Deep learning model-assisted detection of kidney stones on computed tomography
Int. braz. j. urol
;
48(5): 830-839, Sept.-Oct. 2022. tab, graf
Artículo
en Inglés
|
LILACS-Express
| LILACS
| ID: biblio-1394380
ABSTRACT
ABSTRACT Introduction:
The aim of this study was to investigate the success of a deep learning model in detecting kidney stones in different planes according to stone size on unenhanced computed tomography (CT) images. Materials andMethods:
This retrospective study included 455 patients who underwent CT scanning for kidney stones between January 2016 and January 2020; of them, 405 were diagnosed with kidney stones and 50 were not. Patients with renal stones of 0-1 cm, 1-2 cm, and >2 cm in size were classified into groups 1, 2, and 3, respectively. Two radiologists reviewed 2,959 CT images of 455 patients in three planes. Subsequently, these CT images were evaluated using a deep learning model. The accuracy rate, sensitivity, specificity, and positive and negative predictive values of the deep learning model were determined.Results:
The training group accuracy rates of the deep learning model were 98.2%, 99.1%, and 97.3% in the axial plane; 99.1%, 98.2%, and 97.3% in the coronal plane; and 98.2%, 98.2%, and 98.2% in the sagittal plane, respectively. The testing group accuracy rates of the deep learning model were 78%, 68% and 70% in the axial plane; 63%, 72%, and 64% in the coronal plane; and 85%, 89%, and 93% in the sagittal plane, respectively.Conclusions:
The use of deep learning algorithms for the detection of kidney stones is reliable and effective. Additionally, these algorithms can reduce the reporting time and cost of CT-dependent urolithiasis detection, leading to early diagnosis and management.
Texto completo:
Disponible
Índice:
LILACS (Américas)
Tipo de estudio:
Estudio diagnóstico
/
Estudio observacional
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Estudio pronóstico
/
Factores de riesgo
/
Estudio de tamizaje
Idioma:
Inglés
Revista:
Int. braz. j. urol
Asunto de la revista:
Urología
Año:
2022
Tipo del documento:
Artículo
País de afiliación:
Turquía
Institución/País de afiliación:
Izmir Bakırcay University Cigli Training and Research Hospital/TR
/
Izmir Bakırçay University/TR
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