Cascading Neural Network Methodology for Artificial Intelligence-Assisted Radiographic Detection and Classification of Lead-Less Implanted Electronic Devices within the Chest

• URL: http://arxiv.org/abs/2108.11954v1
• Date: Wed, 25 Aug 2021 19:29:48 GMT
• Title: Cascading Neural Network Methodology for Artificial Intelligence-Assisted Radiographic Detection and Classification of Lead-Less Implanted Electronic Devices within the Chest
• Authors: Mutlu Demirer, Richard D. White, Vikash Gupta, Ronnie A. Sebro, Barbaros S. Erdal
• Abstract summary: This work focused on developing CXR interpretation-assisting Artificial Intelligence (AI) methodology with: 1. 100% detection for LLIED presence/location; and 2. High classification in LLIED typing. For developing the cascading neural network (detection via Faster R-CNN and classification via Inception V3), "ground-truth" CXR annotation (ROI labeling per LLIED), as well as inference display (as Generated Bounding Boxes (GBBs))
• Score: 0.7874708385247353
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background & Purpose: Chest X-Ray (CXR) use in pre-MRI safety screening for Lead-Less Implanted Electronic Devices (LLIEDs), easily overlooked or misidentified on a frontal view (often only acquired), is common. Although most LLIED types are "MRI conditional": 1. Some are stringently conditional; 2. Different conditional types have specific patient- or device- management requirements; and 3. Particular types are "MRI unsafe". This work focused on developing CXR interpretation-assisting Artificial Intelligence (AI) methodology with: 1. 100% detection for LLIED presence/location; and 2. High classification in LLIED typing. Materials & Methods: Data-mining (03/1993-02/2021) produced an AI Model Development Population (1,100 patients/4,871 images) creating 4,924 LLIED Region-Of-Interests (ROIs) (with image-quality grading) used in Training, Validation, and Testing. For developing the cascading neural network (detection via Faster R-CNN and classification via Inception V3), "ground-truth" CXR annotation (ROI labeling per LLIED), as well as inference display (as Generated Bounding Boxes (GBBs)), relied on a GPU-based graphical user interface. Results: To achieve 100% LLIED detection, probability threshold reduction to 0.00002 was required by Model 1, resulting in increasing GBBs per LLIED-related ROI. Targeting LLIED-type classification following detection of all LLIEDs, Model 2 multi-classified to reach high-performance while decreasing falsely positive GBBs. Despite 24% suboptimal ROI image quality, classification was correct in 98.9% and AUCs for the 9 LLIED-types were 1.00 for 8 and 0.92 for 1. For all misclassification cases: 1. None involved stringently conditional or unsafe LLIEDs; and 2. Most were attributable to suboptimal images. Conclusion: This project successfully developed a LLIED-related AI methodology supporting: 1. 100% detection; and 2. Typically 100% type classification.

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