontrib><creatorcontrib>Yang, Ren</creatorcontrib><creatorcontrib>Zhang, Rongxiu</creatorcontrib><creatorcontrib>Xu, Li</creatorcontrib><creatorcontrib>Hu, Jie</creatorcontrib><creatorcontrib>Liu, Liang</creatorcontrib><creatorcontrib>Zhang, Kai</creatorcontrib><title>Cone Beam Computerized Tomography Preoperative Guidance under Feature Point Extraction Algorithm Combined with Autologous Concentrated Growth Factors in Maxillary Dental Implantation with Insufficient Bone Mass</title><title>Applied artificial intelligence</title><description>It was to assess the efficacy of preoperative guidance using cone beam computed tomography (CBCT) and the application of autologous concentrated growth factors (CGF) for implantation in maxillary teeth with inadequate bone mass.
An eigenvector-based multimodal elasticity algorithm was developed. Eighty patients with insufficient bone mass in the maxillary dental region were rolled into control group (n = 40, Bio-Oss bone powder treatment) and observation group (n = 40, autologous CGF and Bio-Oss bone powder treatment). The scale-invariant feature transform (SIFT) algorithm and the hierarchical attribute matching mechanism for elastic registration (HAMMER) were introduced, whose registration time and mutual information value were compared. Therapeutic outcomes of patients were also compared.
The eigenvector-based multimodal elastic algorithm exhibited a significantly shorter registration time versus SIFT and HAMMER algorithms, and superior accuracy, sensitivity, and specificity (p < .05). Observation group demonstrated greater alveolar ridge elevation and bone augmentation versus control group, implying superior healing outcomes.
The feature point extraction algorithm is applicable for CBCT image processing in patients with insufficient maxillary bone mass. Autologous CGF exhibited superior therapeutic efficacy.</description><subject>Algorithms</subject><subject>Computed tomography</subject><subject>Dental materials</subject><subject>Effectiveness</subject><subject>Eigenvectors</subject><subject>Growth factors</subject><subject>Hammers</subject><subject>Image processing</subject><subject>Implantation</subject><subject>Registration</subject><subject>Tomography</subject><issn>0883-9514</issn><issn>1087-6545</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>0YH</sourceid><sourceid>DOA</sourceid><recordid>eNp9ks9uEzEQxlcIJELhEZAscU6x43XWvpGGJkRqRQ_lbHm949TR7noZe5uWx-SJ8CaFIxf_m-_7zVgzRfGR0UtGJf1MpeRKsPJyQRd54VyUTLwqZjlYzZeiFK-L2aSZT6K3xbsYD5RSVlVsVvxehx7IFZiOrEM3jAnQ_4KG3Icu7NEMD8_kDiEMgCb5RyDb0Temt0DGvgEkGzBpRCB3wfeJXD8lNDb50JNVuw_o08MJW_s-I4_5SlZjCm3YhzHmQOb02ZFycIvhmMObbA8Yie_JrXnybWvwmXzNKtOSXTe0Jp9O_BNs18fROW99FpCr6SO3Jsb3xRtn2ggfXvaL4sfm-n79bX7zfbtbr27mliuV5hxks2isrA1UUoJaUFmqBuplZXgDSijloGKiKa2spOA1VZRCrZyruDTl0vKLYnfmNsEc9IC-y8XqYLw-PQTca4PJ2xZ0rXKfHChWS1vy2tXGCiZEzkmlcY5n1qcza8Dwc4SY9CGM2OfyNWel4JJWSmaVOKsshhgR3L-sjOppFPTfUdDTKOiXUci-L2ef713AzhwDto1O5rkN6DC3009p_ov4AxmHwGM</recordid><startdate>20241231</startdate><enddate>20241231</enddate><creator>Liu, Fang</creator><creator>Cui, Wenjing</creator><creator>Wang, Tingting</creator><creator>Qian, Cheng</creator><creator>Yang, Ren</creator><creator>Zhang, Rongxiu</creator><creator>Xu, Li</creator><creator>Hu, Jie</creator><creator>Liu, Liang</creator><creator>Zhang, Kai</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><general>Taylor & Francis Group</general><scope>0YH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope></search><sort><creationdate>20241231</creationdate><title>Cone Beam Computerized Tomography Preoperative Guidance under Feature Point Extraction Algorithm Combined with Autologous Concentrated Growth Factors in Maxillary Dental Implantation with Insufficient Bone