Korean Journal of Radiology

  • 제22권11호
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  • Pages.1918-1928
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  • 2021
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  • 1229-6929(pISSN)
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  • 2005-8330(eISSN)
대한영상의학회 (The Korean Society of Radiology)
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Deep Learning-Based Algorithm for the Detection and Characterization of MRI Safety of Cardiac Implantable Electronic Devices on Chest Radiographs

  • Ue-Hwan Kim (School of Electrical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Moon Young Kim (Department of Radiology, SMG-SNU Boramae Medical Center) ;
  • Eun-Ah Park (Department of Radiology, Seoul National University Hospital) ;
  • Whal Lee (Department of Radiology, Seoul National University Hospital) ;
  • Woo-Hyun Lim (Division of Cardiology, Department of Internal Medicine, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine) ;
  • Hack-Lyoung Kim (Division of Cardiology, Department of Internal Medicine, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine) ;
  • Sohee Oh (Medical Research Collaborating Center, Seoul National University Boramae Medical Center) ;
  • Kwang Nam Jin (Department of Radiology, SMG-SNU Boramae Medical Center)
  • 투고 : 2021.03.11
  • 심사 : 2021.06.07
  • 발행 : 2021.11.01
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초록

Objective: With the recent development of various MRI-conditional cardiac implantable electronic devices (CIEDs), the accurate identification and characterization of CIEDs have become critical when performing MRI in patients with CIEDs. We aimed to develop and evaluate a deep learning-based algorithm (DLA) that performs the detection and characterization of parameters, including MRI safety, of CIEDs on chest radiograph (CR) in a single step and compare its performance with other related algorithms that were recently developed. Materials and Methods: We developed a DLA (X-ray CIED identification [XCID]) using 9912 CRs of 958 patients with 968 CIEDs comprising 26 model groups from 4 manufacturers obtained between 2014 and 2019 from one hospital. The performance of XCID was tested with an external dataset consisting of 2122 CRs obtained from a different hospital and compared with the performance of two other related algorithms recently reported, including PacemakerID (PID) and Pacemaker identification with neural networks (PPMnn). Results: The overall accuracies of XCID for the manufacturer classification, model group identification, and MRI safety characterization using the internal test dataset were 99.7% (992/995), 97.2% (967/995), and 98.9% (984/995), respectively. These were 95.8% (2033/2122), 85.4% (1813/2122), and 92.2% (1956/2122), respectively, with the external test dataset. In the comparative study, the accuracy for the manufacturer classification was 95.0% (152/160) for XCID and 91.3% for PPMnn (146/160), which was significantly higher than that for PID (80.0%,128/160; p < 0.001 for both). XCID demonstrated a higher accuracy (88.1%; 141/160) than PPMnn (80.0%; 128/160) in identifying model groups (p < 0.001). Conclusion: The remarkable and consistent performance of XCID suggests its applicability for detection, manufacturer and model identification, as well as MRI safety characterization of CIED on CRs. Further studies are warranted to guarantee the safe use of XCID in clinical practice.

키워드

과제정보

This study was supported by grants from SMG-SNU Boramae Medical Center (grant number 03-2019-26) and the Radiological Research Foundation of Korea (grant number 2018-01).

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