Artificial Intelligence–Guided Segmentation and Path Planning Software for Transthoracic Lung Biopsy

Artificial Intelligence–Guided Segmentation and Path Planning Software for Transthoracic Lung Biopsy

To validate the sensitivity and specificity of a 3-dimensional (3D) convolutional neural network (CNN) artificial intelligence (AI) software for lung lesion detection and to establish concordance between AI-generated needle paths and those used in actual biopsy procedures. This was a retrospective s...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of vascular and interventional radiology 2024-05, Vol.35 (5), p.780-789.e1
Hauptverfasser: Too, Chow Wei, Fong, Khi Yung, Hang, Guanqi, Sato, Takafumi, Nyam, Chiaw Qing, Leong, Siang Huei, Ng, Ka Wei, Ng, Wei Lin, Kawai, Tatsuya
Format: Artikel
Sprache:eng
Schlagworte:
Aged
Bayes Theorem
Biopsy, Needle
Deep Learning
Female
Humans
Image-Guided Biopsy - methods
Lung - diagnostic imaging
Lung - pathology
Lung Neoplasms - diagnostic imaging
Lung Neoplasms - pathology
Male
Middle Aged
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted
Reproducibility of Results
Retrospective Studies
Software
Tomography, X-Ray Computed
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:To validate the sensitivity and specificity of a 3-dimensional (3D) convolutional neural network (CNN) artificial intelligence (AI) software for lung lesion detection and to establish concordance between AI-generated needle paths and those used in actual biopsy procedures. This was a retrospective study using computed tomography (CT) scans from 3 hospitals. Inclusion criteria were scans with 1–5 nodules of diameter ≥5 mm; exclusion criteria were poor-quality scans or those with nodules measuring
ISSN:1051-0443
1535-7732
DOI:10.1016/j.jvir.2024.02.006