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Background and Aims: The resect‐and‐discard strategy for colorectal polyps based on accurate optical diagnosis remains challenges. Our aim was to investigate the feasibility of hyperspectral imaging (HSI) for identifying colorectal polyp properties and diagnosis of colorectal cancer in fresh tissues during colonoscopy. Methods: 144,900 two dimensional images generated from 161 hyperspectral images of colorectal polyp tissues were prospectively obtained from patients undergoing colonoscopy. A residual neural network model was trained with transfer learning to automatically differentiate colorectal polyps, validated by histopathologic diagnosis. The diagnostic performances of the HSI‐AI model and endoscopists were calculated respectively, and the auxiliary efficiency of the model was evaluated after a 2‐week interval. Results: Quantitative HSI revealed histological differences in colorectal polyps. The HSI‐AI model showed considerable efficacy in differentiating nonneoplastic polyps, non‐advanced adenomas, and advanced neoplasia in vitro, with sensitivities of 96.0%, 94.0%, and 99.0% and specificities of 99.0%, 99.0%, and 96.5%, respectively. With the assistance of the model, the median negative predictive value of neoplastic polyps increased from 50.0% to 88.2% (p = 0.013) in novices. Conclusion: This study demonstrated the feasibility of using HSI as a diagnostic tool to differentiate neoplastic colorectal polyps in vitro and the potential of AI‐assisted diagnosis synchronized with colonoscopy. The tool may improve the diagnostic performance of novices and facilitate the application of resect‐and‐discard strategy to decrease the cost. [ABSTRACT FROM AUTHOR]

Background: Patient education contributes to improve public awareness of Helicobacter pylori. Large language models (LLMs) offer opportunities to revolutionize patient education transformatively. This study aimed to assess the quality of patient educational materials (PEMs) generated by LLMs and compared with physician sourced. Materials and Methods: Unified instruction about composing a PEM about H. pylori at a sixth‐grade reading level in both English and Chinese were given to physician and five LLMs (Bing Copilot, Claude 3 Opus, Gemini Pro, ChatGPT‐4, and ERNIE Bot 4.0). The assessments of the completeness and comprehensibility of the Chinese PEMs were conducted by five gastroenterologists and 50 patients according to three‐point Likert scale. Gastroenterologists were asked to evaluate both English and Chinese PEMs and determine the accuracy and safety. The accuracy was assessed by six‐point Likert scale. The minimum acceptable scores were 4, 2, and 2 for accuracy, completeness, and comprehensibility, respectively. The Flesch–Kincaid and Simple Measure of Gobbledygook scoring systems were employed as readability assessment tools. Results: Accuracy and comprehensibility were acceptable for English PEMs of all sources, while completence was not satisfactory. Physician‐sourced PEM had the highest accuracy mean score of 5.60 and LLM‐generated English PEMs ranged from 4.00 to 5.40. The completeness score was comparable between physician‐sourced PEM and LLM‐generated PEMs in English. Chinese PEMs from LLMs proned to have lower score in accuracy and completeness assessment than English PEMs. The mean score for completeness of five LLM‐generated Chinese PEMs was 1.82–2.70 in patients' perspective, which was higher than gastroenterologists' assessment. Comprehensibility was satisfactory for all PEMs. No PEM met the recommended sixth‐grade reading level. Conclusion: LLMs have potential in assisting patient education. The accuracy and comprehensibility of LLM‐generated PEMs were acceptable, but further optimization on improving completeness and accounting for a variety of linguistic contexts are essential for enhancing the feasibility. [ABSTRACT FROM AUTHOR]

Background: The optimal dosage of tetracycline remains unclear for Helicobacter pylori eradication. Frequent dosing requirements may decrease patient adherence and increase the incidence of adverse events, potentially reducing treatment efficacy. This study aimed to compare the efficacy of different tetracycline dosages in rescue treatment for H. pylori infection. Methods: A total of 406 patients needing H. pylori rescue treatment were enrolled. Patients were randomized into two groups and received bismuth‐containing quadruple therapies as follows: esomeprazole 40 mg twice daily, bismuth 220 mg twice daily, amoxicillin 1000 mg twice daily, and tetracycline 500 mg either three (TET‐T group) or four (TET‐F group) times daily. At least 6 weeks after treatment completion, a 13C‐urea breath test was performed to evaluate H. pylori eradication. Results: The intention‐to‐treat (ITT) eradication rates were 91.13% (185/203) and 90.15% (183/203) (p = 0.733), the modified ITT (MITT) eradication rates were 94.87% (185/195) and 95.31% (183/192) (p = 0.841), and the per‐protocol (PP) eradication rates were 94.79% (182/192) and 95.21% (179/188) (p = 0.851) in the TET‐T group and TET‐F group, respectively. The eradication rates for the TET‐T group were not inferior to those of the TET‐F group in ITT, MITT, and PP analyses. The incidence of adverse effects was significantly lower in the TET‐T group than in the TET‐F group (23.65% vs. 33.50%, p = 0.028). No significant differences were observed in treatment compliance between the groups. Conclusions: The dose of tetracycline administered three times daily showed comparable efficacy to that administered four times daily, while significantly reducing the incidence of adverse events. The combination of tetracycline and amoxicillin in bismuth‐containing quadruple therapy achieved a high eradication rate in H. pylori rescue treatment. [ABSTRACT FROM AUTHOR]

