Purpose : Chest radiography is the first-line imaging modality for diagnosing thoracic pathologies. Diagnostic accuracy may be drastically reduced due to technical limitations resulting in poor image quality and thus leading to incorrect diagnosis.
Methods : We collected 4481 frontal Chest X-rays (CXRs), performed in the supine or standing position, labeled by 7 radiologists from the radiology department of Cochin University hospital (AP-HP, Paris,France). Frontal CXR quality criteria included full anatomical coverage, lack of rotation or scapula projection, deep inspiration and optimal exposure. A deep convolutional neural network was trained to classify CXRs as technically correct or incorrect. The model predictions were compared to a ground truth set (15%), labeled by 2 chest radiologist experts.
Results : There were no signifiant differences regarding age, sex, and classification for each criterion between the test, validation and training datasets (p>0,05).We first evaluated the inter-rater reliability and found good correlation for each criterion (Cohen’s kappa > 0,6). The model performance was evaluated and compared to that of each observer with expert annotation as ground truth. The model performance was close to the radiologists' performance, (accuracies 80-90% compared to 83-95% for radiologists) excepted for the rotation criteria.
Conclusion : The trained model to detect quality-deficient CXRs could be used by technologists in real-time to ensure high-quality images.

**Purpose :** Chest radiography is the first-line imaging modality for diagnosing thoracic pathologies. Diagnostic accuracy may be drastically reduced due to technical limitations resulting in poor image quality and thus leading to incorrect diagnosis.
**Methods :** We collected 4481 frontal Chest X-rays (CXRs), performed in the supine or standing position, labeled by 7 radiologists from the radiology department of Cochin University hospital (AP-HP, Paris,France). Frontal CXR quality criteria included full anatomical coverage, lack of rotation or scapula projection, deep inspiration and optimal exposure. A deep convolutional neural network was trained to classify CXRs as technically correct or incorrect. The model predictions were compared to a ground truth set (15%), labeled by 2 chest radiologist experts.
**Results :** There were no signifiant differences regarding age, sex, and classification for each criterion between the test, validation and training datasets (p>0,05).We first evaluated the inter-rater reliability and found good correlation for each criterion (Cohen’s kappa > 0,6). The model performance was evaluated and compared to that of each observer with expert annotation as ground truth. The model performance was close to the radiologists' performance, (accuracies 80-90% compared to 83-95% for radiologists) excepted for the rotation criteria.
**Conclusion :** The trained model to detect quality-deficient CXRs could be used by technologists in real-time to ensure high-quality images.

Development of an AI-based model for Chest X-ray quality assessment

[Networking hour](https://app.wonder.me/?spaceId=fe093845-759f-448f-9f55-01d3adccd820)
[COIs](https://drive.google.com/drive/folders/1nbfwAa9lf9Kz-wAZ7iLKhfpBFjpN_gkn?usp=sharing)