Skip to main content
main-content

28.12.2020 | COVID-19 | Imaging Informatics and Artificial Intelligence Zur Zeit gratis

The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study

Zeitschrift:
European Radiology
Autoren:
Qiuchen Xie, Yiping Lu, Xiancheng Xie, Nan Mei, Yun Xiong, Xuanxuan Li, Yangyong Zhu, Anling Xiao, Bo Yin
Wichtige Hinweise

Supplementary Information

The online version contains supplementary material available at https://​doi.​org/​10.​1007/​s00330-020-07553-7.
Qiuchen Xie and Yiping Lu contributed equally to this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Objectives

Based on the current clinical routine, we aimed to develop a novel deep learning model to distinguish coronavirus disease 2019 (COVID-19) pneumonia from other types of pneumonia and validate it with a real-world dataset (RWD).

Methods

A total of 563 chest CT scans of 380 patients (227/380 were diagnosed with COVID-19 pneumonia) from 5 hospitals were collected to train our deep learning (DL) model. Lung regions were extracted by U-net, then transformed and fed to pre-trained ResNet-50-based IDANNet (Identification and Analysis of New covid-19 Net) to produce a diagnostic probability. Fivefold cross-validation was employed to validate the application of our model. Another 318 scans of 316 patients (243/316 were diagnosed with COVID-19 pneumonia) from 2 other hospitals were enrolled prospectively as the RWDs to testify our DL model’s performance and compared it with that from 3 experienced radiologists.

Results

A three-dimensional DL model was successfully established. The diagnostic threshold to differentiate COVID-19 and non-COVID-19 pneumonia was 0.685 with an AUC of 0.906 (95% CI: 0.886–0.913) in the internal validation group. In the RWD cohort, our model achieved an AUC of 0.868 (95% CI: 0.851–0.876) with the sensitivity of 0.811 and the specificity of 0.822, non-inferior to the performance of 3 experienced radiologists, suggesting promising clinical practical usage.

Conclusions

The established DL model was able to achieve accurate identification of COVID-19 pneumonia from other suspected ones in the real-world situation, which could become a reliable tool in clinical routine.

Key Points

In an internal validation set, our DL model achieved the best performance to differentiate COVID-19 from non-COVID-19 pneumonia with a sensitivity of 0.836, a specificity of 0.800, and an AUC of 0.906 (95% CI: 0.886–0.913) when the threshold was set at 0.685.
In the prospective RWD cohort, our DL diagnostic model achieved a sensitivity of 0.811, a specificity of 0.822, and AUC of 0.868 (95% CI: 0.851–0.876), non-inferior to the performance of 3 experienced radiologists.
The attention heatmaps were fully generated by the model without additional manual annotation and the attention regions were highly aligned with the ROIs acquired by human radiologists for diagnosis.

Nutzen Sie Ihre Chance: Dieser Inhalt ist zurzeit gratis verfügbar.

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Unsere Produktempfehlungen

e.Med Interdisziplinär

Kombi-Abonnement

Für Ihren Erfolg in Klinik und Praxis - Die beste Hilfe in Ihrem Arbeitsalltag als Mediziner

Mit e.Med Interdisziplinär erhalten Sie Zugang zu allen CME-Fortbildungen und Fachzeitschriften auf SpringerMedizin.de.

e.Med Radiologie

Kombi-Abonnement

Mit e.Med Radiologie erhalten Sie Zugang zu CME-Fortbildungen des Fachgebietes Radiologie, den Premium-Inhalten der radiologischen Fachzeitschriften, inklusive einer gedruckten Radiologie-Zeitschrift Ihrer Wahl.

Zusatzmaterial
Nur für berechtigte Nutzer zugänglich
Literatur
Über diesen Artikel

Coronavirus Corona-Update

Die aktuelle Entwicklung im Überblick: Nachrichten, Webinare, Übersichtsarbeiten.

Bildnachweise