Identification of patients with malignant biliary strictures using a cholangioscopy-based deep learning artificial intelligence (with video).

BACKGROUND AND AIMS: Accurately diagnosing malignant biliary strictures (MBSs) as benign or malignant remains challenging. It has been suggested that direct visualization and interpretation of cholangioscopy images provide greater accuracy for stricture classification than current sampling techniques (ie, brush cytology and forceps biopsy sampling) using ERCP. We aimed to develop a convolutional neural network (CNN) model capable of accurate stricture classification and real-time evaluation based solely on cholangioscopy image analysis. METHODS: Consecutive patients with cholangioscopy examinations from 2012 to 2021 were reviewed. A CNN was developed and tested using cholangioscopy images with direct expert annotations. The CNN was then applied to a multicenter, reserved test set of cholangioscopy videos. CNN performance was then directly compared with that of ERCP sampling techniques. Occlusion block heatmap analyses were used to evaluate and rank cholangioscopy features associated with MBSs. RESULTS: One hundred fifty-four patients with available cholangioscopy examinations were included in the study. The final image database comprised 2,388,439 still images. The CNN demonstrated good performance when tasked with mimicking expert annotations of high-quality malignant images (area under the receiver-operating characteristic curve, .941). Overall accuracy of CNN-based video analysis (.906) was significantly greater than that of brush cytology (.625, P = .04) or forceps biopsy sampling (.609, P = .03). Occlusion block heatmap analysis demonstrated that the most frequent image feature for an MBS was the presence of frond-like mucosa/papillary projections. CONCLUSIONS: This study demonstrates that a CNN developed using cholangioscopy data alone has greater accuracy for biliary stricture classification than traditional ERCP-based sampling techniques.

Single-session EUS-directed transgastric endoscopic retrograde cholangiopancreatography and transpapillary gallbladder drainage.

Video 1.

Video Capsule Endoscopy and Device-Assisted Enteroscopy.

Mark Hanscom Courtney Stead Harris Feldman Neil B. Marya David Cave.

Sweet Syndrome, Not so Sweet during an Ulcerative Colitis Flare Especially When You Cannot Eat.

Sweet syndrome is a rare skin condition characterized by painful papules, nodules, or plaques with dense neutrophilic infiltrate in the upper dermis. It has been observed as idiopathic (classical), malignancy-associated, and drug-induced. The pathogenesis is not completely understood, but it is thought to involve hypersensitivity reactions to specific triggers. In some cases the etiology is unclear or may be multifactorial. We present a case of Sweet syndrome secondary to ulcerative colitis flare versus adalimumab re-induction.

Nightly sleep duration, fragmentation, and quality and daily risk of migraine.

OBJECTIVE: To test the hypotheses that insufficient duration, high fragmentation, and poor sleep quality are temporally associated with migraine onset on the day immediately following the sleep period (day 0) and the following day (day 1). METHODS: In this prospective cohort study of 98 adults with episodic migraine, participants completed twice-daily electronic diaries on sleep, headaches, and other health habits, and wore wrist actigraphs for 6 weeks. We estimated the incidence of migraine following nights with short sleep duration, high fragmentation, or low quality compared to nights with adequate sleep with conditional logistic regression models stratified by participant and adjusted for caffeine intake, alcohol intake, physical activity, stress, and day of week. RESULTS: Participants were a mean age of 35.1 ± 12.1 years. We collected 4,406 days of data, with 870 headaches reported. Sleep duration ≤6.5 hours and poor sleep quality were not associated with migraine on day 0 or day 1. Diary-reported low efficiency was associated with 39% higher odds of headache on day 1 (odds ratio [OR] 1.39, 95% confidence interval [CI] 1.06-1.81). Actigraphic-assessed high fragmentation was associated with lower odds of migraine on day 0 (wake after sleep onset >53 minutes, OR 0.64, 95% CI 0.48-0.86; efficiency ≤88%, OR 0.74, 95% CI 0.56-0.99). CONCLUSION: Short sleep duration and low sleep quality were not temporally associated with migraine. Sleep fragmentation, defined by low sleep efficiency, was associated with higher odds of migraine on day 1. Further research is needed to understand the clinical and neurobiologic implications of sleep fragmentation and risk of migraine.