2022 AAPM 64th Annual Meeting
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Session Title: AI Clinical Translation: Opportunities and Pitfalls
Question 1: Which one of the following is the least reliable measure for clinical commissioning of AI auto-segmentation method?
Reference:1. Valentini V, Boldrini L, Damiani A, Muren LP: Recommendations on how to establish evidence from auto-segmentation software in radiotherapy. Radiother Oncol 112:317-20, 2014. 2. Vaassen F, Hazelaar C, Vaniqui A, Gooding M, van der Heyden B, Canters R, van Elmpt W: Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy. Physics and Imaging in Radiation Oncology 13:1-6, 2020
Choice A:Dice similarity coefficient
Choice B:Surface distance
Choice C:User editing times
Choice D:DVH derived metrics
Question 2: Which of the following are reasonable approaches to deal with few labeled datasets for CBCT?
Reference:1. Jia X, Wang S, Liang X, Balagopal A, Nguyen D, Yang M, Wang Z, Ji J.X, Qian X, Jiang S. Cone-Beam Computed Tomography (CBCT) Segmentation by Adversarial Learning Domain Adaptation. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. 2. Zhou B, Augenfield J, Chapiro S, Zhou K, Liu C, Duncan J.S. Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration”, Medical Image Analysis. 2021. 3. Jiang J and Veeraraghavan H. One shot PACS: Patient specific anatomic context and shape prior aware recurrent registration-segmentation of longitudinal cone beam CTs. IEEE TMI 2022.
Choice A:Cross-domain adaptation combining CT with CBCT
Choice B:Multi-tasked learning
Choice C:Registration based segmentation
Choice D:All of the above
Question 3: What are sources of bias that could affect AI technologies?
Reference:McIntosh C, Conroy L, Tjong MC, et al. Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer. Nat Med 2021;27:999–1005. doi:10.1038/s41591-021-01359-w
Choice A:Limited training dataset
Choice B:Variation in testing settings
Choice C:Lack of clinical oversight in algorithm development
Choice D:Limited enduser involved in training and/or testing
Choice E:All of the above
Question 4: What are the advantage(s) of data curation?
Reference:Conroy L, Khalifa A, Berlin A, et al. Performance stability evaluation of atlas-based machine learning radiation therapy treatment planning in prostate cancer. Phys Med Biol 2021;66:134001. doi:10.1088/1361-6560/abfff0
Choice A:Reduce dataset size
Choice B:Ensure dataset is highly population-specific
Choice C:Reduce impact of outliers and imaging artifacts
Choice D:None of the above
Choice E:A + B + C
Question 5: Trustworthiness as applied to AI can be defined as:
Reference:Cutillo CM, Sharma KR, Foschini L, et al. Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency. npj Digit Med 2020;3:47. doi:10.1038/s41746-020-0254-2
Choice A:Assess validity and reliability of AI derived outputs
Choice B:Understand and evaluate inner working of AI algorithms
Choice C:Achieve specific goals in the defined setting
Choice D:Understand inputs that could influence outputs
Question 6: What are barriers to NLP being used clinically?
Reference:Danielle S Bitterman 1, Timothy A Miller 2, Raymond H Mak 3, Guergana K Savova 2. Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer. Int J Radiat Oncol Biol Phys. 2021 Jul 1;110(3):641-655. doi: 10.1016/j.ijrobp.2021.01.044. Epub 2021 Feb 3.
Choice A:Methods are not mature enough for clinic use
Choice B:Insufficient training data available in hospital systems
Choice C:Lack of collaboration/guidance on high priority tasks
Choice D:Classification performance is subjective for language tasks
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