Cargando…

Computer-Aided Detection of Malignant Lung Nodules on Chest Radiographs: Effect on Observers' Performance

OBJECTIVE: To evaluate the effect of computer-aided detection (CAD) system on observer performance in the detection of malignant lung nodules on chest radiograph. MATERIALS AND METHODS: Two hundred chest radiographs (100 normal and 100 abnormal with malignant solitary lung nodules) were evaluated. W...

Descripción completa

Detalles Bibliográficos
Autores principales:	Lee, Kyung Hee, Goo, Jin Mo, Park, Chang Min, Lee, Hyun Ju, Jin, Kwang Nam
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	The Korean Society of Radiology 2012
Materias:	Original Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3435853/ https://www.ncbi.nlm.nih.gov/pubmed/22977323 http://dx.doi.org/10.3348/kjr.2012.13.5.564

Ejemplares similares

Computer-Aided Detection of Lung Nodules on Chest CT: Issues to be Solved before Clinical Use
por: Goo, Jin Mo
Publicado: (2005)

A Computer-Aided Diagnosis for Evaluating Lung Nodules on Chest CT: the Current Status and Perspective
por: Goo, Jin Mo
Publicado: (2011)

Computer-aided Detection Fidelity of Pulmonary Nodules in Chest Radiograph
por: Dellios, Nikolaos, et al.
Publicado: (2017)

Effects of Implementing Artificial Intelligence-Based Computer-Aided Detection for Chest Radiographs in Daily Practice on the Rate of Referral to Chest Computed Tomography in Pulmonology Outpatient Clinic
por: Hong, Wonju, et al.
Publicado: (2023)

Methods of Visualizing the Results of an Artificial-Intelligence-Based Computer-Aided Detection System for Chest Radiographs: Effect on the Diagnostic Performance of Radiologists
por: Hong, Sungho, et al.
Publicado: (2023)

Extension of Coronavirus Disease 2019 on Chest CT and Implications for Chest Radiographic Interpretation
por: Choi, Hyewon, et al.
Publicado: (2020)

Observer training for computer-aided detection of pulmonary nodules in chest radiography
por: De Boo, Diederick W., et al.
Publicado: (2012)

Erratum: Extension of Coronavirus Disease 2019 (COVID-19) on Chest CT and Implications for Chest Radiograph Interpretation
por: Choi, Hyewon, et al.
Publicado: (2020)

Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19
por: Hwang, Eui Jin, et al.
Publicado: (2020)

COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system
por: Hwang, Eui Jin, et al.
Publicado: (2021)

Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs: Case–control study
por: Choi, Soo Yun, et al.
Publicado: (2021)

Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs
por: Nam, Ju Gang, et al.
Publicado: (2021)

Clinical outcomes and actual consequence of lung nodules incidentally detected on chest radiographs by artificial intelligence
por: Hwang, Shin Hye, et al.
Publicado: (2023)

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study
por: Lee, Hyun Woo, et al.
Publicado: (2023)

Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning
por: Lee, Mu Sook, et al.
Publicado: (2021)

Short-term Reproducibility of Pulmonary Nodule and Mass Detection in Chest Radiographs: Comparison among Radiologists and Four Different Computer-Aided Detections with Convolutional Neural Net
por: Kim, Young-Gon, et al.
Publicado: (2019)

A Computer-Aided Detection System for Digital Chest Radiographs
por: Carrillo-de-Gea, Juan Manuel, et al.
Publicado: (2016)

Correction: Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study
por: Lee, Hyun Woo, et al.
Publicado: (2023)

Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs
por: Yoo, Hyunsuk, et al.
Publicado: (2020)

Development and Validation of a Deep Learning–based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs
por: Hwang, Eui Jin, et al.
Publicado: (2019)

Chest Radiographic Findings in Primary Pulmonary Tuberculosis: Observations from High School Outbreaks
por: Koh, Won-Jung, et al.
Publicado: (2010)

Effects of Expert-Determined Reference Standards in Evaluating the Diagnostic Performance of a Deep Learning Model: A Malignant Lung Nodule Detection Task on Chest Radiographs
por: Huh, Jung Eun, et al.
Publicado: (2023)

Miliary nodules on chest radiographs: A diagnostic dilemma
por: Sharma, Bharat Bhushan
Publicado: (2015)

Lung Nodule Detection on Chest CT: Evaluation of a Computer-Aided Detection (CAD) System
por: Lee, In Jae, et al.
Publicado: (2005)

Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs
por: Hwang, Eui Jin, et al.
Publicado: (2019)

Risk factors associated with an increase in the size of ground‐glass lung nodules on chest computed tomography
por: Yoon, Hee‐Young, et al.
Publicado: (2019)

Review: On Segmentation of Nodules from Posterior and Anterior Chest Radiographs
por: Chaya Devi, S. K., et al.
Publicado: (2018)

Development and Validation of a Deep Learning–Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs
por: Kim, Hwiyoung, et al.
Publicado: (2023)

Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial
por: Hwang, Eui Jin, et al.
Publicado: (2023)

Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules
por: Wang, Hui, et al.
Publicado: (2022)

Association of Artificial Intelligence–Aided Chest Radiograph Interpretation With Reader Performance and Efficiency
por: Ahn, Jong Seok, et al.
Publicado: (2022)

Parapneumonic empyema diagnosed by chest radiograph and computed tomography
por: Jin, Ning, et al.
Publicado: (2013)

Clinical significance of abnormal chest radiographic findings for acute kidney injury in patients with scrub typhus
por: Yang, Hyun Ju, et al.
Publicado: (2020)

Computer-aided assessment of regional vascularity of thyroid nodules for prediction of malignancy
por: Baig, Faisal N., et al.
Publicado: (2017)

Pulmonary Nodule Detection in Patients with a Primary Malignancy Using Hybrid PET/MRI: Is There Value in Adding Contrast-Enhanced MR Imaging?
por: Lee, Kyung Hee, et al.
Publicado: (2015)

Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographs
por: Yoo, Seung-Jin, et al.
Publicado: (2023)

Digital Tomosynthesis for Evaluating Metastatic Lung Nodules: Nodule Visibility, Learning Curves, and Reading Times
por: Lee, Kyung Hee, et al.
Publicado: (2015)

Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT
por: Yoon, Soon Ho, et al.
Publicado: (2014)

Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study
por: Jang, Se Bum, et al.
Publicado: (2020)

A systematic approach to deep learning-based nodule detection in chest radiographs
por: Behrendt, Finn, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_mepnuva585hpomcduka0neqh7q