This paper is in the following e-collection/theme issue:

Mobile Health (mhealth) (2748) Imaging Informatics (182) mHealth in a Clinical Setting (579) Stroke (147)

Published on in Vol 22, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15893, first published .
Reliability of Smartphone for Diffusion-Weighted Imaging–Alberta Stroke Program Early Computed Tomography Scores in Acute Ischemic Stroke Patients: Diagnostic Test Accuracy Study

Reliability of Smartphone for Diffusion-Weighted Imaging–Alberta Stroke Program Early Computed Tomography Scores in Acute Ischemic Stroke Patients: Diagnostic Test Accuracy Study

Reliability of Smartphone for Diffusion-Weighted Imaging–Alberta Stroke Program Early Computed Tomography Scores in Acute Ischemic Stroke Patients: Diagnostic Test Accuracy Study

Authors of this article:

Kenichiro Sakai1 Author Orcid Image ;   Teppei Komatsu1 Author Orcid Image ;   Yasuyuki Iguchi1 Author Orcid Image ;   Hiroyuki Takao2 Author Orcid Image ;   Toshihiro Ishibashi2 Author Orcid Image ;   Yuichi Murayama2 Author Orcid Image
Article Authors Cited by (11) Tweetations (3) Metrics

Journals

  1. Komatsu T, Sakai K, Iguchi Y, Takao H, Ishibashi T, Murayama Y. Using a Smartphone Application for the Accurate and Rapid Diagnosis of Acute Anterior Intracranial Arterial Occlusion: Usability Study. Journal of Medical Internet Research 2021;23(8):e28192 View
  2. Dula A, Milling T, Johnston S, Aydelotte J, Peil G, Robinson A, Asif K, Pan S, Parekh S, Warach S. Smartphone imaging repository: a novel method for creating a CT image bank. Trials 2023;24(1) View
  3. Fukaguchi K, Goto T, Yamamoto T, Yamagami H. Experimental Implementation of NSER Mobile App for Efficient Real-Time Sharing of Prehospital Patient Information With Emergency Departments: Interrupted Time-Series Analysis. JMIR Formative Research 2022;6(7):e37301 View
  4. Sato H, Kinoshita M, Tani Y, Kimura T, Osanai T, Osanai H, Ogasawara K. The health economic effects of an imaging technology–based telemedicine system for rural neuro-emergency patient care. Neurosurgical Focus 2022;52(6):E2 View
  5. Sakai K, Sato T, Komatsu T, Mitsumura H, Iguchi Y, Ishibashi T, Murayama Y, Takeshita K, Takao H. Communication-type smartphone application can contribute to reducing elapsed time to reperfusion therapy. Neurological Sciences 2021;42(11):4563 View
  6. ALDİNC H, GUN C. Smartphones for evaluation of computerized tomography scan of patients with suspected skull fractures and intracranial hemorrhage in emergency medicine. Journal of Surgery and Medicine 2021;5(12):1206 View
  7. Omarov B, Tursynova A, Postolache O, Gamry K, Batyrbekov A, Aldeshov S, Azhibekova Z, Nurtas M, Aliyeva A, Shiyapov K. Modified UNet Model for Brain Stroke Lesion Segmentation on Computed Tomography Images. Computers, Materials & Continua 2022;71(3):4701 View
  8. He X, Zheng X, Ding H, Liu Y, Zhu H. AI-CDSS Design Guidelines and Practice Verification. International Journal of Human–Computer Interaction 2023:1 View
  9. Mishra B, Agarwal A, Garg A, Antil Y, Sharma S, Parial A, Nilima N, Vishnu V, Srivastava M. Assessing ASPECTS and ICH score reliability on NCCT scans via SMART INDIA App and PACS by neurologists and neuro-radiologists. Health and Technology 2024;14(2):305 View
  10. Chaki J, Woźniak M. Deep Learning and Artificial Intelligence in Action (2019–2023): A Review on Brain Stroke Detection, Diagnosis, and Intelligent Post-Stroke Rehabilitation Management. IEEE Access 2024;12:52161 View
  11. UCHINO H, OSANAI T, ITO M, KURISU K, SUGIYAMA T, FUJIMURA M. Effective Smartphone Application Use for Postoperative Management of Moyamoya Disease. Neurologia medico-chirurgica 2024 View