Colon polyps have become a focal point of research due to their heightened potential to develop into appendiceal cancer, which has the highest mortality rate globally. Although numerous colon polyp segmentation methods have been developed using public polyp datasets, they tend to underperform on private datasets due to inconsistencies in data distribution and the difficulty of fine-tuning without annotations. In this paper, we propose a Self-Adaptive Teacher-Student (SATS) framework to segment colon polyps from unannotated private data by utilizing multiple publicly annotated datasets. The SATS trains multiple teacher networks on public datasets and then generates pseudo-labels on private data to assist in training a student network. To enhance the reliability of the pseudo-labels from the teacher networks, the SATS includes a newly proposed Uncertainty and Distance Fusion (UDFusion) strategy. UDFusion dynamically adjusts the pseudo-label weights based on a novel reconstruction similarity measure, innovatively bridging the gap between private and public data distributions. To ensure accurate identification and segmentation of colon polyps, the SATS also incorporates a Granular Attention Network (GANet) architecture for both teacher and student networks. GANet first identifies polyps roughly from a global perspective by encoding long-range anatomical dependencies and then refines this identification to remove false-positive areas through multi-scale background-foreground attention. The SATS framework was validated using three public datasets and one private dataset, achieving 76.30% on IoU, 86.00% on Recall, and 7.01 pixels on HD. These results outperform the existing five methods, indicating the effectiveness of this approach for colon polyp segmentation.

BACKGROUND: To investigate the effect of different working periods on missed diagnoses in patients with colorectal polyps in colonoscopy.
METHODS: We conducted a retrospective analysis of patients who were diagnosed with colorectal polyps during colonoscopy in an outpatient department between July and December 2022. These patients were subsequently hospitalized for resection during this period. Patients with missed diagnoses were those who had newly discovered polyps in a second colonoscopy. The working periods were categorized as work, near the end of work, and delayed work, respectively, in the morning and afternoon.
RESULTS: A total of 482 patients were included, and the miss rate of diagnosis was 48.1% (232/482), mainly in the transverse colon (25%), and the ascending colon (23%). Patient age was a risk factor for the miss rate of diagnosis (OR = 1.025, 95%CI: 1.009-1.042, P = 0.003) and was also associated with the number of polyps detected for the first colonoscopy (χ2 = 18.196, P = 0.001). The different working periods had no statistical effect on the missed rate of diagnosis (χ2 = 1.998, P = 0.849). However, there was an increasing trend in miss rates towards the end of work and delayed work periods, both in the morning and afternoon. The highest miss rate (60.0%) was observed during delayed work in the afternoon. Additionally, poor bowel preparation was significantly more common during delayed work in the afternoon.
CONCLUSIONS: The increasing trend in miss rates towards the end of work and delayed work periods deserves clinical attention. Endoscopists cannot always stay in good condition under heavy workloads.

This study aims to analyze the risk factors associated with delayed postoperative bleeding (DPPB) following colorectal polyp surgery, develop a dynamic nomogram and evaluate the model efficacy, provide a reference for clinicians to identify the patients at high risk of DPPB. Retrospective study was done on patients who underwent endoscopic colorectal polypectomy at the First Hospital of Lanzhou University from January 2020 to March 2023. Differences between the group with and without DPPB were compared, and independent risk factors for DPPB occurrence were identified through univariate analysis and combination LASSO and logistic regression. A dynamic nomogram was constructed based on multiple logistic regression to predict DPPB following colorectal polyp surgery. Model evaluation included receiver operating characteristic (ROC), Calibration curve, Decision curve analysis (DCA). DPPB occurred in 38 of the 1544 patients included. multivariate analysis showed that direct oral anticoagulants (DOACs), polyp location in the right hemi colon, polyp diameter, drink, and prophylactic hemoclips were the independent risk factors for DPPB and dynamic nomogram were established. Model validation indicated area under the ROC curve values of 0.936, 0.796, and 0.865 for the training set, validation set, and full set, respectively. The calibration curve demonstrated a strong alignment between the predictions of the column-line diagram model and actual observations. The decision curve analysis (DCA) displayed a significant net clinical benefit across the threshold probability range of 0-100%. The dynamic nomogram aids clinicians in identifying high-risk patients, enabling personalized diagnosis and treatment.

