Deep learning-enabled system for rapid pneumothorax screening on chest CT.
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 appro...
| Publicado en: | European Journal of Radiology Vol. 120 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Nov2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139347772&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139347772 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0720048X 3S7 jtl: European Journal of Radiology issn: 0720048X maglogo: N pubinfo: dt: Nov2019 vid: 120 pid: 1004 pub: Elsevier B.V. artinfo: ui: 139347772 139347772 NLM31585302 10.1016/j.ejrad.2019.108692 NLM31585302 139347772 ppct: 1 formats: tig: atl: Deep learning-enabled system for rapid pneumothorax screening on chest CT. aug: au: Li, Xiang Thrall, James H. Digumarthy, Subba R. Kalra, Mannudeep K. Pandharipande, Pari V. Zhang, Bowen Nitiwarangkul, Chayanin Singh, Ramandeep Khera, Ruhani Doda Li, Quanzheng affil: Massachusetts General Hospital, Department of Radiolgoy, United States sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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