{"title":"Diagnostic Efficiency of an Artificial Intelligence-Based Technology in Dental Radiography.","authors":"A A Obrubov, E A Solovykh, A G Nadtochiy","doi":"10.1007/s10517-025-06420-z","DOIUrl":null,"url":null,"abstract":"<p><p>We present results of the development of Dentomo artificial intelligence model based on two neural networks. The model includes a database and a knowledge base harmonized with SNOMED CT that allows processing and interpreting the results of cone beam computed tomography (CBCT) scans of the dental system, in particular, identifying and classifying teeth, identifying CT signs of pathology and previous treatments. Based on these data, artificial intelligence can draw conclusions and generate medical reports, systematize the data, and learn from the results. The diagnostic effectiveness of Dentomo was evaluated. The first results of the study have demonstrated that the model based on neural networks and artificial intelligence is a valuable tool for analyzing CBCT scans in clinical practice and optimizing the dentist workflow.</p>","PeriodicalId":9331,"journal":{"name":"Bulletin of Experimental Biology and Medicine","volume":" ","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of Experimental Biology and Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10517-025-06420-z","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
We present results of the development of Dentomo artificial intelligence model based on two neural networks. The model includes a database and a knowledge base harmonized with SNOMED CT that allows processing and interpreting the results of cone beam computed tomography (CBCT) scans of the dental system, in particular, identifying and classifying teeth, identifying CT signs of pathology and previous treatments. Based on these data, artificial intelligence can draw conclusions and generate medical reports, systematize the data, and learn from the results. The diagnostic effectiveness of Dentomo was evaluated. The first results of the study have demonstrated that the model based on neural networks and artificial intelligence is a valuable tool for analyzing CBCT scans in clinical practice and optimizing the dentist workflow.
期刊介绍：
Bulletin of Experimental Biology and Medicine presents original peer reviewed research papers and brief reports on priority new research results in physiology, biochemistry, biophysics, pharmacology, immunology, microbiology, genetics, oncology, etc. Novel trends in science are covered in new sections of the journal - Biogerontology and Human Ecology - that first appeared in 2005.
World scientific interest in stem cells prompted inclusion into Bulletin of Experimental Biology and Medicine a quarterly scientific journal Cell Technologies in Biology and Medicine (a new Russian Academy of Medical Sciences publication since 2005). It publishes only original papers from the leading research institutions on molecular biology of stem and progenitor cells, stem cell as the basis of gene therapy, molecular language of cell-to-cell communication, cytokines, chemokines, growth and other factors, pilot projects on clinical use of stem and progenitor cells.
The Russian Volume Year is published in English from April.
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