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Published on: December 19, 2020
Development and validation of a deep learning-based algorithm for quantifying bronchiolitis obliterans in paediatric
Chanyoung Rhee1, Jae-Yeon Hwang1, Ji Young Ha2
1Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
Insights
A deep learning algorithm accurately quantifies bronchiolitis obliterans (BO) on pediatric CT scans. This AI tool shows robust performance and strong agreement with radiologist assessments for precise BO measurement.
Area of Science:
- Artificial Intelligence in Medical Imaging
- Pediatric Radiology
- Pulmonary Disease Quantification
Background:
- Bronchiolitis obliterans (BO) is a serious complication following pediatric lung transplantation and other conditions.
- Accurate quantification of BO on CT scans is crucial for patient management and treatment assessment.
- Current quantification methods can be subjective and time-consuming.
Purpose of the Study:
- To develop and validate a deep learning (DL) algorithm for objective quantification of BO on pediatric chest CT.
- To assess the robustness and accuracy of the DL model against radiologist segmentations and grading.
Main Methods:
- A retrospective study utilized 86 pediatric chest CT scans from patients diagnosed with BO.
- A 3D nnU-Net deep learning model was trained on radiologist-segmented low attenuation regions (LARs) as ground truth.
- Model performance was evaluated using internal and external test sets, intra-vendor robustness testing, and comparison with semi-quantitative radiologist grading.
Main Results:
- The DL model achieved high performance metrics, including Dice Similarity Coefficients (DSC) of 85.41% (internal) and 82.53% (external).
- The algorithm demonstrated robustness across varying CT acquisition parameters (reconstruction methods, kernel types, slice thicknesses).
- Strong to very strong correlations were observed between the DL model's quantification and radiologist grading across all lung lobes (p < 0.001).
Conclusions:
- The developed 3D nnU-Net deep learning algorithm provides accurate and reproducible quantification of BO on pediatric CT.
- The model shows good agreement with radiologist-segmented ground truth, offering a reliable tool for clinical assessment.
- This AI-driven approach enhances the objective measurement of low attenuation regions (LARs) in pediatric BO.
Objectives:
To develop and validate a deep learning-based algorithm for quantifying bronchiolitis obliterans (BO) on paediatric chest CT.
Methods:
This retrospective study included 86 children (39 males; median age, 10 years) diagnosed with BO who underwent both inspiratory and expiratory CT between January 2018 and November 2021. The deep learning-based BO quantification model was trained on 26 CT scans using a 3D nnU-Net, with radiologist-segmented low attenuation regions (LARs) serving as ground truth. Model performance was evaluated through internal test with 4 CT scans and external test with 6 CT scans. Intra-vendor robustness was assessed using 22 CT scans with varying reconstruction methods, kernel types, and slice thicknesses. Comparison with semiquantitative radiologist grading was performed using 28 CT scans. Dice similarity coefficient (DSC), sensitivity, and precision were used to evaluate model performance.
Results:
The model achieved a DSC of 85.41 ± 3.28%, sensitivity of 85.14 ± 7.66%, and precision of 86.21 ± 3.92% in the internal test, and 82.53 ± 4.34%, 82.17 ± 6.15%, and 84.15 ± 3.16% in the external test, respectively. For intra-vendor robustness, no significant differences in BO quantification were observed across different reconstruction methods, kernel types, and slice thicknesses (all P > .05). Compared to radiologists' grading, the model demonstrated strong to very strong correlations across all lung lobes (all P < .001).
Conclusion:
The model demonstrated accurate quantification of BO on paediatric CT, with good agreement with the radiologist-segmented ground truth.
Advances In Knowledge:
This study presents a 3D nnU-Net-based deep learning algorithm for robust quantification of BO on paediatric CT, providing reproducible measurements of LARs.
