Artificial intelligence for nuclear medicine in oncology

被引:0
作者
Kenji Hirata
Hiroyuki Sugimori
Noriyuki Fujima
Takuya Toyonaga
Kohsuke Kudo
机构
[1] Hokkaido University Graduate School of Medicine,Department of Diagnostic Imaging
[2] Hokkaido University Hospital,Department of Nuclear Medicine
[3] Hokkaido University Graduate School of Medicine,Division of Medical AI Education and Research
[4] Hokkaido University,Faculty of Health Sciences
[5] Hokkaido University Hospital,Department of Diagnostic and Interventional Radiology
[6] Yale University,Department of Radiology and Biomedical Imaging
[7] Hokkaido University Faculty of Medicine,Global Center for Biomedical Science and Engineering
来源
Annals of Nuclear Medicine | 2022年 / 36卷
关键词
Artificial intelligence; Nuclear medicine; Oncology; Deep learning; Radiomics;
D O I
暂无
中图分类号
学科分类号
摘要
As in all other medical fields, artificial intelligence (AI) is increasingly being used in nuclear medicine for oncology. There are many articles that discuss AI from the viewpoint of nuclear medicine, but few focus on nuclear medicine from the viewpoint of AI. Nuclear medicine images are characterized by their low spatial resolution and high quantitativeness. It is noted that AI has been used since before the emergence of deep learning. AI can be divided into three categories by its purpose: (1) assisted interpretation, i.e., computer-aided detection (CADe) or computer-aided diagnosis (CADx). (2) Additional insight, i.e., AI provides information beyond the radiologist's eye, such as predicting genes and prognosis from images. It is also related to the field called radiomics/radiogenomics. (3) Augmented image, i.e., image generation tasks. To apply AI to practical use, harmonization between facilities and the possibility of black box explanations need to be resolved.
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页码:123 / 132
页数:9
相关论文
共 486 条
  • [1] LeCun Y(2015)Deep learning Nature 521 436-444
  • [2] Bengio Y(2021)Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation study BMC Cancer 21 1120-412
  • [3] Hinton G(2021)Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer inpatients who underwent upper gastrointestinal endoscopy Endoscopy 32 406-469
  • [4] Ueda D(2021)Automated deep learning in ophthalmology: AI that can build AI Curr Opin Ophthalmol 12 5976-1540
  • [5] Yamamoto A(2021)Genome-wide detection of cytosine methylations in plant from nanopore data using deep learning Nat Commun 22 485-673
  • [6] Shimazaki A(2021)Protein-protein interaction prediction based on ordinal regression and recurrent convolutional neural networks BMC Bioinform 16 449-211
  • [7] Walston SL(2021)Machine learning in gastrointestinal surgery Surg Today. 26 1533-79
  • [8] Matsumoto T(2021)A brief history of AI: how to prevent another winter (a critical review) PET Clin 18 501-210
  • [9] Izumi N(2021)Prognostic impact of bone metastatic volume beyond vertebrae and pelvis in patients with metastatic hormone-sensitive prostate cancer Int J Clin Oncol 24 668-1153
  • [10] Tsukioka T(2018)Prognostic value of an automated bone scan index for men with metastatic castration-resistant prostate cancer treated with cabazitaxel BMC Cancer 5 1095-55