Kim Dohun, Lee Jae-Hyeok, Kim Si-Wook, Hong Jong-Myeon, Kim Sung-Jin, Song Minji, Choi Jong-Mun, Lee Sun-Yeop, Yoon Hongjun, Yoo Jin-Young
Department of Thoracic and Cardiovascular Surgery, College of Medicine, Chungbuk National University Hospital, Chungbuk National University, Cheongju 28644, Korea.
Deepnoid, Inc., Seoul 08376, Korea.
Diagnostics (Basel). 2022 Jul 29;12(8):1823. doi: 10.3390/diagnostics12081823.
Artificial intelligence (AI) techniques can be a solution for delayed or misdiagnosed pneumothorax. This study developed, a deep-learning-based AI model to estimate the pneumothorax amount on a chest radiograph and applied it to a treatment algorithm developed by experienced thoracic surgeons. U-net performed semantic segmentation and classification of pneumothorax and non-pneumothorax areas. The pneumothorax amount was measured using chest computed tomography (volume ratio, gold standard) and chest radiographs (area ratio, true label) and calculated using the AI model (area ratio, predicted label). Each value was compared and analyzed based on clinical outcomes. The study included 96 patients, of which 67 comprised the training set and the others the test set. The AI model showed an accuracy of 97.8%, sensitivity of 69.2%, a negative predictive value of 99.1%, and a dice similarity coefficient of 61.8%. In the test set, the average amount of pneumothorax was 15%, 16%, and 13% in the gold standard, predicted, and true labels, respectively. The predicted label was not significantly different from the gold standard ( = 0.11) but inferior to the true label (difference in MAE: 3.03%). The amount of pneumothorax in thoracostomy patients was 21.6% in predicted cases and 18.5% in true cases.
人工智能（AI）技术可以成为解决气胸诊断延迟或误诊问题的一种方法。本研究开发了一种基于深度学习的AI模型，用于估计胸部X光片上的气胸量，并将其应用于由经验丰富的胸外科医生制定的治疗算法中。U-net对气胸和非气胸区域进行语义分割和分类。使用胸部计算机断层扫描（体积比，金标准）和胸部X光片（面积比，真实标签）测量气胸量，并使用AI模型（面积比，预测标签）进行计算。根据临床结果对每个值进行比较和分析。该研究纳入了96例患者，其中67例组成训练集，其余为测试集。AI模型的准确率为97.8%，灵敏度为69.2%，阴性预测值为99.1%，骰子相似系数为61.8%。在测试集中，金标准、预测标签和真实标签中的气胸平均量分别为15%、16%和13%。预测标签与金标准无显著差异（P = 0.11），但不如真实标签（平均绝对误差差异：3.03%）。胸腔造口术患者的气胸量在预测病例中为21.6%，在真实病例中为18.5%。
