Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cascading Neural Network Methodology for Artificial
  Intelligence-Assisted Radiographic Detection and Classification of Lead-Less
  Implanted Electronic Devices within the Chest   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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The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga