Convolutional neural network deep learning model accurately detects rectal cancer in endoanal ultrasounds.
Tech Coloproctol
; 28(1): 44, 2024 Apr 01.
Article
en En
| MEDLINE
| ID: mdl-38561492
ABSTRACT
BACKGROUND:
Imaging is vital for assessing rectal cancer, with endoanal ultrasound (EAUS) being highly accurate in large tertiary medical centers. However, EAUS accuracy drops outside such settings, possibly due to varied examiner experience and fewer examinations. This underscores the need for an AI-based system to enhance accuracy in non-specialized centers. This study aimed to develop and validate deep learning (DL) models to differentiate rectal cancer in standard EAUS images.METHODS:
A transfer learning approach with fine-tuned DL architectures was employed, utilizing a dataset of 294 images. The performance of DL models was assessed through a tenfold cross-validation.RESULTS:
The DL diagnostics model exhibited a sensitivity and accuracy of 0.78 each. In the identification phase, the automatic diagnostic platform achieved an area under the curve performance of 0.85 for diagnosing rectal cancer.CONCLUSIONS:
This research demonstrates the potential of DL models in enhancing rectal cancer detection during EAUS, especially in settings with lower examiner experience. The achieved sensitivity and accuracy suggest the viability of incorporating AI support for improved diagnostic outcomes in non-specialized medical centers.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neoplasias del Recto
/
Aprendizaje Profundo
Límite:
Humans
Idioma:
En
Revista:
Tech Coloproctol
/
Tech. coloproctol
/
Techniques in coloproctology
Asunto de la revista:
GASTROENTEROLOGIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Israel
Pais de publicación:
Italia