Diagnostic performance of chest radiography measurements for the assessment of cardiac chamber enlargement

Abstract Objectives We aim ed to evaluate a commercial artificial intelligence (AI) solution on a multicenter cohort of chest radiographs and to compare physicians' ability to detect and localize referable thoracic abnormalities with and without AI assistance. Methods In this retrospective diagnostic cohort study, we investigated 6,006 consecutive patients who underwent both chest radiography and CT. We evaluated a commercially available AI solution intended to facilitate the detection of three chest abnormalities (nodule/masses, consolidation, and pneumothorax) against a reference standard to measure its diagnostic performance. Moreover, twelve physicians, including thoracic radiologists, board-certified radiologists, radiology residents, and pulmonologists, assessed a dataset of 230 randomly sampled chest radiographic images. The images were reviewed twice per physician, with and without AI, with a 4-week washout period. We measured the impact of AI assistance on observer's AUC, sensitivity, specificity, and the area under the alternative free-response ROC (AUAFROC). Results In the entire set (n = 6,006), the AI solution showed average sensitivity, specificity, and AUC of 0.885, 0.723, and 0.867, respectively. In the test dataset (n = 230), the average AUC and AUAFROC across observers significantly increased with AI assistance (from 0.861 to 0.886; p = 0.003 and from 0.797 to 0.822; p = 0.003, respectively). Conclusions The diagnostic performance of the AI solution was found to be acceptable for the images from respiratory outpatient clinics. The diagnostic performance of physicians marginally improved with the use of AI solutions. Further evaluation of AI assistance for chest radiographs using a prospective design is required to prove the efficacy of AI assistance. Key Points • AI assistance for chest radiographs marginally improved physicians’ performance in detecting and localizing referable thoracic abnormalities on chest radiographs. • The detection or localization of referable thoracic abnormalities by pulmonologists and radiology residents improved with the use of AI assistance.

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Diagnostic performance of artificial intelligence model for pneumonia from chest radiography

PLoS ONE ◽

10.1371/journal.pone.0249399 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0249399

Author(s):

TaeWoo Kwon ◽

Sang Pyo Lee ◽

Dongmin Kim ◽

Jinseong Jang ◽

Myungjae Lee ◽

...

Keyword(s):

Artificial Intelligence ◽

Chest Radiography ◽

Diagnostic Performance ◽

University Hospital ◽

Ensemble Model ◽

Classification Models ◽

Clinical Prognosis ◽

Probability Score ◽

Significant Difference

Objective The chest X-ray (CXR) is the most readily available and common imaging modality for the assessment of pneumonia. However, detecting pneumonia from chest radiography is a challenging task, even for experienced radiologists. An artificial intelligence (AI) model might help to diagnose pneumonia from CXR more quickly and accurately. We aim to develop an AI model for pneumonia from CXR images and to evaluate diagnostic performance with external dataset. Methods To train the pneumonia model, a total of 157,016 CXR images from the National Institutes of Health (NIH) and the Korean National Tuberculosis Association (KNTA) were used (normal vs. pneumonia = 120,722 vs.36,294). An ensemble model of two neural networks with DenseNet classifies each CXR image into pneumonia or not. To test the accuracy of the models, a separate external dataset of pneumonia CXR images (n = 212) from a tertiary university hospital (Gachon University Gil Medical Center GUGMC, Incheon, South Korea) was used; the diagnosis of pneumonia was based on both the chest CT findings and clinical information, and the performance evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, we tested the change of the AI probability score for pneumonia using the follow-up CXR images (7 days after the diagnosis of pneumonia, n = 100). Results When the probability scores of the models that have a threshold of 0.5 for pneumonia, two models (models 1 and 4) having different pre-processing parameters on the histogram equalization distribution showed best AUC performances of 0.973 and 0.960, respectively. As expected, the ensemble model of these two models performed better than each of the classification models with 0.983 AUC. Furthermore, the AI probability score change for pneumonia showed a significant difference between improved cases and aggravated cases (Δ = -0.06 ± 0.14 vs. 0.06 ± 0.09, for 85 improved cases and 15 aggravated cases, respectively, P = 0.001) for CXR taken as a 7-day follow-up. Conclusions The ensemble model combined two different classification models for pneumonia that performed at 0.983 AUC for an external test dataset from a completely different data source. Furthermore, AI probability scores showed significant changes between cases of different clinical prognosis, which suggest the possibility of increased efficiency and performance of the CXR reading at the diagnosis and follow-up evaluation for pneumonia.

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Diagnostic Performance of a Flat-Panel Detector at Low Tube Voltage in Chest Radiography

Investigative Radiology ◽

10.1097/01.rli.0000110566.23154.81 ◽

2004 ◽

Vol 39 (2) ◽

pp. 97-103 ◽

Cited By ~ 10

Author(s):

Thomas M. Bernhardt ◽

Ulrike Rapp-Bernhardt ◽

Horst Lenzen ◽

Friedrich W. Röhl ◽

Stefan Diederich ◽

...

Keyword(s):

Chest Radiography ◽

Diagnostic Performance ◽

Flat Panel ◽

Tube Voltage ◽

Flat Panel Detector ◽

Low Tube Voltage

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Correction: Diagnostic performance of congestion score index evaluated from chest radiography for acute heart failure in the emergency department: A retrospective analysis from the PARADISE cohort

PLoS Medicine ◽

10.1371/journal.pmed.1003562 ◽

2021 ◽

Vol 18 (3) ◽

pp. e1003562

Author(s):

Masatake Kobayashi ◽

Amine Douair ◽

Kevin Duarte ◽

Déborah Jaeger ◽

Gaetan Giacomin ◽

...

Keyword(s):

Heart Failure ◽

Emergency Department ◽

Retrospective Analysis ◽

Acute Heart Failure ◽

Chest Radiography ◽

Diagnostic Performance ◽

Score Index

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Large-Image Intensifier Photofluorography and Conventional Radiography in Pulmonary Emphysema

Acta Radiologica ◽

10.1177/028418518802900307 ◽

1988 ◽

Vol 29 (3) ◽

pp. 293-297

Author(s):

H. Manninen ◽

K. Partanen ◽

S. Soimakallio ◽

H. Rytkönen

Keyword(s):

Computed Tomography ◽

Chest Radiography ◽

Pulmonary Emphysema ◽

Diagnostic Performance ◽

False Negative ◽

Image Intensifier ◽

Conventional Radiography ◽

Full Size ◽

Radiologic Evaluation ◽

The Difference

Large-screen image intensifier (II) photofluorography was compared with full-size screen-film chest radiography in the diagnosis of pulmonary emphysema in 84 patients. Photospot films and conventional radiographs were interpreted independently by three radiologists. Computed tomography (CT) was used as an independent reference technique, and diagnostic performance of chest radiography in various CT patterns of emphysema was evaluated. The difference in diagnostic sensitivity for emphysema in favor of conventional chest radiography over photofluorography (0.65 versus 0.56) was statistically significant (p<0.05). Specificity of the imaging modalities was equal: 0.78 in full-size films and 0.77 in photospot films. All CT patterns of emphysema had great false negative response rates in chest radiography, which is an inaccurate technique for the diagnosis of emphysema. CT is required for reliable radiologic evaluation of emphysema.

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