Transfer learning in spirometry: CNN models for automated flow-volume curve quality control in paediatric populations

Abstract

Problem: Current spirometers face challenges in evaluating acceptability criteria, often requiring manual visual inspection by trained specialists. Automating this process could improve diagnostic workflows and reduce variability in test assessments.

Aim: This study aimed to apply transfer learning to convolutional neural networks (CNNs) to automate the classification of spirometry flow-volume curves based on acceptability criteria.

Methods: A total of 5287 spirometry flow-volume curves were divided into three categories: (A) all criteria met, (B) early termination, and (C) non-acceptable results. Six CNN models (VGG16, InceptionV3, Xception, ResNet152V2, InceptionResNetV2, DenseNet121) were trained using a balanced dataset after data augmentation. The models' performance was evaluated on part of the original unbalanced dataset with accuracy, precision, recall, and F1-score metrics.

Results: VGG16 achieved the highest accuracy at 93.9 %, while ResNet152V2 had the lowest at 83.0 %. Non-acceptable curves (Group C) were the easiest to classify, with precision reaching at least 87.7 %. Early termination curves (Group B) were the most challenging, with precision ranging from 75.0 % to 90.3 %.

Conclusion: CNN models, particularly VGG16, show promise in automating spirometry quality control, potentially reducing the need for manual inspection by specialized technicians. This approach can streamline spirometry assessments, offering consistent, high-quality diagnostics even in non-specialized or low-resource environments.