Dataset Information

Deep Learning-Based Algorithm for the Detection and Characterization of MRI Safety of Cardiac Implantable Electronic Devices on Chest Radiographs.

ABSTRACT:

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.

SUBMITTER: Kim UH

PROVIDER: S-EPMC8546129 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background To date, limited population-level studies have examined the impact of sex on the acute complications of cardiac implantable electronic devices ( CIED) , including permanent pacemakers, implantable cardioverter defibrillators, and cardiac resynchronization therapy devices. Methods and Results We studied all patients aged >18 years from 2010 to 2015 who were a resident of Australia or New Zealand, undergoing a new permanent pacemaker, implantable cardioverter defibrillator, or cardiac resynchronization therapy implant. Standardized variables were collected including patient demographic characteristics, primary and secondary diagnoses, procedures performed and discharge status. Diagnoses and procedures were coded as per the International Classification of Diseases, Tenth Revision ( ICD-10) and the Australian Classification of Health Interventions. The primary end point was the incidence of major CIED -related complications in-hospital or within 90 days of discharge, with the effect of sex evaluated using multiple logistic regression. A total of 81 304 new CIED (61 658 permanent pacemakers, 12 097 implantable cardioverter defibrillators, 7574 cardiac resynchronization therapy) implants were included (38% women). Overall, 8.5% of women and 8.0% of men experienced a CIED complication ( P=0.008). Differences between women and men remained significant after adjustment for age, procedural acuity, and comorbidities (odds ratio 1.10, 95% CI: 1.04-1.16, P<0.001). Differences in CIED complication rates were primarily driven by excess rate of in-hospital pleural drainage (1.2% women versus 0.6% men, P<0.001; adjusted odds ratio 1.86, 95% CI: 1.59-2.17, P<0.001) and pericardial drainage (0.3% women versus 0.1% men, P<0.001; adjusted odds ratio 2.17, 95% CI: 1.48-3.18, P<0.001). Conclusions Women are at higher risk of acute CIED complications. Improvements in implant technique and technologies are required to minimize the risk of implant-related complications in women.