Early detection of polyps is essential to decrease colorectal cancer(CRC) incidence. Therefore, developing an efficient and accurate polyp segmentation technique is crucial for clinical CRC prevention. In this paper, we propose an end-to-end training approach for polyp segmentation that employs diffusion model. The images are considered as priors, and the segmentation is formulated as a mask generation process. In the sampling process, multiple predictions are generated for each input image using the trained model, and significant performance enhancements are achieved through the use of majority vote strategy. Four public datasets and one in-house dataset are used to train and test the model performance. The proposed method achieves mDice scores of 0.934 and 0.967 for datasets Kvasir-SEG and CVC-ClinicDB respectively. Furthermore, one cross-validation is applied to test the generalization of the proposed model, and the proposed methods outperformed previous state-of-the-art(SOTA) models to the best of our knowledge. The proposed method also significantly improves the segmentation accuracy and has strong generalization capability.

Objective: This investigation sought to delineate the associations among colorectal adenomatous polyps, diabetes, and biomolecules involved in glucose metabolism. Method: Data were collected from 40 patients who underwent endoscopic polypectomy at the Endoscopy Department of Shandong Cancer Hospital between June 2019 and September 2021. This cohort included 27 patients with inflammatory polyps and 13 with adenomatous polyps. We assessed fasting insulin (Fins), fasting blood glucose (FBG), and the mRNA expressions of fibroblast growth factor 19 (FGF-19) and insulin-like growth factor 1 (IGF-1) in the polyp tissues. Both univariate and multivariate logistic regression analyses were employed to ascertain the determinants influencing the emergence of adenomatous polyps. From these analyses, a predictive nomogram was constructed to forecast the occurrence of adenomatous polyps, and evaluations on the discriminative capacity, calibration, and clinical utility of the model were conducted. Results: The adenomatous polyp group exhibited markedly elevated levels of glucose, insulin, FGF-19, and IGF-1, with respective concentrations of (8.67±2.70) mmol/L, (12.72±7.69) μU/L, 2.20±1.88, and 1.36±0.69. These figures were significantly higher compared to the inflammatory polyp group, which showed levels of (5.51±0.72) mmol/L, (5.49±2.68) μU/L, 0.53±0.97, and 0.41±0.46, respectively, P=0.001. Multivariate logistic regression revealed that the relative expression of IGF-1 served as an independent risk factor for the development of colorectal adenomatous polyps (OR=5.622, 95% CI:1.085-29.126). The nomogram displayed a C-index of 0.849, indicating substantial discriminative capability. The calibration curve affirmed the model\'s accuracy in aligning predicted probabilities with actual outcomes, and the clinical decision curve demonstrated thepractical clinical applicability of the model. Conclusions: There was a significant correlation between the occurrence of colorectal adenomatous polyps and glucose metabolic pathways. Individuals with diabetes showed a higher propensity to develop such polyps.
目的： 探讨结直肠腺瘤性息肉与糖尿病及糖代谢相关分子的关系。 方法： 收集2019年6月到2021年9月山东省肿瘤医院内镜科进行内镜下息肉切除术的40例患者，其中炎症性息肉27例，腺瘤性息肉13例。测量这些患者的空腹胰岛素、空腹血糖以及息肉组织中成纤维细胞生长因子19（FGF-19）和胰岛素样生长因子1（IGF-1）mRNA的表达。采用单因素和多因素logistic回归分析明确腺瘤性息肉发生的影响因素，基于多因素logistic回归分析结果构建预测腺瘤性息肉发生的列线图模型，并对模型进行区分度、校准度和临床适用性评价。 结果： 腺瘤性息肉组患者的血糖、胰岛素、FGF-19和IGF-1的相对表达量分别为（8.67±2.70）mmol/L、（12.72±7.69）μU/L、2.20±1.88和1.36±0.69，均高于炎症性息肉组［分别为（5.51±0.72）mmol/L、（5.49±2.68）μU/L、0.53±0.97和0.41±0.46，均P=0.001］。多因素logistic回归分析显示，IGF-1的相对表达量为结直肠腺瘤性息肉发生的独立危险因素（OR=5.622，95% CI：1.085～29.126）。根据多因素logistic回归分析结果成功构建腺瘤性息肉的预测列线图模型。列线图模型的C指数为0.849，表明列线图模型的区分度较好。校准曲线显示，列线图模型的预测概率与实际观测结果的一致性尚可。临床决策曲线显示，列线图模型有一定的临床适用性。 结论： 结直肠腺瘤性息肉的发生与糖代谢有关，糖尿病患者容易发生结直肠腺瘤。.