Background: Potassium‐competitive acid blockers have demonstrated enormous potential in the eradication treatment of Helicobacter pylori infection, with tegoprazan being one of the representatives. The available data on the safety and efficacy of tegoprazan in dual therapy are limited. Materials and Methods: The multicenter, noninferiority, randomized‐controlled trial was conducted from May 2023 to March 2024. Treatment‐naive subjects were randomly assigned (1:1) to enter either the tegoprazan–amoxicillin (TA) group (tegoprazan 50 mg twice daily and amoxicillin 750 mg four times daily) or the esomeprazole–amoxicillin (EA) group (esomeprazole 20 mg and amoxicillin 750 mg all four times daily), with a duration for 14 days. The primary outcome was eradication rate as determined by 13C‐urea breath test, including per‐protocol (PP) analysis and intention‐to‐treat (ITT) analysis. Secondary outcomes were adverse events and compliance. Results: A total of 368 individuals were included in the randomization. The eradication rates in the EA group and the TA group were 84.2% and 85.8%, respectively, according to an ITT analysis (p = 0.77), and 88.5% and 88.2%, respectively, according to PP analysis (p = 1.00). The eradication rates for the TA group were not inferior to those of the EA group in both PP (p = 0.0023) and ITT analyses (p = 0.0009). There were no significant statistical differences in the incidence of adverse events and compliance between the two groups. The multivariate logistic regression analysis revealed that poor compliance increased the risk of eradication failure (p < 0.001). Conclusions: Dual therapy containing tegoprazan is safe and effective to be considered as a clinical first‐line treatment option, but further optimization involving antimicrobial susceptibility testing and adjustments in dosage and frequency is warranted. Trial Registration: ClinicalTrials.gov ID: NCT05870683. [ABSTRACT FROM AUTHOR]

In endoscopic submucosal dissection (ESD), the gastrointestinal (GI) tract warrants the surgical instruments to navigate through a long, narrow and tortuous endoscope. This poses a great challenge in developing ESD instruments with small dimensions, flexibility, and high distal dexterity. In this work, we propose the first Transendoscopic Flexible Parallel Continuum Robotic mechanism to develop a miniature dexterous flexible-stiff-balanced Wrist (FPCW). Besides, it can steer multifunctional instruments of diameters 2.5 mm to 3.5 mm, including the electrosurgical knife, injection needle, and forceps. Our FPCW instruments are adaptable to commercially available dual-channel endoscopes (diameter: <12 mm, channel width: 2.8 mm and around 3.8 mm). Furthermore, we develop a surgical telerobotic system, called DREAMS (Dual-arm Robotic Endoscopic Assistant for Minimally Invasive Surgery), by using our smallest FPCW instruments for bimanual ESD procedures. First, we conduct a series of experiments to determine the FPCW's design and kinematics parameters and to verify the mechanical properties of the FPCW instruments' prototypes, including workspace, stiffness, strength, and teleoperation accuracy. Second, we validate the functionality of the FPCW instruments through ex-vivo tests by performing ESD steps on porcine stomachs. Finally, we perform an invivo test on a live porcine model and showcase that our developed DREAMS can be teleoperated intuitively to perform bimanual ESD efficiently with an average dissection speed of 108.95 mm2/min at the greater curvature in gastric body, which demonstrates that our DREAMS has satisfactory maneuverability as well as accuracy and is more competitive than counterpart robotic systems. [ABSTRACT FROM AUTHOR]

Helicobacter pylori is a potential underlying cause of many diseases. Although the Carbon 13 breath test is considered the gold standard for detection, it is high cost and low public accessibility in certain areas limit its widespread use. In this study, we sought to use machine learning and deep learning algorithm models to classify and diagnose H. pylori infection status. We used hyperspectral imaging system to gather gastric juice images and then retrieved spectral feature information between 400 and 1000 nm. Two different data processing methods were employed, resulting in the establishment of one‐dimensional (1D) and two‐dimensional (2D) datasets. In the binary classification task, the random forest model achieved a prediction accuracy of 83.27% when learning features from 1D data, with a specificity of 84.56% and a sensitivity of 92.31%. In the ternary classification task, the ResNet model learned from 2D data and achieved a classification accuracy of 91.48%. [ABSTRACT FROM AUTHOR]