Automated Cerebral Hemorrhage Detection Using RAPID

References

1. 1.↵
   1. van Asch CJ,
   2. Luitse MJ,
   3. Rinkel GJ, et al

   . Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis. Lancet Neurol 2010;9:167–76 doi:10.1016/S1474-4422(09)70340-0 pmid:20056489

   CrossRefPubMedWeb of Science
2. 2.↵
   1. Heit JJ,
   2. Iv M,
   3. Wintermark M

   . Imaging of intracranial hemorrhage. J Stroke 2017;19:11–27 doi:10.5853/jos.2016.00563 pmid:28030895

   CrossRefPubMed
3. 3.↵
   1. Zahuranec DB,
   2. Lisabeth LD,
   3. Sanchez BN, et al

   . Intracerebral hemorrhage mortality is not changing despite declining incidence. Neurology 2014;82:2180–86 doi:10.1212/WNL.0000000000000519 pmid:24838789

   Abstract/FREE Full Text
4. 4.↵
   1. Broderick JP,
   2. Brott TG,
   3. Duldner JE, et al

   . Volume of intracerebral hemorrhage. A powerful and easy-to-use predictor of 30-day mortality. Stroke 1993;24:987–93 doi:10.1161/01.STR.24.7.987 pmid:8322400

   Abstract/FREE Full Text
5. 5.↵
   1. Dengler NF,
   2. Sommerfeld J,
   3. Diesing D, et al

   . Prediction of cerebral infarction and patient outcome in aneurysmal subarachnoid hemorrhage: comparison of new and established radiographic, clinical and combined scores. Eur J Neurol 2018;25:111–19 doi:10.1111/ene.13471 pmid:28940973

   CrossRefPubMed
6. 6.↵
   1. Hemphill JC 3rd.,
   2. Greenberg SM,
   3. Anderson CS, et al

   . Guidelines for the management of spontaneous intracerebral hemorrhage: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2015;46:2032–60 doi:10.1161/STR.0000000000000069 pmid:26022637

   Abstract/FREE Full Text
7. 7.↵
   1. Goldstein JN,
   2. Gilson AJ

   . Critical care management of acute intracerebral hemorrhage. Curr Treat Options Neurol 2011;13:204–16 doi:10.1007/s11940-010-0109-2 pmid:21222062

   CrossRefPubMed
8. 8.↵
   1. Glover M 4th.,
   2. Almeida RR,
   3. Schaefer PW, et al

   . Quantifying the impact of noninterpretive tasks on radiology report turn-around times. J Am Coll Radiol 2017;14:1498–1503 doi:10.1016/j.jacr.2017.07.023 pmid:28916177

   CrossRefPubMed
9. 9.↵
   1. Chang PD,
   2. Kuoy E,
   3. Grinband J, et al

   . Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. AJNR Am J Neuroradiol 2018;39:1609–16 doi:10.3174/ajnr.A5742 pmid:30049723

   Abstract/FREE Full Text
10. 10.↵
    1. Arbabshirani MR,
    2. Fornwalt BK,
    3. Mongelluzzo GJ, et al

    . Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit Med 2018;1:9 doi:10.1038/s41746-017-0015-z pmid:31304294 pmid:30318264

    CrossRefPubMed
11. 11.↵
    1. Chilamkurthy S,
    2. Ghosh R,
    3. Tanamala S, et al

    . Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 2018;392:2388–96 doi:10.1016/S0140-6736(18)31645-3 pmid:30318264

    CrossRefPubMed
12. 12.↵
    1. Titano JJ,
    2. Badgeley M,
    3. Schefflein J, et al

    . Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med 2018;24:1337–41 doi:10.1038/s41591-018-0147-y pmid:30104767

    CrossRefPubMed
13. 13.↵
    1. Ye H,
    2. Gao F,
    3. Yin Y, et al

    . Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. Eur Radiol 2019;29:6191–01 doi:10.1007/s00330-019-06163-2 pmid:31041565

    CrossRefPubMed
14. 14.↵
    1. Ginat DT

    . Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage. Neuroradiology 2020;62:335–40 doi:10.1007/s00234-019-02330-w pmid:31828361

    CrossRefPubMed
15. 15.↵
    1. Saver JL,
    2. Goyal M,
    3. Bonafe A, et al

    . Stent-retriever thrombectomy after intravenous t-PA vs. t-PA alone in stroke. N Engl J Med 2015;372:2285–95 doi:10.1056/NEJMoa1415061 pmid:25882376

    CrossRefPubMed
16. 16.↵
    1. Campbell BC,
    2. Mitchell PJ,
    3. Kleinig TJ, et al

    . Endovascular therapy for ischemic stroke with perfusion-imaging selection. N Engl J Med 2015;372:1009–18 doi:10.1056/NEJMoa1414792 pmid:25671797

    CrossRefPubMed
17. 17.↵
    1. Albers GW,
    2. Marks MP,
    3. Kemp S, et al

    . 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
18. 18.↵
    1. Nogueira RG,
    2. Jadhav AP,
    3. Haussen DC, et al

    . Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med 2018;378:11–21 doi:10.1056/NEJMoa1706442 pmid:29129157

    CrossRefPubMed
19. 19.↵
    1. Winsberg F,
    2. Elkin M,
    3. Macy J, et al

    . Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis. Radiology 1967;89:211–15 doi:10.1148/89.2.211

    CrossRef
20. 20.↵
    1. Monnier-Cholley L,
    2. MacMahon H,
    3. Katsuragawa S, et al

    . Computer-aided diagnosis for detection of interstitial opacities on chest radiographs. AJR Am J Roentgenol 1998;171:1651–56 doi:10.2214/ajr.171.6.9843307 pmid:9843307

    CrossRefPubMed
21. 21.↵
    1. Yoshida H,
    2. Masutani Y,
    3. MacEneaney P, et al

    . Computerized detection of colonic polyps at CT colonography on the basis of volumetric features: pilot study. Radiology 2002;222:327–36 doi:10.1148/radiol.2222010506 pmid:11818596

    CrossRefPubMed
22. 22.↵
    1. Gulshan V,
    2. Peng L,
    3. Coram M, et al

    . Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316:2402–10 doi:10.1001/jama.2016.17216 pmid:27898976

    CrossRefPubMed
23. 23.↵
    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
24. 24.↵
    1. Sunshine JH,
    2. Burkhardt JH

    . Radiology groups' workload in relative value units and factors affecting it. Radiology 2000;214:815–22 doi:10.1148/radiology.214.3.r00mr50815 pmid:10715051

    CrossRefPubMed
25. 25.↵
    1. McDonald RJ,
    2. Schwartz KM,
    3. Eckel LJ, et al

    . The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Acad Radiol 2015;22:1191–98 doi:10.1016/j.acra.2015.05.007 pmid:26210525

    CrossRefPubMed