Mass</title><author>Liu, Fang ; Cui, Wenjing ; Wang, Tingting ; Qian, Cheng ; Yang, Ren ; Zhang, Rongxiu ; Xu, Li ; Hu, Jie ; Liu, Liang ; Zhang, Kai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c399t-3e8d2dc8bae788e920849deb67a3de9599fe715d4c87853b0900eb9ff738a46c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Computed tomography</topic><topic>Dental materials</topic><topic>Effectiveness</topic><topic>Eigenvectors</topic><topic>Growth factors</topic><topic>Hammers</topic><topic>Image processing</topic><topic>Implantation</topic><topic>Registration</topic><topic>Tomography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Fang</creatorcontrib><creatorcontrib>Cui, Wenjing</creatorcontrib><creatorcontrib>Wang, Tingting</creatorcontrib><creatorcontrib>Qian, Cheng</creatorcontrib><creatorcontrib>Yang, Ren</creatorcontrib><creatorcontrib>Zhang, Rongxiu</creatorcontrib><creatorcontrib>Xu, Li</creatorcontrib><creatorcontrib>Hu, Jie</creatorcontrib><creatorcontrib>Liu, Liang</creatorcontrib><creatorcontrib>Zhang, Kai</creatorcontrib><collection>Taylor & Francis Open Access</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Applied artificial intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Fang</au><au>Cui, Wenjing</au><au>Wang, Tingting</au><au>Qian, Cheng</au><au>Yang, Ren</au><au>Zhang, Rongxiu</au><au>Xu, Li</au><au>Hu, Jie</au><au>Liu, Liang</au><au>Zhang, Kai</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cone Beam Computerized Tomography Preoperative Guidance under Feature Point Extraction Algorithm Combined with Autologous Concentrated Growth Factors in Maxillary Dental Implantation with Insufficient Bone Mass</atitle><jtitle>Applied artificial intelligence</jtitle><date>2024-12-31</date><risdate>2024</risdate><volume>38</volume><issue>1</issue><issn>0883-9514</issn><eissn>1087-6545</eissn><abstract>It was to assess the efficacy of preoperative guidance using cone beam computed tomography (CBCT) and the application of autologous concentrated growth factors (CGF) for implantation in maxillary teeth with inadequate bone mass.
An eigenvector-based multimodal elasticity algorithm was developed. Eighty patients with insufficient bone mass in the maxillary dental region were rolled into control group (n = 40, Bio-Oss bone powder treatment) and observation group (n = 40, autologous CGF and Bio-Oss bone powder treatment). The scale-invariant feature transform (SIFT) algorithm and the hierarchical attribute matching mechanism for elastic registration (HAMMER) were introduced, whose registration time and mutual information value were compared. Therapeutic outcomes of patients were also compared.
The eigenvector-based multimodal elastic algorithm exhibited a significantly shorter registration time versus SIFT and HAMMER algorithms, and superior accuracy, sensitivity, and specificity (p < .05). Observation group demonstrated greater alveolar ridge elevation and bone augmentation versus control group, implying superior healing outcomes.
The feature point extraction algorithm is applicable for CBCT image processing in patients with insufficient maxillary bone mass. Autologous CGF exhibited superior therapeutic efficacy.</abstract><cop>Philadelphia</cop><pub>Taylor & Francis</pub><doi>10.1080/08839514.2024.2335415</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Computed tomography Dental materials Effectiveness Eigenvectors Growth factors Hammers Image processing Implantation Registration Tomography |
| title | Cone Beam Computerized Tomography Preoperative Guidance under Feature Point Extraction Algorithm Combined with Autologous Concentrated Growth Factors in Maxillary Dental Implantation with Insufficient Bone Mass |
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