Room: AAPM ePoster Library
Purpose: The AAPM previously developed a research database consisting of the NIH grants that were awarded to its members. The purpose of this study is to classify these grants into various medical physics sub-disciplines and analyze the scope of AAPM member research.
Methods: For this study, an algorithm was developed to classify grants topics into medical physics research subspecialties (grants from 2002 to 2018 were analyzed). This algorithm utilized a search for common words and phrases within grant titles, keywords, abstracts, and activity codes to classify them. AAPM member grants were compared with non-AAPM member grants in various relevant subcategories to assess what percentage of these grants were held by AAPM members.
Results: The percentage of AAPM member grants that included words relating to both imaging and therapy (image-guided therapy grants) increased from 13% (27/207) to 24% (59/246) from 2002 to 2018. The majority of AAPM member grants included words relating to clinical research (81% of grants in 2002 and 96% in 2018). The percentage of AAPM member grants utilizing words relating to artificial intelligence (AI) increased from 7% to 16% from 2002 to 2018. AAPM member grants focused more on cancer than all other diseases combined. When comparing AAPM member with non-AAPM member grants it was found that in 2018 AAPM members held a substantial fraction of all NIH grants relating to radionuclide therapy (27%), brachytherapy (40%), intensity-modulated radiation therapy (29%), and particle therapy (50%).
Conclusion: The majority of grants awarded to AAPM members focused on clinical research, which underlies the translational aspect of medical physics and suggests medical physicists are uniquely positioned to help translate new technologies such as AI into the clinic. A significant fraction of all radiotherapy-related research grants were awarded to AAPM members, emphasizing the important role physicists have in developing radiotherapy-related treatments and technologies.
Image-guided Therapy, Classifier Design