An Artificial Intelligence Tool for Clinical Decision Support and Protocol Selection for Brain MRI

References

1. 1.↵
   1. Schemmel A,
   2. Lee M,
   3. Hanley T, et al

   . Radiology workflow disruptors: a detailed analysis. J Am Coll Radiology 2016;13:1210–14 doi:10.1016/j.jacr.2016.04.009 pmid:27313127

   CrossRefPubMed
2. 2.↵
   American College of Radiology. ACR Appropriateness Criteria Overview. https://www.acr.org/Clinical-Resources/ACR-Appropriateness-Criteria/Overview. Accessed May 4, 2021
3. 3.↵
   1. Boland GW,
   2. Duszak R,
   3. Kalra M

   . Protocol design and optimization. J Am Coll Radiol 2014;11:440–41 doi:10.1016/j.jacr.2014.01.021 pmid:24793037

   CrossRefPubMed
4. 4.↵
   1. Kalra A,
   2. Chakraborty A,
   3. Fine B, et al

   . Machine learning for automation of radiology protocols for quality and efficiency improvement. J Am Coll Radiol 2020;17:1149–58 doi:10.1016/j.jacr.2020.03.012 pmid:32278847

   CrossRefPubMed
5. 5.↵
   1. Harris ZS

   . Distributional structure. WORD 1954;10:146–62 doi:10.1080/00437956.1954.11659520

   CrossRefPubMed
6. 6.↵
   1. Zhang Y,
   2. Chen Q,
   3. Yang Z, et al

   . BioWordVec, improving biomedical word embeddings with subword information and MeSH. Sci Data 2019;6:52 doi:10.1038/s41597-019-0055-0 pmid:31076572

   CrossRefPubMed
7. 7.↵
   1. Hochreiter S,
   2. Schmidhuber J

   . Long Short-Term Memory. Neural Comput 1997;9:1735–80 doi:10.1162/neco.1997.9.8.1735 pmid:9377276

   CrossRefPubMedWeb of Science
8. 8.↵
   1. Howard J,
   2. Ruder S

   . Universal language model fine-tuning for text classification. May 23, 2018. ArXiv180106146 Cs Stat http://arxiv.org/abs/1801.06146. Accessed April 8, 2021
9. 9.↵
   1. Rehurek R,
   2. Sojka P

   . Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, Valletta, Malta. May 22, 2010:45–50
10. 10.↵
    1. Paszke A,
    2. Gross S,
    3. Massa F, et al

    . PyTorch: an imperative style, high-performance deep learning library. In: Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada. December 8–14, 2019
11. 11.↵
    1. Pedregosa F,
    2. Varoquaux G,
    3. Gramfort A, et al

    . Scikit-learn: machine learning in Python. J Mach Learn Res 2011;12:2825–30

    CrossRefPubMed
12. 12.↵
    1. Seabold S,
    2. Perktold J

    . Statsmodels: econometric and statistical modeling with Python. In: Proceedings of the 9th Python in Science Conference (SciPy 2010), Austin, Texas. June 28 to July 3, 2010 doi:10.25080/Majora-92bf1922-011

    CrossRef
13. 13.↵
    1. Kokhlikyan N,
    2. Miglani V,
    3. Martin M, et al

    . Captum: a unified and generic model interpretability library for PyTorch. ArXiv200907896 Cs Stat. September 16, 2020. http://arxiv.org/abs/2009.07896. Accessed February 12, 2022
14. 14.↵
    1. Sundararajan M,
    2. Taly A,
    3. Yan Q

    . Axiomatic attribution for deep networks. ArXiv170301365. June 12, 2017. http://arxiv.org/abs/1703.01365. Accessed February 12, 2022
15. 15.↵
    1. Brown AD,
    2. Marotta TR

    . A natural language processing-based model to automate MRI brain protocol selection and prioritization. Acad Radiol 2017;24:160–66 doi:10.1016/j.acra.2016.09.013 pmid:27889399

    CrossRefPubMed