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<p>Bronchopulmonary dysplasia (BPD) in preterm infants is a major concern in neonatal intensive care, necessitating early and accurate detection for improved outcomes. Despite the use of clinical information in previous studies to assess BPD severity, there is a gap in early prediction through imaging techniques. This paper proposed a novel method using convolutional neural networks (CNNs) for the early prediction of BPD from neonatal chest X-rays. We employed two strategies: first, analyzing chest X-ray images taken on specific days post-birth to evaluate day-wise BPD predictive performance using CNN models; and second, aggregating these images to enhance the training dataset. Thirteen specific CNN architectures were evaluated using a five-fold cross-validation method on a dataset acquired at four distinct time points: 3, 7, 14, and 28-days post-birth. The dataset included 115 preterm infants, 51 with BPD and 64 normal, classified based on their condition at 36&#xa0;weeks of post-menstrual age. MobileNetV2 demonstrated consistent and fairly above-moderate performance among the networks used, with calculated metrics showing an accuracy of 0.665 ± 0.045, an AUC of 0.736 ± 0.053, a recall of 0.635 ± 0.042, a precision of 0.647 ± 0.046, and an F1-score of 0.641 ± 0.042. The results highlight the potential of CNNs in enhancing early diagnostic accuracy for BPD in neonatal patients using chest X-ray images.</p>

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Early prediction of bronchopulmonary dysplasia in preterm infants using chest X-rays through a comparative analysis of 13 CNN models across different post-birth days

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月26日

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  • Md Anas Ali,
  • Ryunosuke Maeda,
  • Daisuke Fujita,
  • Naoyuki Miyahara,
  • Fumihiko Namba,
  • Syoji Kobashi

摘要

Bronchopulmonary dysplasia (BPD) in preterm infants is a major concern in neonatal intensive care, necessitating early and accurate detection for improved outcomes. Despite the use of clinical information in previous studies to assess BPD severity, there is a gap in early prediction through imaging techniques. This paper proposed a novel method using convolutional neural networks (CNNs) for the early prediction of BPD from neonatal chest X-rays. We employed two strategies: first, analyzing chest X-ray images taken on specific days post-birth to evaluate day-wise BPD predictive performance using CNN models; and second, aggregating these images to enhance the training dataset. Thirteen specific CNN architectures were evaluated using a five-fold cross-validation method on a dataset acquired at four distinct time points: 3, 7, 14, and 28-days post-birth. The dataset included 115 preterm infants, 51 with BPD and 64 normal, classified based on their condition at 36 weeks of post-menstrual age. MobileNetV2 demonstrated consistent and fairly above-moderate performance among the networks used, with calculated metrics showing an accuracy of 0.665 ± 0.045, an AUC of 0.736 ± 0.053, a recall of 0.635 ± 0.042, a precision of 0.647 ± 0.046, and an F1-score of 0.641 ± 0.042. The results highlight the potential of CNNs in enhancing early diagnostic accuracy for BPD in neonatal patients using chest X-ray images.