{"id":954,"date":"2022-12-22T02:58:57","date_gmt":"2022-12-22T02:58:57","guid":{"rendered":"https:\/\/ds27i1.cc.yamaguchi-u.ac.jp\/~medphys\/?page_id=954"},"modified":"2023-04-19T08:41:57","modified_gmt":"2023-04-19T08:41:57","slug":"aimedicine","status":"publish","type":"page","link":"https:\/\/ds27i1.cc.yamaguchi-u.ac.jp\/~medphys\/en\/research\/aimedicine\/","title":{"rendered":"Application of Artificial Intelligence to Radiation Medicine"},"content":{"rendered":"\n

Imaging Biopsy using AI<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n

Tumors are heterogeneous and have a variety of characteristics. Medical imaging can obtain information about the entire tumor in a minimally invasive manner. In medical imaging, the entire tumor appears to be uniform in density, but statistical analysis of image features can extract information that is invisible to the human eye. By using this information, we are developing a mathematical model that can detect the characteristics of tumors and select the most effective treatment.<\/p>\n\n\n\n\n\n\n\n

Development of automated radiotherapy planning by AI<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n

Radiotherapy involves CT imaging, which is used to create a treatment plan that optimizes the beam direction and dose by outlining the target to be irradiated and the normal organs that are not to be irradiated. Currently, radiation oncologists and medical physicists spend a great deal of time on treatment planning. We are developing an AI-based automated radiation therapy planning system to make this process as efficient as possible.<\/p>\n\n\n\n

Development of AI-based estimation method for pulmonary and hepatic blood flow imaging<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n

The current CT images can obtain anatomical information such as the shape and morphology of organs, but it is difficult to obtain physiological and functional information. However, CT systems used in conventional diagnostics can only acquire images with one energy. We are developing an algorithm to obtain functional information about organs by using AI to generate images with two different energies.<\/p>\n\n\n\n\n\n\n\n

Works<\/h3>\n\n\n\n
    \n
  1. \u533b\u5b66\u7269\u7406\u306e\u8996\u70b9\u304b\u3089\u898b\u308bPrecision Medicine\u306b\u5411\u3051\u305f\u653e\u5c04\u7dda\u6cbb\u7642
    \u690e\u6728 \u5065\u88d5\uff0c \u7530\u4e2d \u79c0\u548c\uff0eRadFan2020\u5e7412\u6708\u53f7\uff0e<\/li>\n\n\n\n
  2. Prediction of EGFR Mutations, Subtypes, and Uncommon Mutations in Lung Adenocarcinoma Based On Machine Learning.
    Kawazoe Y, Shiinoki T, Fujimoto K, Yuasa Y, Hirano T, Matsunaga K, Tanaka H. AAPM 2021, 63rd Annual Meeting.<\/li>\n\n\n\n
  3. Predicting PD-L1 Expression Level in Non-Small Cell Lung Cancer On Computed tomography Using Machine Learning.
    T Shiinoki, K Fujimoto, Y Kawazoe, Y Yuasa, M Kajima, Y Manabe, T Hirano, K Matsunaga, H Tanaka.
    AAPM 2021, 63rd Annual Meeting.<\/li>\n\n\n\n
  4. Comparison of the radiomics-based predictive models using machine learning and nomogram for epidermal growth factor receptor mutation status and subtypes in lung adenocarcinoma.
    Yusuke Kawazoe, Takehiro Shiinoki, Koya Fujimoto, Yuki Yuasa, Tsunahiko Hirano, Kazuto Matsunaga, Hidekazu Tanaka.
    Physical and engineering sciences in Medicine 2023\u5e742\u670814\u65e5<\/li>\n\n\n\n
  5. Investigation of the combination of intratumoral and peritumoral radiomic signatures for predicting epidermal growth factor receptor mutation in lung adenocarcinoma.
    Yusuke Kawazoe, Takehiro Shiinoki, Koya Fujimoto, Yuki Yuasa, Tsunahiko Hirano, Kazuto Matsunaga, Hidekazu Tanaka.
    Journal of Applied Clinical Medical Physics 2023\u5e744\u6708<\/li>\n\n\n\n
  6. Predicting programmed death-ligand 1 expression level in non-small cell lung cancer using a combination of peritumoral and intratumoral radiomic features on computed tomography
    Shiinoki\u3000T, Fujimoto K, Kawazoe K, Yuasa Y, Kajima M, Manabe Y, Ono T, Hirano T, Matsunaga K, Tanaka H.
    Biomedical Physics & Engineering Express 8(2) 025008-025008 2022 March 1.<\/li>\n\n\n\n
  7. Single-energy CT-based perfusion imaging in thoracic and abdominal region based on the convolution neural network
    Y. Yuasa, T. Shiinoki, K. Fujimoto, H. Tanaka.
    International Journal of Computer Assisted Radiology and Surgery 15(S1) 1 \u2013 214.<\/li>\n\n\n\n
  8. \u6df1\u5c64\u5b66\u7fd2\u3092\u5229\u7528\u3057\u305f\u4eee\u60f3Dual energy CT\u753b\u50cf\u3068\u30e8\u30fc\u30c9\u30de\u30c3\u30d7\u306e\u751f\u6210
    \u6e6f\u6dfa\u52c7\u7d00, \u690e\u6728\u5065\u88d5, \u85e4\u672c\u6602\u4e5f, \u7530\u4e2d\u79c0\u548c
    \u7b2c50\u56de\u65e5\u672c\u653e\u5c04\u7dda\u6280\u8853\u5b66\u4f1a\u79cb\u5b63\u5b66\u8853\u5927\u4f1a 2022\u5e7410\u67088\u65e5<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Imaging Biopsy using AI Tumors are heterogeneous and have a variety of characteristics. Medical imaging can obtain information about the entire tumor in a minimally invasive manner. In medical imaging, the entire tumor appears to be uniform in density, but statistical analysis of image features can extract information that is invisible to the human eye. […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":942,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"swell_btn_cv_data":"","_locale":"en_US","_original_post":"https:\/\/ds27i1.cc.yamaguchi-u.ac.jp\/~medphys\/?page_id=429","footnotes":""},"class_list":["post-954","page","type-page","status-publish","hentry","en-US"],"_links":{"self":[{"href":"https:\/\/ds27i1.cc.yamaguchi-u.ac.jp\/~medphys\/wp-json\/wp\/v2\/pages\/954","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ds27i1.cc.yamaguchi-u.ac.jp\/~medphys\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/ds27i1.cc.yamaguchi-u.ac.jp\/~medphys\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/ds27i1.cc.yamaguchi-u.ac.jp\/~medphys\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ds27i1.cc.yamaguchi-u.ac.jp\/~medphys\/wp-json\/wp\/v2\/comments?post=954"}],"version-history":[{"count":4,"href":"https:\/\/ds27i1.cc.yamaguchi-u.ac.jp\/~medphys\/wp-json\/wp\/v2\/pages\/954\/revisions"}],"predecessor-version":[{"id":1350,"href":"https:\/\/ds27i1.cc.yamaguchi-u.ac.jp\/~medphys\/wp-json\/wp\/v2\/pages\/954\/revisions\/1350"}],"up":[{"embeddable":true,"href":"https:\/\/ds27i1.cc.yamaguchi-u.ac.jp\/~medphys\/wp-json\/wp\/v2\/pages\/942"}],"wp:attachment":[{"href":"https:\/\/ds27i1.cc.yamaguchi-u.ac.jp\/~medphys\/wp-json\/wp\/v2\/media?parent=954"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}