IntroductionThe identification of early-stage colorectal cancers (CRC) and the resection of pre-cancerous neoplastic lesions through colonoscopy allows to decrease both CRC incidence and mortality. However, colonoscopy miss rates up to 26% for adenomas and 9% for advanced adenomas have been reported. In recent years, artificial intelligence (AI) systems have been emerging as easy-to-use tools, potentially lowering the risk of missing lesions.Areas coveredThis review paper focuses on GI Genius device (Medtronic Co. Minneapolis, MN, U.S.A.) a computer-assisted tool designed to assist endoscopists during standard white-light colonoscopies in detecting mucosal lesions.Expert opinionRandomized controlled trials (RCTs) suggest that GI Genius is a safe and effective tool for improving adenoma detection, especially in CRC screening and surveillance colonoscopies. However, its impact seems to be less significant among experienced endoscopists and in real-world clinical scenarios compared to the controlled conditions of RCTs. Furthermore, it appears that GI Genius mainly enhances the detection of non-advanced, small polyps, but does not significantly impact the identification of advanced and difficult-to-detect adenoma. When using GI Genius, no complications were documented. Only a small number of studies reported an increased in withdrawal time or the removal of non-neoplastic lesions.

Background and study aims Artificial Intelligence (AI) systems could make the optical diagnosis (OD) of diminutive colorectal polyps (DCPs) more reliable and objective. This study was aimed at prospectively evaluating feasibility and diagnostic performance of AI-standalone and AI-assisted OD of DCPs in a real-life setting by using a white light-based system (GI Genius, Medtronic Co, Minneapolis, Minnesota, United States). Patients and methods Consecutive colonoscopy outpatients with at least one DCP were evaluated by 11 endoscopists (5 experts and 6 non-experts in OD). DCPs were classified in real time by AI (AI-standalone OD) and by the endoscopist with the assistance of AI (AI-assisted OD), with histopathology as the reference standard. Results Of the 480 DCPs, AI provided the outcome "adenoma" or "non-adenoma" in 81.4% (95% confidence interval [CI]: 77.5-84.6). Sensitivity, specificity, positive and negative predictive value, and accuracy of AI-standalone OD were 97.0% (95% CI 94.0-98.6), 38.1% (95% CI 28.9-48.1), 80.1% (95% CI 75.2-84.2), 83.3% (95% CI 69.2-92.0), and 80.5% (95% CI 68.7-82.8%), respectively. Compared with AI-standalone, the specificity of AI-assisted OD was significantly higher (58.9%, 95% CI 49.7-67.5) and a trend toward an increase was observed for other diagnostic performance measures. Overall accuracy and negative predictive value of AI-assisted OD for experts and non-experts were 85.8% (95% CI 80.0-90.4) vs. 80.1% (95% CI 73.6-85.6) and 89.1% (95% CI 75.6-95.9) vs. 80.0% (95% CI 63.9-90.4), respectively. Conclusions Standalone AI is able to provide an OD of adenoma/non-adenoma in more than 80% of DCPs, with a high sensitivity but low specificity. The human-machine interaction improved diagnostic performance, especially when experts were involved.

Background and aimSignificant weight loss is the only proven therapy for patients with metabolic dysfunction-associated steatotic liver disease (MASLD). This study aimed to identify factors that predict significant weight loss - exceeding 7% of the initial weight - in MASLD outpatients.MethodsWe included all MASLD patients referred to four Italian tertiary liver centres between January 2019 and December 2021. They received advice on lifestyle changes according to current guidelines, with reassessment of anthropometric measures after 18 to 24 months.ResultsAfter evaluating 908 patients meeting the inclusion criteria, the majority were found to be male (518/908, 57%) with a mean age of 61.7 ± 13.31 years and a mean baseline body mass index (BMI) of 30.31 ± 4.49 kg/m2. Over a mean follow-up period of 21.88 ± 6 months, only 166 (18.3%) patients achieved significant weight loss. Unadjusted regression analysis revealed significant correlations between dyslipidaemia, baseline BMI ≥ 30kg/m2, and the use of GLP-1 (glucagon-like peptide 1) agonists with significant weight loss (p<0.05). Multivariate regression analysis identified only BMI ≥ 30 kg/m2 (OR = 1.96, 95% CI: 1.37-2.8) and dyslipidaemia (OR = 0.6, 95% CI: 0.44-0.88) as independent predictors of significant weight loss.ConclusionsA baseline BMI ≥ 30 kg/m2 and the absence of dyslipidemia emerged as significant predictors of achieving substantial weight loss in MASLD patients. These findings highlight the need for personalized interventions to improve the effectiveness of weight management strategies in MASLD.

Several scoring systems have been developed for both upper and lower GI bleeding to predict the bleeding severity and discriminate between low-risk patients, who may be suitable for outpatient management, and those who would likely need hospital-based interventions and are at high risk for adverse outcomes. Risk scores created to identify low-risk patients (namely the Glasgow Blatchford Score and the Oakland score) showed very good discriminative performances and their implementation has proven to be effective in reducing hospital admissions and healthcare burden. Conversely, the performances of risk scores in identifying specific adverse events to define high-risk patients are less accurate, and whether their integration into routine clinical practice has a tangible impact on patient management remains unproven. This review describes the existing risk score systems for GI bleeding, emphasizes key research findings, elucidates the circumstances in which their utilization can be beneficial, examines their constraints when considering routine clinical application, and discuss future development.

The identification of advanced fibrosis by applying noninvasive tests is still a key component of the diagnostic algorithm of NAFLD. The aim of this study is to assess the concordance between the FIB-4 and liver stiffness measurement (LSM) in patients referred to two liver centers for the ultrasound-based diagnosis of NAFLD. Fibrosis 4 Index for Liver Fibrosis (FIB-4) and LSM were assessed in 1338 patients. A total of 428 (32%) had an LSM ≥ 8 kPa, whereas 699 (52%) and 113 (9%) patients had an FIB-4 < 1.3 and >3.25, respectively. Among 699 patients with an FIB-4 < 1.3, 118 (17%) had an LSM ≥ 8 kPa (false-negative FIB-4). This proportion was higher in patients ≥60 years, with diabetes mellitus (DM), arterial hypertension or a body mass index (BMI) ≥ 27 kg/m2. In multiple adjusted models, age ≥ 60 years (odds ratio (OR) = 1.96, 95% confidence interval (CI) 1.19–3.23)), DM (OR = 2.59, 95% CI 1.63–4.13), body mass index (BMI) ≥ 27 kg/m2 (OR = 2.17, 95% CI 1.33–3.56) and gamma-glutamyltransferase ≥ 25 UI/L (OR = 2.68, 95% CI 1.49–4.84) were associated with false-negative FIB-4. The proportion of false-negative FIB-4 was 6% in patients with none or one of these risk factors and increased to 16, 31 and 46% among those with two, three and four concomitant risk factors, respectively. FIB-4 is suboptimal to identify patients to refer to liver centers, because about one-fifth may be false negative at FIB-4, having instead an LSM ≥ 8 KPa.