عنوان انگلیسی مقاله:
Deep learning-enabled system for rapid pneumothorax screening on chest CT
ترجمه فارسی عنوان مقاله:
یک سیستم با قابلیت یادگیری عمیق برای آزمایش غربالگری سریع بر روی ct قفسه سینه
Sciencedirect - Elsevier - European Journal of Radiology, 120 (2019) 108692: doi:10:1016/j:ejrad:2019:108692
Xiang Li⁎, James H. Thrall, Subba R. Digumarthy, Mannudeep K. Kalra, Pari V. Pandharipande, Bowen Zhang, Chayanin Nitiwarangkul, Ramandeep Singh, Ruhani Doda Khera, Quanzheng Li
Purpose: Prompt diagnosis and quantitation of pneumothorax impact decisions pertaining to patient management.
The purpose of our study was to develop and evaluate the accuracy of a deep learning (DL)-based image
classification program for detection of pneumothorax on chest CT.
Method: In an IRB approved study, an eight-layer convolutional neural network (CNN) using constant-size
(36*36 pixels) 2D image patches was trained on a set of 80 chest CTs, with (n=50) and without (n=30)
pneumothorax. Image patches were classified based on their probability of representing pneumothorax with
subsequent generation of 3D heat-maps. The heat maps were further defined to include 1) pneumothorax area
size, 2) relative location of the region to the lung boundary, and 3) a shape descriptor based on regional anisotropy.
A support vector machine (SVM) was trained for classification.
Result: We assessed performance of our program in a separate test dataset of 200 chest CT examinations, with
(160/200, 75%) and without (40/200, 25%) pneumothorax. Data were analyzed to determine the accuracy,
sensitivity, specificity. The subject-wise sensitivity was 100% (all 160/160 pneumothoraces detected) and
specificity was 82.5% (33 true negative/40). False positive classifications were primarily related to emphysema
and/or artifacts in the test images.
Conclusion: This deep learning-based program demonstrated high accuracy for automatic detection of pneumothorax
on chest CTs. By implementing it on a high-performance computing platform and integrating the
domain knowledge of radiologists into the analytics framework, our method can be used to rapidly pre-screen
large numbers of cases for presence of pneumothorax, a critical finding.
Keywords: Chest CT | Pneumothorax | Deep learning