Chest radiographs of cardiac devices (Part 1): Cardiovascular implantable electronic devices, cardiac valve prostheses and Amplatzer occluder devices

Mathew, Rishi Philip; Alexander, Timothy; Patel, Vimal; Low, Gavin

doi:10.4102/sajr.v23i1.1730

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…A chest X-ray (CXR) is a standard component of pre-MRI safety screening (for LLIEDs or other man-made objects in the chest). 20 – 27 Such CXR-based screening assumes even greater importance when there is inadequate EMR documentation from lack of a prior visit and/or internal misrecording. 26 – 28 Unfortunately, any LLIED could be overlooked on a CXR due to their mutually small sizes (subject to projection-related distortions), especially when accompanied by (1) Suboptimal radiographic technique (e.g., under-penetration); (2) Patient-related factors (e.g., motion-related blurring); (3) Obscuration by adjacent-internal or superimposed-external radio-opaque or electronic materials.…”

Section: Introductionmentioning

confidence: 99%

Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions

et al. 2022

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Purpose: Chest X-ray (CXR) use in pre-MRI safety screening, such as for lead-less implanted electronic device (LLIED) recognition, is common. To assist CXR interpretation, we "predeployed" an artificial intelligence (AI) model to assess (1) accuracies in LLIED-type (and consequently safety-level) identification, (2) safety implications of LLIED nondetections or misidentifications, (3) infrastructural or workflow requirements, and (4) demands related to model adaptation to real-world conditions.Approach: A two-tier cascading methodology for LLIED detection/localization and identification on a frontal CXR was applied to evaluate the performance of the original nine-class AI model. With the unexpected early appearance of LLIED types during simulated real-world trialing, retraining of a newer 12-class version preceded retrialing. A zero footprint (ZF) graphical user interface (GUI)/viewer with DICOM-based output was developed for inference-result display and adjudication, supporting end-user engagement and model continuous learning and/or modernization.Results: During model testing or trialing using both the nine-class and 12-class models, robust detection/localization was consistently 100%, with mAP 0.99 from fivefold cross-validation. Safety-level categorization was high during both testing (AUC ≥ 0.98 and ≥0.99, respectively) and trialing (accuracy 98% and 97%, respectively). LLIED-type identifications by the two models during testing (1) were 98.9% and 99.5% overall correct and (2) consistently showed AUC ≥ 0.92 (1.00 for 8/9 and 9/12 LLIED-types, respectively). Pre-deployment trialing of both models demonstrated overall type-identification accuracies of 94.5% and 95%, respectively. Of the small number of misidentifications, none involved MRI-stringently conditional or MRIunsafe types of LLIEDs. Optimized ZF GUI/viewer operations led to greater user-friendliness for radiologist engagement.Conclusions: Our LLIED-related AI methodology supports (1) 100% detection sensitivity, (2) high identification (including MRI-safety) accuracy, and (3) future model deployment with facilitated inference-result display and adjudication for ongoing model adaptation to future realworld experiences.

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Section: Introductionmentioning

confidence: 99%

Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions

et al. 2022

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“…Chest radiographs are the initial modality to evaluate the device location and its integrity after implantation and for diagnosis of complications and malfunction. [ 25 , 26 , 27 ]. Antibacterial properties of gold were also desired as a potential lowering of implant failure in the early stages after operation time due to the reluctance of bacteria to settle on the surface [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Laser Superficial Fusion of Gold Nanoparticles with PEEK Polymer for Cardiovascular Application

et al. 2021

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This paper analyses the possibility of obtaining surface-infused nano gold particles with the polyether ether ketone (PEEK) using picosecond laser treatment. To fuse particles into polymer, the raw surface of PEEK was sputtered with 99.99% Au and micromachined by an A-355 laser device for gold particle size reduction. Biomimetic pattern and parameters optimization were key properties of the design for biomedical application. The structures were investigated by employing surface topography in the presence of micron and sub-micron features. The energy of the laser beam stating the presence of polymer bond thermalisation with remelting due to high temperature was also taken into the account. The process was suited to avoid intensive surface modification that could compromise the mechanical properties of fragile cardiovascular devices. The initial material analysis was conducted by power–depth dependence using confocal microscopy. The evaluation of gold particle size reduction was performed with scanning electron microscopy (SEM) (secondary electron (SE) and quadrant backscatter electron detector (QBSD) and energy dispersive spectroscopy (EDS) analysis. The visibility of the constituted coating was checked by a commercial grade X-ray that is commonly used in hospitals. Attempts to reduce deposited gold coating to the size of Au nanoparticles (Au NPs) and to fuse them into the groove using a laser beam have been successfully completed. The relationship between the laser power and the characteristics of the particles remaining in the laser irradiation area has been established. A significant increase in quantity was achieved using laser power with a minimum power of 15 mW. The obtained results allowed for the continuation of the pilot study for augmented research and material properties analysis.

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General approach for evaluation of cardiac implantable electronic devices

Haghjoo

2022

Radiographic Atlas of Cardiac Implantable Electronic Devices

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Chest radiographs of cardiac devices (Part 1): Cardiovascular implantable electronic devices, cardiac valve prostheses and Amplatzer occluder devices

Cited by 3 publications

References 20 publications

Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions

Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions

Laser Superficial Fusion of Gold Nanoparticles with PEEK Polymer for Cardiovascular Application

General approach for evaluation of cardiac implantable electronic devices

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