Design and validation of an artificial intelligence system to detect the quality of colon cleansing before colonoscopy.
Gastroenterol Hepatol
; 47(5): 481-490, 2024 May.
Article
en En, Es
| MEDLINE
| ID: mdl-38154552
ABSTRACT
BACKGROUND AND AIMS:
Patients' perception of their bowel cleansing quality may guide rescue cleansing strategies before colonoscopy. The main aim of this study was to train and validate a convolutional neural network (CNN) for classifying rectal effluent during bowel preparation intake as "adequate" or "inadequate" cleansing before colonoscopy. PATIENTS ANDMETHODS:
Patients referred for outpatient colonoscopy were asked to provide images of their rectal effluent during the bowel preparation process. The images were categorized as adequate or inadequate cleansing based on a predefined 4-picture quality scale. A total of 1203 images were collected from 660 patients. The initial dataset (799 images), was split into a training set (80%) and a validation set (20%). The second dataset (404 images) was used to develop a second test of the CNN accuracy. Afterward, CNN prediction was prospectively compared with the Boston Bowel Preparation Scale (BBPS) in 200 additional patients who provided a picture of their last rectal effluent.RESULTS:
On the initial dataset, a global accuracy of 97.49%, a sensitivity of 98.17% and a specificity of 96.66% were obtained using the CNN model. On the second dataset, an accuracy of 95%, a sensitivity of 99.60% and a specificity of 87.41% were obtained. The results from the CNN model were significantly associated with those from the BBPS (P<0.001), and 77.78% of the patients with poor bowel preparation were correctly classified.CONCLUSION:
The designed CNN is capable of classifying "adequate cleansing" and "inadequate cleansing" images with high accuracy.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Catárticos
/
Colonoscopía
Límite:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
/
Es
Año:
2024
Tipo del documento:
Article