Academic radiology · 2026
AI-Based Denoising for Simulated Dose Reduction in Pediatric Chest Radiography: A Prospective Multicenter Evaluation of Image Quality and Diagnostic Performance.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Shakhreet Bassam Z
Аннотация
RATIONALE AND OBJECTIVES: Radiation dose reduction in pediatric chest radiography is a clinical priority due to increased radiosensitivity and cumulative lifetime risk. However, substantial dose reduction may degrade image quality and compromise diagnostic performance. Artificial intelligence (AI)-based denoising has emerged as a potential solution, although robust prospective multicenter validation remains limited. To prospectively evaluate whether a vendor-neutral deep learning-based denoising system improves image quality while assessing the magnitude of change in diagnostic performance in simulated low-dose pediatric chest radiographs at approximately 50% dose reduction.
MATERIALS AND METHODS: In this prospective multicenter study, 420 pediatric patients undergoing clinically indicated chest radiography were enrolled across four tertiary hospitals. Simulated low-dose images (50% for all patients; exploratory 70% subset) were generated using a validated physics-informed Poisson-Gaussian noise-insertion framework. Images were processed using a deep residual convolutional neural network (CNN). Three blinded pediatric radiologists independently assessed image quality and diagnostic performance for pneumonia, atelectasis, and cardiomegaly. Quantitative metrics and receiver operating characteristic (ROC) analysis with DeLong's method were performed. Non-inferiority was tested using a prespecified ΔAUC margin of -0.05.
RESULTS: AI denoising improved perceived image quality, with mean scores approaching those of full-dose imaging (4.4 ± 0.5 vs 4.5 ± 0.4). Quantitative metrics demonstrated preservation of structural similarity and noise characteristics. Although the absolute difference in diagnostic performance was small (ΔAUC = -0.03), the confidence interval (CI) crossed the prespecified non-inferiority margin; therefore, formal non-inferiority was not established.
CONCLUSION: AI-based denoising improves image quality in simulated low-dose pediatric chest radiography. Although the absolute difference in diagnostic performance was small (ΔAUC = -0.03), the CI crossed the prespecified non-inferiority margin; therefore, formal non-inferiority was not established. These findings support further prospective validation using clinically acquired low-dose data before clinical equivalence can be assumed.
MATERIALS AND METHODS: In this prospective multicenter study, 420 pediatric patients undergoing clinically indicated chest radiography were enrolled across four tertiary hospitals. Simulated low-dose images (50% for all patients; exploratory 70% subset) were generated using a validated physics-informed Poisson-Gaussian noise-insertion framework. Images were processed using a deep residual convolutional neural network (CNN). Three blinded pediatric radiologists independently assessed image quality and diagnostic performance for pneumonia, atelectasis, and cardiomegaly. Quantitative metrics and receiver operating characteristic (ROC) analysis with DeLong's method were performed. Non-inferiority was tested using a prespecified ΔAUC margin of -0.05.
RESULTS: AI denoising improved perceived image quality, with mean scores approaching those of full-dose imaging (4.4 ± 0.5 vs 4.5 ± 0.4). Quantitative metrics demonstrated preservation of structural similarity and noise characteristics. Although the absolute difference in diagnostic performance was small (ΔAUC = -0.03), the confidence interval (CI) crossed the prespecified non-inferiority margin; therefore, formal non-inferiority was not established.
CONCLUSION: AI-based denoising improves image quality in simulated low-dose pediatric chest radiography. Although the absolute difference in diagnostic performance was small (ΔAUC = -0.03), the CI crossed the prespecified non-inferiority margin; therefore, formal non-inferiority was not established. These findings support further prospective validation using clinically acquired low-dose data before clinical equivalence can be assumed.