{"title":"[Advances in the application of multimodal image fusion technique in stomatology].","authors":"T Y Ma, N Zhu, Y Zhang","doi":"10.3760/cma.j.cn112144-20250423-00154","DOIUrl":null,"url":null,"abstract":"<p><p>Within the treatment process of modern stomatology, obtaining exquisite preoperative information is the key to accurate intraoperative planning with implementation and prognostic judgment. However, traditional single mode image has obvious shortcomings, such as \"monotonous contents\" and \"unstable measurement accuracy\", which could hardly meet the diversified needs of oral patients. Multimodal medical image fusion (MMIF) technique has been introduced into the studies of stomatology in the 1990s, aiming at realizing personalized patients' data analysis through multiple fusion algorithms, which combines the advantages of multimodal medical images while laying a stable foundation for new treatment technologies. Recently artificial intelligence (AI) has significantly increased the precision and efficiency of MMIF's registration: advanced algorithms and networks have confirmed the great compatibility between AI and MMIF. This article systematically reviews the development history of the multimodal image fusion technique and its current application in stomatology, while analyzing technological progresses within the domain combined with the background of AI's rapid development, in order to provide new ideas for achieving new advancements within the field of stomatology.</p>","PeriodicalId":23965,"journal":{"name":"中华口腔医学杂志","volume":"60 10","pages":"1209-1216"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中华口腔医学杂志","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3760/cma.j.cn112144-20250423-00154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
Within the treatment process of modern stomatology, obtaining exquisite preoperative information is the key to accurate intraoperative planning with implementation and prognostic judgment. However, traditional single mode image has obvious shortcomings, such as "monotonous contents" and "unstable measurement accuracy", which could hardly meet the diversified needs of oral patients. Multimodal medical image fusion (MMIF) technique has been introduced into the studies of stomatology in the 1990s, aiming at realizing personalized patients' data analysis through multiple fusion algorithms, which combines the advantages of multimodal medical images while laying a stable foundation for new treatment technologies. Recently artificial intelligence (AI) has significantly increased the precision and efficiency of MMIF's registration: advanced algorithms and networks have confirmed the great compatibility between AI and MMIF. This article systematically reviews the development history of the multimodal image fusion technique and its current application in stomatology, while analyzing technological progresses within the domain combined with the background of AI's rapid development, in order to provide new ideas for achieving new advancements within the field of stomatology.
期刊介绍:
Founded in August 1953, Chinese Journal of Stomatology is a monthly academic journal of stomatology published publicly at home and abroad, sponsored by the Chinese Medical Association and co-sponsored by the Chinese Stomatology Association. It mainly reports the leading scientific research results and clinical diagnosis and treatment experience in the field of oral medicine, as well as the basic theoretical research that has a guiding role in oral clinical practice and is closely combined with oral clinical practice.
Chinese Journal of Over the years, Stomatology has been published in Medline, Scopus database, Toxicology Abstracts Database, Chemical Abstracts Database, American Cancer database, Russian Abstracts database, China Core Journal of Science and Technology, Peking University Core Journal, CSCD and other more than 20 important journals at home and abroad Physical medicine database and retrieval system included.