Sensitivity of Computer-Aided Detection (CAD) Device for Lung Nodule Detection on Chest Radiography: A Real-Life Application

Purpose: The radiographic diagnosis of lung nodules is associated with low sensitivity and specificity. Computer-aided detection (CAD) system has been shown to have higher accuracy in the detection of lung nodules. The purpose of this study is to assess the effect on sensitivity and specificity when a CAD system is used to review chest radiographs in real-time setting. Methods: Sixty-three patients, including 24 controls, who had chest radiographs and CT within three months were included in this study. Three radiologists were presented chest radiographs without CAD and were asked to mark all lung nodules. Then the radiologists were allowed to see the CAD region-of-interest (ROI) marks and were asked to agree or disagree with the marks. All marks were correlated with CT studies. Results: The mean sensitivity of the three radiologists without CAD was 16.1%, which showed a statistically significant improvement to 22.5% with CAD. The mean specificity of the three radiologists was 52.5% without CAD and decreased to 48.1% with CAD. There was no significant change in the positive predictive value or negative predictive value. Conclusion: The addition of a CAD system to chest radiography interpretation statistically improves the detection of lung nodules without affecting its specificity. Thus suggesting CAD would improve overall detection of lung nodules.

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Lung Nodule Segmentation Using Mathematical Morphology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.58-60.1378 ◽

2011 ◽

Vol 58-60 ◽

pp. 1378-1383

Author(s):

Ming Zhi Qu ◽

Gui Rong Weng

Keyword(s):

Mathematical Morphology ◽

Lung Nodule ◽

Lung Nodules ◽

Computer Aided Detection ◽

Cad System ◽

Multi Scale ◽

Morphological Segmentation ◽

Computer Aided ◽

Ct Technology ◽

Detection And Diagnosis

Contemporary computed tomography (CT) technology offers the better potential of screening for the early detection of lung cancer than the traditional x-ray chest radiographs. In order to help improve radiologists’ diagnostic performance and efficiency, many researchers propose to develop computer-aided detection and diagnosis (CAD) system for the detection and characterization of lung nodules depicted on CT images and to evaluate its potentially clinical utility in assisting radiologists. Based on review of computer-aided detection and diagnosis of lung nodules using CT at home and abroad in recent years, this paper presented a new algorithm that achieves an automated way for applying multi-scale nodule enhancement, mathematical morphology and morphological Segmentation.

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Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning

European Radiology ◽

10.1007/s00330-020-06892-9 ◽

2020 ◽

Vol 30 (9) ◽

pp. 4943-4951

Author(s):

Young-Gon Kim ◽

Sang Min Lee ◽

Kyung Hee Lee ◽

Ryoungwoo Jang ◽

Joon Beom Seo ◽

...

Keyword(s):

Deep Learning ◽

Lung Nodule ◽

Chest Radiographs ◽

Computer Aided Detection ◽

Matrix Size ◽

Computer Aided

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Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190206153321 ◽

2020 ◽

Vol 16 (1) ◽

pp. 16-26 ◽

Cited By ~ 1

Author(s):

Shabana Rasheed Ziyad ◽

Venkatachalam Radha ◽

Thavavel Vayyapuri

Keyword(s):

Computed Tomography ◽

Review Paper ◽

Lung Nodule ◽

Lung Nodules ◽

Computer Aided Detection ◽

Lung Nodule Detection ◽

Nodule Detection ◽

Computer Aided ◽

Aided Diagnosis

Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

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Catheter detection and classification on chest radiographs: an automated prototype computer-aided detection (CAD) system for radiologists

10.1117/12.878172 ◽

2011 ◽

Cited By ~ 4

Author(s):

Bharath Ramakrishna ◽

Matthew Brown ◽

Jonathan Goldin ◽

Chris Cagnon ◽

Dieter Enzmann

Keyword(s):

Chest Radiographs ◽

Computer Aided Detection ◽

Cad System ◽

Computer Aided

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Deep learning computer-aided detection system for pneumonia in febrile neutropenia patients: a diagnostic cohort study

BMC Pulmonary Medicine ◽

10.1186/s12890-021-01768-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Eui Jin Hwang ◽

Jong Hyuk Lee ◽

Jae Hyun Kim ◽

Woo Hyeon Lim ◽

Jin Mo Goo ◽

...

Keyword(s):

Deep Learning ◽

Body Temperature ◽

Febrile Neutropenia ◽

Sensitivity And Specificity ◽

Detection System ◽

Characteristic Curve ◽

Computer Aided Detection ◽

Tertiary Referral Hospital ◽

Cad System ◽

Computer Aided

Abstract Background Diagnosis of pneumonia is critical in managing patients with febrile neutropenia (FN), however, chest X-ray (CXR) has limited performance in the detection of pneumonia. We aimed to evaluate the performance of a deep learning-based computer-aided detection (CAD) system in pneumonia detection in the CXRs of consecutive FN patients and investigated whether CAD could improve radiologists’ diagnostic performance when used as a second reader. Methods CXRs of patients with FN (a body temperature ≥ 38.3 °C, or a sustained body temperature ≥ 38.0 °C for an hour; absolute neutrophil count < 500/mm3) obtained between January and December 2017 were consecutively included, from a single tertiary referral hospital. Reference standards for the diagnosis of pneumonia were defined by consensus of two thoracic radiologists after reviewing medical records and CXRs. A commercialized, deep learning-based CAD system was retrospectively applied to detect pulmonary infiltrates on CXRs. For comparing performance, five radiologists independently interpreted CXRs initially without the CAD results (radiologist-alone interpretation), followed by the interpretation with CAD. The sensitivities and specificities for detection of pneumonia were compared between radiologist-alone interpretation and interpretation with CAD. The standalone performance of the CAD was also evaluated, using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Moreover, sensitivity and specificity of standalone CAD were compared with those of radiologist-alone interpretation. Results Among 525 CXRs from 413 patients (52.3% men; median age 59 years), pneumonia was diagnosed in 128 (24.4%) CXRs. In the interpretation with CAD, average sensitivity of radiologists was significantly improved (75.4% to 79.4%, P = 0.003) while their specificity remained similar (75.4% to 76.8%, P = 0.101), compared to radiologist-alone interpretation. The CAD exhibited AUC, sensitivity, and specificity of 0.895, 88.3%, and 68.3%, respectively. The standalone CAD exhibited higher sensitivity (86.6% vs. 75.2%, P < 0.001) and lower specificity (64.8% vs. 75.4%, P < 0.001) compared to radiologist-alone interpretation. Conclusions In patients with FN, the deep learning-based CAD system exhibited radiologist-level performance in detecting pneumonia on CXRs and enhanced radiologists’ performance.

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