Literature DB >> 35618476

Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines).

Abhinav K Jha1, Tyler J Bradshaw2, Irène Buvat3, Mathieu Hatt4, Prabhat Kc5, Chi Liu6, Nancy F Obuchowski7, Babak Saboury8, Piotr J Slomka9, John J Sunderland10, Richard L Wahl11, Zitong Yu12, Sven Zuehlsdorff13, Arman Rahmim14, Ronald Boellaard15.   

Abstract

An important need exists for strategies to perform rigorous objective clinical-task-based evaluation of artificial intelligence (AI) algorithms for nuclear medicine. To address this need, we propose a 4-class framework to evaluate AI algorithms for promise, technical task-specific efficacy, clinical decision making, and postdeployment efficacy. We provide best practices to evaluate AI algorithms for each of these classes. Each class of evaluation yields a claim that provides a descriptive performance of the AI algorithm. Key best practices are tabulated as the RELAINCE (Recommendations for EvaLuation of AI for NuClear medicinE) guidelines. The report was prepared by the Society of Nuclear Medicine and Molecular Imaging AI Task Force Evaluation team, which consisted of nuclear-medicine physicians, physicists, computational imaging scientists, and representatives from industry and regulatory agencies.
© 2022 by the Society of Nuclear Medicine and Molecular Imaging.

Entities:  

Keywords:  PET; SPECT; artificial intelligence; best practices; clinical decision making; clinical task; evaluation; generalizability; postdeployment; technical efficacy

Mesh:

Year:  2022        PMID: 35618476      PMCID: PMC9454473          DOI: 10.2967/jnumed.121.263239

Source DB:  PubMed          Journal:  J Nucl Med        ISSN: 0161-5505            Impact factor:   11.082


  2 in total

1.  Investigating the limited performance of a deep-learning-based SPECT denoising approach: An observer-study-based characterization.

Authors:  Zitong Yu; Md Ashequr Rahman; Abhinav K Jha
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2022-04-04

Review 2.  Monitoring of Current Cancer Therapy by Positron Emission Tomography and Possible Role of Radiomics Assessment.

Authors:  Noboru Oriuchi; Hideki Endoh; Kyoichi Kaira
Journal:  Int J Mol Sci       Date:  2022-08-20       Impact factor: 6.208

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.