BACKGROUND: This study aimed to compare oral sulfate solution (OSS) with polyethylene glycol (PEG) for bowel preparation before colonoscopy.
METHODS: A literature search was performed on PubMed, Ovid, and Cochrane Databases for randomized clinical trials (RCT) comparing OSS with PEG for bowel preparation before colonoscopy. The last search was performed on 22 August 2023. The primary outcome was the quality of bowel preparation. The outcomes were compared by meta-analysis and trial sequential analysis (TSA).
RESULTS: A total of 14 RCTs with 4526 patients were included. OSS was comparable with PEG regarding adequate bowel preparation [P = 0.16, odds ratio (OR) = 1.19, 95% confidence interval (CI) [0.93, 1.51], I2 = 0%]. However, OSS showed obvious priority in excellent bowel preparation (P < 0.001, OR = 1.62, 95% CI [1.27, 2.05], I2 = 0%) and total Boston bowel preparation scale (BBPS) [P = 0.02, weighted mean difference (WMD) = 0.27, 95% CI [0.05, 0.50], I2 = 84%]. Additionally, the detection rate of polyps (P = 0.001, OR = 1.44, 95% CI [1.15, 1.80], I2 = 0%) and adenoma (P = 0.007, OR = 1.22, 95% CI [1.06, 1.42], I2 = 0%) was significantly higher in the OSS group. The two groups showed comparable incidence of adverse events except for a higher incidence of dizziness (P = 0.02, OR = 1.74, 95% CI [1.08, 2.83], I2 = 11%) was indicated in the OSS group. Moreover, OSS was associated with a higher satisfaction score (P = 0.02, WMD = 0.62, 95% CI [0.09, 1.15], I2 = 70%). In the TSA, the cumulative Z-curve crossed both the conventional boundary and trial sequential monitoring boundary and the required information size has been reached for excellent bowel preparation and total BBPS.
CONCLUSIONS: The current data demonstrated that OSS was associated with better quality of bowel preparation. More clinical trials are still needed to confirm other outcomes.

BACKGROUND: Construct deep learning models for colonoscopy quality control using different architectures and explore their decision-making mechanisms.
METHODS: A total of 4,189 colonoscopy images were collected from two medical centers, covering different levels of bowel cleanliness, the presence of polyps, and the cecum. Using these data, eight pre-trained models based on CNN and Transformer architectures underwent transfer learning and fine-tuning. The models\' performance was evaluated using metrics such as AUC, Precision, and F1 score. Perceptual hash functions were employed to detect image changes, enabling real-time monitoring of colonoscopy withdrawal speed. Model interpretability was analyzed using techniques such as Grad-CAM and SHAP. Finally, the best-performing model was converted to ONNX format and deployed on device terminals.
RESULTS: The EfficientNetB2 model outperformed other architectures on the validation set, achieving an accuracy of 0.992. It surpassed models based on other CNN and Transformer architectures. The model\'s precision, recall, and F1 score were 0.991, 0.989, and 0.990, respectively. On the test set, the EfficientNetB2 model achieved an average AUC of 0.996, with a precision of 0.948 and a recall of 0.952. Interpretability analysis showed the specific image regions the model used for decision-making. The model was converted to ONNX format and deployed on device terminals, achieving an average inference speed of over 60 frames per second.
CONCLUSIONS: The AI-assisted quality system, based on the EfficientNetB2 model, integrates four key quality control indicators for colonoscopy. This integration enables medical institutions to comprehensively manage and enhance these indicators using a single model, showcasing promising potential for clinical applications.

