{"title":"人工智能在腹部成像中不断发展的新应用。","authors":"Mark R Loper, Mina S Makary","doi":"10.3390/tomography10110133","DOIUrl":null,"url":null,"abstract":"<p><p>Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of AI in abdominal imaging, with a focus on recent literature contributions. This work explores the diagnosis and characterization of hepatobiliary, pancreatic, gastric, colonic, and other pathologies. In addition, the role of AI has been observed to help differentiate renal, adrenal, and splenic disorders. Furthermore, workflow optimization strategies and quantitative imaging techniques used for the measurement and characterization of tissue properties, including radiomics and deep learning, are highlighted. An assessment of how these advancements enable more precise diagnosis, tumor description, and body composition evaluation is presented, which ultimately advances the clinical effectiveness and productivity of radiology. Despite the advancements of AI in abdominal imaging, technical, ethical, and legal challenges persist, and these challenges, as well as opportunities for future development, are highlighted.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"10 11","pages":"1814-1831"},"PeriodicalIF":2.2000,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11598375/pdf/","citationCount":"0","resultStr":"{\"title\":\"Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.\",\"authors\":\"Mark R Loper, Mina S Makary\",\"doi\":\"10.3390/tomography10110133\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of AI in abdominal imaging, with a focus on recent literature contributions. This work explores the diagnosis and characterization of hepatobiliary, pancreatic, gastric, colonic, and other pathologies. In addition, the role of AI has been observed to help differentiate renal, adrenal, and splenic disorders. Furthermore, workflow optimization strategies and quantitative imaging techniques used for the measurement and characterization of tissue properties, including radiomics and deep learning, are highlighted. An assessment of how these advancements enable more precise diagnosis, tumor description, and body composition evaluation is presented, which ultimately advances the clinical effectiveness and productivity of radiology. Despite the advancements of AI in abdominal imaging, technical, ethical, and legal challenges persist, and these challenges, as well as opportunities for future development, are highlighted.</p>\",\"PeriodicalId\":51330,\"journal\":{\"name\":\"Tomography\",\"volume\":\"10 11\",\"pages\":\"1814-1831\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11598375/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tomography\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3390/tomography10110133\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tomography","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/tomography10110133","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.
Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of AI in abdominal imaging, with a focus on recent literature contributions. This work explores the diagnosis and characterization of hepatobiliary, pancreatic, gastric, colonic, and other pathologies. In addition, the role of AI has been observed to help differentiate renal, adrenal, and splenic disorders. Furthermore, workflow optimization strategies and quantitative imaging techniques used for the measurement and characterization of tissue properties, including radiomics and deep learning, are highlighted. An assessment of how these advancements enable more precise diagnosis, tumor description, and body composition evaluation is presented, which ultimately advances the clinical effectiveness and productivity of radiology. Despite the advancements of AI in abdominal imaging, technical, ethical, and legal challenges persist, and these challenges, as well as opportunities for future development, are highlighted.
TomographyMedicine-Radiology, Nuclear Medicine and Imaging
CiteScore
2.70
自引率
10.50%
发文量
222
期刊介绍：
TomographyTM publishes basic (technical and pre-clinical) and clinical scientific articles which involve the advancement of imaging technologies. Tomography encompasses studies that use single or multiple imaging modalities including for example CT, US, PET, SPECT, MR and hyperpolarization technologies, as well as optical modalities (i.e. bioluminescence, photoacoustic, endomicroscopy, fiber optic imaging and optical computed tomography) in basic sciences, engineering, preclinical and clinical medicine.
Tomography also welcomes studies involving exploration and refinement of contrast mechanisms and image-derived metrics within and across modalities toward the development of novel imaging probes for image-based feedback and intervention. The use of imaging in biology and medicine provides unparalleled opportunities to noninvasively interrogate tissues to obtain real-time dynamic and quantitative information required for diagnosis and response to interventions and to follow evolving pathological conditions. As multi-modal studies and the complexities of imaging technologies themselves are ever increasing to provide advanced information to scientists and clinicians.
Tomography provides a unique publication venue allowing investigators the opportunity to more precisely communicate integrated findings related to the diverse and heterogeneous features associated with underlying anatomical, physiological, functional, metabolic and molecular genetic activities of normal and diseased tissue. Thus Tomography publishes peer-reviewed articles which involve the broad use of imaging of any tissue and disease type including both preclinical and clinical investigations. In addition, hardware/software along with chemical and molecular probe advances are welcome as they are deemed to significantly contribute towards the long-term goal of improving the overall impact of imaging on scientific and clinical discovery.
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