Small bowel capsule endoscopy in obscure gastrointestinal bleeding : A matched cohort comparison of patients with normal vs surgically altered gastric anatomy

Background: Little is known about small bowel capsule endoscopy (SBCE) outcomes in patients with surgically altered anatomy. Aims: To assess the feasibility and diagnostic yield of orally ingested SBCE to investigate obscure gastrointestinal bleeding (OGIB) in patients with surgically altered gastric anatomy, compared to native gastric anatomy. Methods: 207 patients with OGIB were selected from an open, multicenter, retrospective cohort (SAGA study) and match-paired according to age, gender and bleeding type (overt/occult) to 207 control patients from a randomized controlled trial (PREPINTEST). Primary outcomes were the diagnostic yield (P1 or P2 findings), completion rate, adverse events rate, and small bowel transit time (SBTT). Results: The diagnostic yield was not statistically different between groups (44.9% in SAGA vs 42.5% in control patients). Inflammatory/ulcerated lesions were significantly more frequent in patients with SAGA (43.0% vs 29.3%). The median SBTT was significantly longer in the SAGA group than in control patients (283 vs 206 minutes), with a significantly lower completion rate (82.6% vs 89.9%); Adverse events were scarce (0.5% vs 0.0%). Conclusion: Patients with surgically altered gastric anatomy should benefit from SBCE investigation for OGIB as much as non-operated patients

A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy

International audienceBackground and AimsGastrointestinal angiectasia (GIA) is the most common small bowel (SB) vascular lesion, with an inherent risk of bleeding. SB Capsule Endoscopy (SB-CE) is the currently accepted diagnostic procedure. The aim of this study was to develop a computer-assisted diagnosis (CAD) tool for the detection of GIA. MethodsDeidentified SB-CE still frames featuring annotated typical GIA and normal control still frames, were selected from a database. A semantic segmentation images approach associated with a convolutional neural network (CNN) was used for deep feature extractions and classification. Two datasets of still frames were created and used for machine-learning and for algorithm testing. ResultsThe GIA detection algorithm yielded a sensitivity of 100%, a specificity of 96%, a positive predictive value of 96%, and a negative predictive value of 100%. Reproducibility was optimal. The reading process for an entire SB-CE video would take 2340 seconds (39 minutes).Conclusion The developed CNN-based algorithm had high diagnostic performances allowing detection of GIA in SB-CE still frames. This study paves the way for future automated CNN-based SB-CE reading softwares

CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy

International audienceBackground and study aims : Capsule endoscopy (CE) is the preferred method for small bowel (SB) exploration. With a mean number of 50,000 SB frames per video, SBCE reading is time-consuming and tedious (30 to 60 minutes per video). We describe a large, multicenter database named CAD-CAP (Computer-Assisted Diagnosis for CAPsule Endoscopy, CAD-CAP). This database aims to serve the development of CAD tools for CE reading.Materials and methods Twelve French endoscopy centers were involved. All available third-generation SB-CE videos (Pillcam, Medtronic) were retrospectively selected from these centers and deidentified. Any pathological frame was extracted and included in the database. Manual seg-mentation of findings within these frames was performed by two pre-med students trained and supervised by an expert reader. All frames were then classified by type and clinical relevance by a panel of three expert readers. An automated extraction process was also developed to create a dataset of normal, proofread, control images from normal, complete, SB-CE videos.Results Four-thousand-one-hundred-and-seventy-four SB-CE were included. Of them, 1,480 videos (35 %) containing at least one pathological finding were selected. Findings from 5,184 frames (with their short video sequences) were extracted and delimited: 718 frames with fresh blood, 3,097 frames with vascular lesions, and 1,369 frames with inflammatory and ulcerative lesions. Twenty-thousand normal frames were extracted from 206 SB-CE normal videos. CAD-CAP has already been used for development of automated tools for angiectasia detection and also for two international challenges on medical computerized analysis

CAD-CAP: une base de données française à vocation internationale, pour le développement et la validation d'outils de diagnostic assisté par ordinateur en vidéocapsule endoscopique du grêle

International audienceThe Computer-Assisted Diagnosis for CAPsule Endoscopy (CAD-CAP) database is a large, multicentric, public database of small-bowel (SB) capsule endoscopy (CE) still frames and video sequences, that aims to serve the development of computer-assisted diagnosis tools, from the initial steps (machine learning) to the last preclinical steps (assessment and comparison of performances). The CAD-CAP database is endorsed and maintained by the Société Française d’Endoscopie Digestive (SFED), with the support of the Société Nationale Française de Gastroentérologie (SNFGE) and MS

Including Ratio of Platelets to Liver Stiffness Improves Accuracy of Screening for Esophageal Varices That Require Treatment

International audienceBackground & aims: Based on platelets and liver stiffness measurements, the Baveno VI criteria (B6C), the expanded B6C (EB6C), and the ANTICIPATE score can be used to rule out varices needing treatment (VNT) in patients with compensated chronic liver disease. We aimed to improve these tests by including data on the ratio of platelets to liver stiffness.Methods: In a retrospective analysis of data from 10 study populations, collected from 2004 through 2018, we randomly assigned data from 2368 patients with chronic liver disease of different etiologies to a derivation population (n = 1579; 15.1% with VNT, 50.2% with viral hepatitis, 28.9% with nonalcoholic fatty liver disease, 20.8% with alcohol-associated liver disease, with model for end-stage liver disease scores of 9.5 ± 3.0, and 93.0% with liver stiffness measurements ≥10 kPa) or a validation population (n = 789). Test results were compared with results from a sequential algorithm (VariScreen). VariScreen incorporated data on platelets or liver stiffness measurements and then the ratio of platelets to liver stiffness measurement, adjusted for etiology, patient sex, and international normalized ratio.Results: In the derivation population, endoscopies were spared for 23.9% of patients using the B6C (VNT missed in 2.9%), 24.3% of patients using the ANTICIPATE score (VNT missed in 4.6%), 34.5% of patients using VariScreen (VNT missed in 2.9%), and 41.9% of patients using the EB6C (VNT missed in 10.9%). Differences in spared endoscopy rates were significant (P ≤ .001), except for B6C vs ANTICIPATE and in missed VNT only for EB6C vs the others (P ≤ .009). VariScreen was the only safe test regardless of sex or etiology (missed VNT ≤5%). Moreover, VariScreen secured screening without missed VNT in patients with model for end-stage liver disease scores higher than 10. This overall strategy performed better than a selective strategy restricted to patients with compensated liver disease. Test performance and safety did not differ significantly among populations.Conclusions: In a retrospective study of data from 2368 patients with chronic liver disease, we found that the B6C are safe whereas the EB6C are unsafe, based on missed VNT. The VariScreen algorithm performed well in patients with chronic liver disease of any etiology or severity. It is the only test that safely rules out VNT and can be used in clinical practice