Automatic polyp segmentation in endoscopic images is critical for the early diagnosis of colorectal cancer. Despite the availability of powerful segmentation models, two challenges still impede the accuracy of polyp segmentation algorithms. Firstly, during a colonoscopy, physicians frequently adjust the orientation of the colonoscope tip to capture underlying lesions, resulting in viewpoint changes in the colonoscopy images. These variations increase the diversity of polyp visual appearance, posing a challenge for learning robust polyp features. Secondly, polyps often exhibit properties similar to the surrounding tissues, leading to indistinct polyp boundaries. To address these problems, we propose a viewpoint-aware framework named VANet for precise polyp segmentation. In VANet, polyps are emphasized as a discriminative feature and thus can be localized by class activation maps in a viewpoint classification process. With these polyp locations, we design a viewpoint-aware Transformer (VAFormer) to alleviate the erosion of attention by the surrounding tissues, thereby inducing better polyp representations. Additionally, to enhance the polyp boundary perception of the network, we develop a boundary-aware Transformer (BAFormer) to encourage self-attention towards uncertain regions. As a consequence, the combination of the two modules is capable of calibrating predictions and significantly improving polyp segmentation performance. Extensive experiments on seven public datasets across six metrics demonstrate the state-of-the-art results of our method, and VANet can handle colonoscopy images in real-world scenarios effectively. The source code is available at https://github.com/1024803482/Viewpoint-Aware-Network.

Colorectal polyps serve as potential precursors of colorectal cancer and automating polyp segmentation aids physicians in accurately identifying potential polyp regions, thereby reducing misdiagnoses and missed diagnoses. However, existing models often fall short in accurately segmenting polyps due to the high degree of similarity between polyp regions and surrounding tissue in terms of color, texture, and shape. To address this challenge, this study proposes a novel three-stage polyp segmentation network, named Reverse Attention Feature Purification with Pyramid Vision Transformer (RAFPNet), which adopts an iterative feedback UNet architecture to refine polyp saliency maps for precise segmentation. Initially, a Multi-Scale Feature Aggregation (MSFA) module is introduced to generate preliminary polyp saliency maps. Subsequently, a Reverse Attention Feature Purification (RAFP) module is devised to effectively suppress low-level surrounding tissue features while enhancing high-level semantic polyp information based on the preliminary saliency maps. Finally, the UNet architecture is leveraged to further refine the feature maps in a coarse-to-fine approach. Extensive experiments conducted on five widely used polyp segmentation datasets and three video polyp segmentation datasets demonstrate the superior performance of RAFPNet over state-of-the-art models across multiple evaluation metrics.

OBJECTIVE: The fragmentation of polyps affects complete resection confirmation. The primary aim of this study was to assess the feasibility of a novel polyp retrieval bag for reducing the fragmentation rate of colon polyps.
METHODS: Patients with a 5-15 mm colon polyp were recruited and randomized into two groups at a 1:1 ratio. After polyp resection, polyps obtained from patients in the treatment group were extracted via a novel polyp retrieval bag without traversing the instrument channel, whereas polyps obtained from patients in the control group were collected through the instrument channel, attaching the polyp trap to the instrument channel port, and applying suction.
RESULTS: From January to July 2022, 225 patients were assessed for eligibility. The study participants included 204 patients, and seven patients whose samples were not retrieved were excluded. Polyp fragmentation was significantly lower in the treatment group than in the control group (3.0% [3/100] vs. 17.5% [17/97], P = 0.001). The retrieval failure rates in the treatment group and control group were not significantly different (2.0% [2/102] vs. 4.9% [5/102], P = 0.442). There were fewer colonoscope insertions in the treatment group than in the control group (102 vs. 110), but a significant difference was not present (P = 0.065). No significant adverse events were observed during the follow-up.
CONCLUSIONS: This study demonstrated that the polyp retrieval bag was safe and feasible for reducing the fragmentation rate of retrieved polyps.
BACKGROUND: The study was registered at ClinicalTrials.gov (NCT05189912, 1/12/2021).