标题： 基于逐层冻结的站点级微调：从极早产婴儿出生第一天胸部X光片中稳健预测支气管肺发育不良 显示英文标题

标题： Site-Level Fine-Tuning with Progressive Layer Freezing: Towards Robust Prediction of Bronchopulmonary Dysplasia from Day-1 Chest Radiographs in Extremely Preterm Infants

摘要： 支气管肺发育不良（BPD）是一种慢性肺部疾病，影响35%的极低出生体重婴儿。 在胎龄36周时仍需要吸氧来定义，它会导致终身的呼吸系统并发症。 然而，预防性干预措施具有严重的风险，包括神经发育障碍、呼吸机引起的肺损伤和全身性并发症。 因此，早期BPD预后和BPD结果预测对于避免低风险婴儿不必要的毒性非常重要。 极早产婴儿入院时的X光片通常在出生后24小时内获取，并可作为非侵入性的预后工具。 在这项工作中，我们开发并研究了一种深度学习方法，使用来自163名极低出生体重婴儿（$\leq$32周妊娠，401-999g）的胸部X光片，在出生后24小时内获取。 我们对专门针对成人胸部X光片预训练的ResNet-50进行了微调，采用渐进式层冻结和区分性学习率以防止过拟合，并评估了CutMix增强和线性探测。 对于中度/重度BPD结果预测，我们的最佳模型通过渐进式冻结、线性探测和CutMix实现了AUROC为0.78$\pm$0.10，平衡准确率为0.69$\pm$0.10，F1分数为0.67$\pm$0.11。 领域内预训练显著优于ImageNet初始化（p = 0.031），这证实了领域特定预训练对于BPD结果预测的重要性。 常规IRDS等级显示预后价值有限（AUROC 0.57$\pm$0.11），确认了学习特征的必要性。 我们的方法表明，领域特定预训练使得从常规出生第一天的X光片中准确预测BPD成为可能。 通过渐进式冻结和线性探测，该方法在站点级别实施和未来联邦学习部署中保持计算可行性。 显示英文摘要

摘要： Bronchopulmonary dysplasia (BPD) is a chronic lung disease affecting 35% of extremely low birth weight infants. Defined by oxygen dependence at 36 weeks postmenstrual age, it causes lifelong respiratory complications. However, preventive interventions carry severe risks, including neurodevelopmental impairment, ventilator-induced lung injury, and systemic complications. Therefore, early BPD prognosis and prediction of BPD outcome is crucial to avoid unnecessary toxicity in low risk infants. Admission radiographs of extremely preterm infants are routinely acquired within 24h of life and could serve as a non-invasive prognostic tool. In this work, we developed and investigated a deep learning approach using chest X-rays from 163 extremely low-birth-weight infants ($\leq$32 weeks gestation, 401-999g) obtained within 24 hours of birth. We fine-tuned a ResNet-50 pretrained specifically on adult chest radiographs, employing progressive layer freezing with discriminative learning rates to prevent overfitting and evaluated a CutMix augmentation and linear probing. For moderate/severe BPD outcome prediction, our best performing model with progressive freezing, linear probing and CutMix achieved an AUROC of 0.78 $\pm$ 0.10, balanced accuracy of 0.69 $\pm$ 0.10, and an F1-score of 0.67 $\pm$ 0.11. In-domain pre-training significantly outperformed ImageNet initialization (p = 0.031) which confirms domain-specific pretraining to be important for BPD outcome prediction. Routine IRDS grades showed limited prognostic value (AUROC 0.57 $\pm$ 0.11), confirming the need of learned markers. Our approach demonstrates that domain-specific pretraining enables accurate BPD prediction from routine day-1 radiographs. Through progressive freezing and linear probing, the method remains computationally feasible for site-level implementation and future federated learning deployments.