Identifying cardiomegaly in chest X-rays: a cross-sectional study of evaluation and comparison between different transfer learning methods.

Autor:	Bougias H; Department of Clinical Radiology, Ioannina University Hospital, Ioannina, Greece., Georgiadou E; Department of Medical Imaging, Metaxa Anticancer Hospital, Athens, Greece., Malamateniou C; Division of Midwifery and Radiography, School of Health Sciences, City University of London, London, UK., Stogiannos N; Division of Midwifery and Radiography, School of Health Sciences, City University of London, London, UK.; Department of Medical Imaging, Corfu General Hospital, Corfu, Greece.
Jazyk:	angličtina
Zdroj:	Acta radiologica (Stockholm, Sweden : 1987) [Acta Radiol] 2021 Dec; Vol. 62 (12), pp. 1601-1609. Date of Electronic Publication: 2020 Nov 17.
DOI:	10.1177/0284185120973630
Abstrakt:	Background: Cardiomegaly is a relatively common incidental finding on chest X-rays; if left untreated, it can result in significant complications. Using Artificial Intelligence for diagnosing cardiomegaly could be beneficial, as this pathology may be underreported, or overlooked, especially in busy or under-staffed settings. Purpose: To explore the feasibility of applying four different transfer learning methods to identify the presence of cardiomegaly in chest X-rays and to compare their diagnostic performance using the radiologists' report as the gold standard. Material and Methods: Two thousand chest X-rays were utilized in the current study: 1000 were normal and 1000 had confirmed cardiomegaly. Of these exams, 80% were used for training and 20% as a holdout test dataset. A total of 2048 deep features were extracted using Google's Inception V3, VGG16, VGG19, and SqueezeNet networks. A logistic regression algorithm optimized in regularization terms was used to classify chest X-rays into those with presence or absence of cardiomegaly. Results: Diagnostic accuracy is reported by means of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), with the VGG19 network providing the best values of sensitivity (84%), specificity (83%), PPV (83%), NPV (84%), and overall accuracy (84,5%). The other networks presented sensitivity at 64.1%-82%, specificity at 77.1%-81.1%, PPV at 74%-81.4%, NPV at 68%-82%, and overall accuracy at 71%-81.3%. Conclusion: Deep learning using transfer learning methods based on VGG19 network can be used for the automatic detection of cardiomegaly on chest X-ray images. However, further validation and training of each method is required before application to clinical cases.
Databáze:	MEDLINE
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