Neural network-based fully automated cardiac resting phase detection algorithm compared with manual detection in patients.
Acta Radiol Open
; 11(10): 20584601221137772, 2022 Oct.
Article
em En
| MEDLINE
| ID: mdl-36325309
ABSTRACT
Background:
A cardiac resting phase is used when performing free-breathing cardiac magnetic resonance examinations.Purpose:
The purpose of this study was to test a cardiac resting phase detection system based on neural networks in clinical practice. Material andMethods:
Four chamber-view cine images were obtained from 32 patients and analyzed. The rest duration, start point, and end point were compared between that determined by the experts and general operators, and a similar comparison was done between that determined by the experts and neural networks the normalized root-mean-square error (RMSE) was also calculated.Results:
Unlike manual detection, the neural network was able to determine the resting phase almost simultaneously as the image was obtained. The rest duration and start point were not significantly different between the neural network and expert (p = .30, .90, respectively), whereas the end point was significantly different between the two groups (p < .05). The start point was not significantly different between the general operator and expert (p = .09), whereas the rest duration and end point were significantly different between the two groups (p < .05). The normalized RMSEs of the rest duration, start point, and end point of the neural network were 0.88, 0.64, and 0.33 ms, respectively, which were lower than those of the general operator (normalized RMSE values were 0.98, 0.68, and 0.51 ms, respectively).Conclusions:
The neural network can determine the resting phase instantly with better accuracy than the manual detection of general operators.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Idioma:
En
Revista:
Acta Radiol Open
Ano de publicação:
2022
Tipo de documento:
Article