Johannes Schwenck, Manfred Kneilling, Niels P Riksen, Christian la Fougère, Douwe J Mulder, Riemer J H A Slart, Erik H J G Aarntzen
{"title":"人工智能在感染和炎症分子成像中的作用。","authors":"Johannes Schwenck, Manfred Kneilling, Niels P Riksen, Christian la Fougère, Douwe J Mulder, Riemer J H A Slart, Erik H J G Aarntzen","doi":"10.1186/s41824-022-00138-1","DOIUrl":null,"url":null,"abstract":"<p><p>The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers' expertise. Although molecular imaging, like [<sup>18</sup>F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.</p>","PeriodicalId":36160,"journal":{"name":"European Journal of Hybrid Imaging","volume":" ","pages":"17"},"PeriodicalIF":1.7000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433558/pdf/","citationCount":"3","resultStr":"{\"title\":\"A role for artificial intelligence in molecular imaging of infection and inflammation.\",\"authors\":\"Johannes Schwenck, Manfred Kneilling, Niels P Riksen, Christian la Fougère, Douwe J Mulder, Riemer J H A Slart, Erik H J G Aarntzen\",\"doi\":\"10.1186/s41824-022-00138-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers' expertise. Although molecular imaging, like [<sup>18</sup>F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.</p>\",\"PeriodicalId\":36160,\"journal\":{\"name\":\"European Journal of Hybrid Imaging\",\"volume\":\" \",\"pages\":\"17\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2022-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433558/pdf/\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Hybrid Imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s41824-022-00138-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Hybrid Imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s41824-022-00138-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
A role for artificial intelligence in molecular imaging of infection and inflammation.
The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers' expertise. Although molecular imaging, like [18F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.