A Clinical and Imaging Fused Deep Learning Model Matches Expert Clinician Prediction of 90-Day Stroke Outcomes

References

1. 1.↵
   1. Xie Y,
   2. Jiang B,
   3. Gong E, et al

   . Use of gradient boosting machine learning to predict patient outcome in acute ischemic stroke on the basis of imaging, demographic, and clinical information. AJR Am J Roentgenol 2019;212:44–51 doi:10.2214/AJR.18.20260 pmid:30354266

   CrossRefPubMed
2. 2.↵
   1. Nichols-Larsen DS,
   2. Clark PC,
   3. Zeringue A, et al

   . Factors influencing stroke survivors’ quality of life during subacute recovery. Stroke 2005;36:1480–84 doi:10.1161/01.STR.0000170706.13595.4f pmid:15947263

   Abstract/FREE Full Text
3. 3.↵
   1. Langhorne P,
   2. Bernhardt J,
   3. Kwakkel G

   . Stroke rehabilitation. Lancet 2011;377:1693–702 doi:10.1016/S0140-6736(11)60325-5 pmid:21571152

   CrossRefPubMedWeb of Science
4. 4.↵
   1. Heo J,
   2. Yoon JG,
   3. Park H, et al

   . Machine learning-based model for prediction of outcomes in acute stroke. Stroke 2019;50:1263–65 doi:10.1161/STROKEAHA.118.024293 pmid:30890116

   CrossRefPubMed
5. 5.↵
   1. Brugnara G,
   2. Neuberger U,
   3. Mahmutoglu MA, et al

   . Multimodal predictive modeling of endovascular treatment outcome for acute ischemic stroke using machine-learning. Stroke 2020;51:3541–51 doi:10.1161/STROKEAHA.120.030287 pmid:33040701

   CrossRefPubMed
6. 6.↵
   1. Ospel JM,
   2. Hill MD,
   3. Menon BK, et al

   ; ESCAPE-NA1 Investigators. Strength of association between infarct volume and clinical outcome depends on the magnitude of infarct size: results from the ESCAPE-NA1 trial. AJNR Am J Neuroradiol 2021;42:1375–79 doi:10.3174/ajnr.A7183 pmid:34167959

   Abstract/FREE Full Text
7. 7.↵
   1. Albers GW,
   2. Marks MP,
   3. Kemp S, et al

   ; DEFUSE 3 Investigators. Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med 2018;378:708–18 doi:10.1056/NEJMoa1713973 pmid:29364767

   CrossRefPubMed
8. 8.↵
   1. Zhang MY,
   2. Mlynash M,
   3. Sainani KL, et al

   . Ordinal prediction model of 90-day modified Rankin scale in ischemic stroke. Front Neurol 2021;12:727171 doi:10.3389/fneur.2021.727171 pmid:34744968

   CrossRefPubMed
9. 9.↵
   1. Shen D,
   2. Wu G,
   3. Suk HI

   . Deep learning in medical image analysis. Annu Rev Biomed Eng 2017;19:221–48 doi:10.1146/annurev-bioeng-071516-044442 pmid:28301734

   CrossRefPubMed
10. 10.↵
    1. Liu Y,
    2. Zheng H,
    3. Liang Z, et al

    . Textured-based deep learning in prostate cancer classification with 3t multiparametric MRI: comparison with PI-RADS-based classification. Diagnostics (Basel) 2021;11:1785 doi:10.3390/diagnostics11101785 pmid:34679484

    CrossRefPubMed
11. 11.↵
    1. Fargen KM,
    2. Kittel C,
    3. Curry BP, et al

    ; Satin Research Group. Mechanical thrombectomy decision making and prognostication: Stroke treatment Assessments prior to Thrombectomy In Neurointervention (SATIN) study. J Neurointerv Surg 2023;15:e381–87 doi:10.1136/jnis-2022-019741 pmid:36609542

    Abstract/FREE Full Text
12. 12.↵
    1. Banks JL,
    2. Marotta CA

    . Outcomes validity and reliability of the modified Rankin scale: implications for stroke clinical trials: a literature review and synthesis. Stroke 2007;38:1091–96 doi:10.1161/01.STR.0000258355.23810.c6 pmid:17272767

    Abstract/FREE Full Text
13. 13.↵
    1. Broderick JP,
    2. Adeoye O,
    3. Elm J

    . Evolution of the modified Rankin scale and its use in future stroke trials. Stroke 2017;48:2007–12 doi:10.1161/STROKEAHA.117.017866 pmid:28626052

    FREE Full Text
14. 14.↵
    1. Liu Y,
    2. Yu Y,
    3. Ouyang J, et al

    . Functional outcome prediction in acute ischemic stroke using a fused imaging and clinical deep learning model. Stroke 2023;54:2316–27 doi:10.1161/STROKEAHA.123.044072 pmid:37485663

    CrossRefPubMed
15. 15.↵
    1. Gwet KL

    . Computing inter-rater reliability and its variance in the presence of high agreement. Br J Math Stat Psychol 2008;61:29–48 doi:10.1348/000711006X126600 pmid:18482474

    CrossRefPubMed
16. 16.↵
    StataCorp. Stata Statistical Software: Release 15. College Station, Texas: StataCorp LLC. 2017.
17. 17.↵
    1. Chalos V,
    2. Venema E,
    3. Mulder MJ, et al

    ; HERMES Collaborators; MR CLEAN Registry Investigators. Development and validation of a postprocedural model to predict outcome after endovascular treatment for ischemic stroke. JAMA Neurol 2023;80:940–48 doi:10.1001/jamaneurol.2023.2392 pmid:37523199

    CrossRefPubMed
18. 18.↵
    1. Kamnitsas K,
    2. Ledig C,
    3. Newcombe VF, et al

    . Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 2017;36:61–78 doi:10.1016/j.media.2016.10.004 pmid:27865153

    CrossRefPubMed
19. 19.↵
    1. Esteva A,
    2. Kuprel B,
    3. Novoa RA, et al

    . Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115–18 doi:10.1038/nature21056 pmid:28117445

    CrossRefPubMed