Ozcelik Gozde, Erol Sara, Korkut Sabriye, Kose Cetinkaya Aslihan, Ozcelik Halil
Department of Pediatrics, Ankara Bilkent City Hospital, Ankara, Turkey.
Division of Neonatology, Department of Pediatrics, Ankara Bilkent City Hospital, Ankara, Turkey.
Medicine (Baltimore). 2025 Sep 5;104(36):e44322. doi: 10.1097/MD.0000000000044322.
Bronchopulmonary dysplasia (BPD) is a significant morbidity in premature infants. This study aimed to assess the accuracy of the model's predictions in comparison to clinical outcomes. Medical records of premature infants born ≤ 28 weeks and < 1250 g between January 1, 2020, and December 31, 2021, in the neonatal intensive care unit were obtained. In this retrospective model development and validation study, an artificial intelligence model was developed using DenseNet121 deep learning architecture. The data set and test set consisted of chest radiographs obtained on postnatal day 1 as well as during the 2nd, 3rd, and 4th weeks. The model predicted the likelihood of developing no BPD, or mild, moderate, or severe BPD. The accuracy of the artificial intelligence model was tested based on the clinical outcomes of patients. This study included 122 premature infants with a birth weight of 990 g (range: 840-1120 g). Of these, 33 (27%) patients did not develop BPD, 24 (19.7%) had mild BPD, 28 (23%) had moderate BPD, and 37 (30%) had severe BPD. A total of 395 chest radiographs from these patients were used to develop an artificial intelligence (AI) model for predicting BPD. Area under the curve values, representing the accuracy of predicting severe, moderate, mild, and no BPD, were as follows: 0.79, 0.75, 0.82, and 0.82 for day 1 radiographs; 0.88, 0.82, 0.74, and 0.94 for week 2 radiographs; 0.87, 0.83, 0.88, and 0.96 for week 3 radiographs; and 0.90, 0.82, 0.86, and 0.97 for week 4 radiographs. The artificial intelligence model successfully identified BPD on chest radiographs and classified its severity. The accuracy of the model can be improved using larger control and external validation datasets.
支气管肺发育不良（BPD）是早产儿的一种严重发病情况。本研究旨在评估该模型预测与临床结果相比的准确性。获取了2020年1月1日至2021年12月31日在新生儿重症监护病房出生的孕周≤28周且出生体重<1250克的早产儿的病历。在这项回顾性模型开发和验证研究中，使用DenseNet121深度学习架构开发了一个人工智能模型。数据集和测试集包括出生后第1天以及第2、3、4周获得的胸部X光片。该模型预测无BPD、轻度、中度或重度BPD的发生可能性。基于患者的临床结果测试了人工智能模型的准确性。本研究纳入了122例出生体重为990克（范围：840 - 1120克）的早产儿。其中，33例（27%）患者未发生BPD，24例（19.7%）有轻度BPD，28例（23%）有中度BPD，37例（30%）有重度BPD。这些患者总共395张胸部X光片被用于开发一个预测BPD的人工智能（AI）模型。代表预测重度、中度、轻度和无BPD准确性的曲线下面积值如下：出生后第1天X光片分别为0.79、0.75、0.82和0.82；第2周X光片分别为0.88、0.82、0.74和0.94；第3周X光片分别为0.87、0.83、0.88和0.96；第4周X光片分别为0.90、0.82、0.86和0.97。人工智能模型成功在胸部X光片上识别出BPD并对其严重程度进行了分类。使用更大的对照和外部验证数据集可以提高模型的准确性。
