Design and validation of an artificial intelligence system to detect the quality of colon cleansing before colonoscopy.

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 And Methods: Patients referred for outpatient colonoscopy were asked to provide images of their rectal effluent during the bowel preparation process.

Ganglion cell layer analysis with deep learning in glaucoma diagnosis.

Objective: To determine and compare the diagnostic precision in glaucoma of two deep learning models using infrared images of the optic nerve, eye fundus, and the ganglion cell layer (GCL).

Methods: We have selected a sample of normal and glaucoma patients. Three infrared images were registered with a spectral-domain optical coherence tomography (SD-OCT).