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.

Agreement between the perception of colon cleansing reported by patients and colon cleansing assessed by a validated colon cleansing scale.

Aims: Patients' perception of their cleansing quality can guide strategies to improve cleansing during colonoscopy. There are no studies assessing the agreement between the quality of cleansing perceived by patients and cleansing quality assessed during colonoscopy using validated bowel preparation scales. The main aim of this study was to compare the cleansing quality reported by patients with the quality during colonoscopy using the Boston Bowel Preparation Scale (BBPS).

Efficacy and Safety of Erenumab, Galcanezumab, and Fremanezumab in the Treatment of Drug-Resistant Chronic Migraine: Experience in Real Clinical Practice.

Background: Due to the recent introduction of new biologic drugs for chronic migraine, a global evaluation in real clinical practice is necessary.

Objective: The objective was to evaluate the effectiveness and safety in real clinical practice of drugs targeting the calcitonin gene-related peptide receptor (CGRPr) in patients with chronic migraine.

Methods: Single-center, restrospective study (2019-2022), including patients with chronic migraine treated with erenumab, galcanezumab, or fremanezumab.