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BACKGROUND: Artificial intelligence (AI) has emerged as a promising tool for detecting and characterizing colorectal polyps during colonoscopy, offering potential enhancements in traditional colonoscopy procedures to improve outcomes in patients with inadequate bowel preparation. AIMS: This study aimed to assess the impact of an AI tool on computer-aided detection (CADe) assistance during colonoscopy in this population. METHODS: This case-control study utilized propensity score matching (PSM) for age, sex, race, and colonoscopy indication to analyze a database of patients who underwent colonoscopy at a single tertiary referral center between 2017 and 2023. Patients were excluded if the procedure was incomplete or aborted owing to poor preparation. The patients were categorized based on the use of AI during colonoscopy. Data on patient demographics and colonoscopy performance metrics were collected. Univariate and multivariate logistic regression models were used to compare the groups. RESULTS: After PSM patients with adequately prepped colonoscopies (n = 1466), the likelihood of detecting hyperplastic polyps (OR = 2.0, 95%CI 1.7-2.5, p < 0.001), adenomas (OR = 1.47, 95%CI 1.19-1.81, p < 0.001), and sessile serrated polyps (OR = 1.90, 95%CI 1.20-3.03, p = 0.007) significantly increased with the inclusion of CADe. In inadequately prepped patients (n = 160), CADe exhibited a more pronounced impact on the polyp detection rate (OR = 4.34, 95%CI 1.6-6.16, p = 0.049) and adenomas (OR = 2.9, 95%CI 2.20-8.57, p < 0.001), with a marginal increase in withdrawal and procedure times. CONCLUSION: This study highlights the significant improvement in detecting diminutive polyps (< 5 mm) and sessile polyps using CADe, although notably, this benefit was only seen in patients with adequate bowel preparation. In conclusion, the integration of AI in colonoscopy, driven by artificial intelligence, promises to significantly enhance lesion detection and diagnosis, revolutionize the procedure's effectiveness, and improve patient outcomes.
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BACKGROUND: Gastric adenocarcinoma (GC) is the fourth leading cause of cancer-related mortality, and leading infection-associated cancer. GC has striking geographic variability, with high incidence in East Asia and mountainous Latin America. Reliable cancer data and population-based cancer registries (PBCRs) are lacking for the majority of LMICs, including the Central American Four region (CA-4, Nicaragua, El Salvador, Honduras, and Guatemala). METHODS: Mortality data for Nicaragua were obtained from the highly-rated Ministry of Health death registry. All the patients were diagnosed with gastric cancer between 1997 and 2012 (ICD-10 codes C16.0-C16.9) and death due to any cause were included in the study. Data on variables such as sex, age (stratified by 5-year age groups), municipality, urban/rural, altitude, and year of death were analyzed. RESULTS: A total of 3,886 stomach cancer deaths were reported in Nicaragua between 1997 and 2012, of which 2,214 (56.9%) were male. The ASMR were 13.1 and 8.7 per 100,000 habitants for males and females, respectively, and without significant change during the study period (APC= -0.7, P=0.2). An average of 17.9 years were lost per death (AYLL), accounting for 67,964 years of life lost (YYL). CONCLUSIONS: The burden of gastric cancer mortality is high in Nicaragua with significantly elevated ASMR, YYL, and AYLL. IMPACT: The projected increase in mortality portends the double cancer burden in northern Central America, with persistent infection-associated cancers and growing transition cancers (e.g., breast and colon cancers), which has implications for cancer control in Mesoamerica and U.S. Latino populations.
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BACKGROUND: Deep brain stimulation is an effective treatment for movement disorders and psychiatric conditions. Intra-operative and post-operative events can result in brain tissue deformation (i.e. subdural gaps) which may cause lead deformation and its displacement from optimal target. We developed a method to quantify postoperative lead deformation and we present two DBS cases to illustrate the phenomena of lead deformation resulting from the development of subdural gaps. NEW METHOD: We present a semi-automatic computational algorithm using Computed Tomography scanning with reconstruction to determine lead curvature relative to a theoretical straight lead between the skull entry site and lead tip. Subdural gap was quantified from the CT scan. RESULTS: In 2 patients who had leads implanted, analysis of CT scans was completed within 5 min each. The maximum deviation of the observed lead from the theoretical linear path was 1.1 and 2.6 mm, and the subdural gap was 5.5 and 9.6 mL, respectively. COMPARISON WITH EXISTING METHOD(S): This is the first method allowing a comprehensive characterization of the lead deformation in situ. CONCLUSIONS: The computational algorithms provide a simple, semiautomatic method to characterize in situ lead curvature related to brain tissue deformation after lead placement.