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Pilot Report for Intracranial Hemorrhage Detection with Deep Learning Implanted Head Computed Tomography Images at Emergency Department

  • Clinical Systems
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Abstract

Hemorrhagic stroke is a serious clinical condition that requires timely diagnosis. An artificial intelligence algorithm system called DeepCT can identify hemorrhagic lesions rapidly from non-contrast head computed tomography (NCCT) images and has received regulatory clearance. A non-controlled retrospective pilot clinical trial was conducted. Patients who received NCCT at the emergency department (ED) of Kaohsiung Veteran General Hospital were collected. From 2020 January-1st to April-30th, the physicians read NCCT images without DeepCT. From 2020May-1st to August-31st, the physicians were assisted by DeepCT. The length of ED stays (LOS) for the patients was collected. 2,999 patients were included (188 and 2811 with and without ICH). For patients with a final diagnosis of ICH, implementing DeepCT significantly shortened their LOS (560.67 ± 604.93 min with DeepCT vs. 780.83 ± 710.27 min without DeepCT; p = 0.0232). For patients with a non-ICH diagnosis, the LOS did not significantly differ (705.90 ± 760.86 min with DeepCT vs. 679.45 ± 681.97 min without DeepCT; p = 0.3362). For patients with ICH, those assisted with DeepCT had a significantly shorter LOS than those without DeepCT. For patients with a non-ICH diagnosis, implementing DeepCT did not affect the LOS, because emergency physicians need same efforts to identify the underlying problem(s) with or without DeepCT. In summary, implementing DeepCT system in the ED will save costs, decrease LOS, and accelerate patient flow; most importantly, it will improve the quality of care and increase the confidence and shorten the response time of the physicians and radiologists.

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Authors and Affiliations

  1. Emergency Department, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan

    Hung-Wei Chang Chien

  2. Quality Management Center, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan

    Tsung-Lung Yang & Wang-Chuan Juang

  3. Department of Business Management, National Sun Yat-sen University, Kaohsiung, Taiwan

    Wang-Chuan Juang

  4. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan

    Yen-Yu Arthur Chen & Yu-Chuan Jack Li

  5. Department of Orthopedics, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan

    Chih-Yu Chen

  6. International Ph.D. Program in Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei City, Taiwan

    Chih-Yu Chen

Authors
  1. Hung-Wei Chang Chien
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  2. Tsung-Lung Yang
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  3. Wang-Chuan Juang
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  4. Yen-Yu Arthur Chen
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Corresponding author

Correspondence to Chih-Yu Chen.

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Ethics approval and consent to participate

This study approved by the Joint Institutional Review Board (KSVGH20-CT10-12) of Kaohsiung Veteran General Hospital (KSVGH). All the participants had informed consent. The medical device used for the study had premarket notification by regulatory agencies (US FDA 510(k) No.: K182875 and Taiwan Food and Drug Administration (TFDA) license No.: MOHW-MD-No. 006649).

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The authors declare no conflict of interest.

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Chien, HW.C., Yang, TL., Juang, WC. et al. Pilot Report for Intracranial Hemorrhage Detection with Deep Learning Implanted Head Computed Tomography Images at Emergency Department. J Med Syst 46, 49 (2022). https://doi.org/10.1007/s10916-022-01833-z

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  • DOI: https://doi.org/10.1007/s10916-022-01833-z

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