Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open, clinically embedded software development platform and machine learning
Data
2020Autor
Anastasopoulos, Constantin
Weikert, Thomas
Yang, Shan
Abdulkadir, Ahmed
Schmülling, Lena
Bühler, Claudia
Paciolla, Fabiano
Sexauer, Raphael
Cyriac, Joshy
Nesic, Ivan
Twerenbold, Raphael
Bremerich, Jens
Stieltjes, Bram
Sauter, Alexander W.
Sommer, Gregor
Metadata
Mostrar registro completoResumo
Purpose: During the emerging COVID-19 pandemic, radiology departments faced a substantial increase in chest
CT admissions coupled with the novel demand for quantification of pulmonary opacities. This article describes
how our clinic implemented an automated software solution for this purpose into an established software
platform in 10 days. The underlying hypothesis was that modern academic centers in radiology are capable of
developing and implementing such tools by their own efforts and fast enough to meet the rapidly increasing
clinical needs in the wake of a pandemic.
Method: Deep convolutional neural network algorithms for lung segmentation and opacity quantification on
chest CTs were trained using semi-automatically and manually created ground-truth (Ntotal = 172). The performance of the in-house method was compared to an externally developed algorithm on a separate test subset
(N = 66).
Results: The final algorithm was available at day 10 and achieved human-like performance (Dice coefficient = 0.97). For opacity quantification, a slight underestimation was seen both for the in-house (1.8 %) and for
the external algorithm (0.9 %). In contrast to the external reference, the underestimation for the in-house algorithm showed no dependency on total opacity load, making it more suitable for follow-up.
Conclusions: The combination of machine learning and a clinically embedded software development platform
enabled time-efficient development, instant deployment, and rapid adoption in clinical routine. The algorithm
for fully automated lung segmentation and opacity quantification that we developed in the midst of the COVID19 pandemic was ready for clinical use within just 10 days and achieved human-level performance even in
complex cases.
Palabras clave
Computed tomography; COVID-19; Machine learning; SoftwareLink para o recurso
https://doi.org/10.1016/j.ejrad.2020.109233Collections
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