|Awarded On||February 16, 2022|
|Title||Automated image quality assessment for AI applications in cancer treatment|
|Award Mechanism||Individual Investigator|
|Institution/Organization||The University of Texas M.D. Anderson Cancer Center|
|Principal Investigator/Program Director||Laurence E Court|
|Cancer Sites||Breast, Cervix Uteri, Head and Neck|
Artificial intelligence and other automation approaches are set to dramatically improve workflows, consistency and quality in radiation therapy treatment planning and other image-guided cancer therapies. This is important because it should lead to improved patient outcomes and reduced treatment cost. The use of AI may, however, also result in avoidable risk to the patient, specifically related to human error, over-reliance (automation bias), and off-label use. AI workflows should, therefore, be designed to avoid these risks. One example is the influence of imaging protocol and image quality on AI-based auto-contouring (which is used to identify the tumor and normal tissues), as even ro...