A prospective multicenter randomized controlled trial on artificial intelligence assisted colonoscopy for enhanced polyp detection.

Colon polyp detection and removal via colonoscopy are essential for colorectal cancer screening and prevention. This study aimed to develop a colon polyp detection program based on the RetinaNet algorithm and verify its clinical utility. To develop the AI-assisted program, the dataset was fully anonymized and divided into 10 folds for 10-fold cross-validation. Each fold consisted of 9,639 training images and 1,070 validation images. Video data from 56 patients were used for model training, and transfer learning was performed using the developed still image-based model. The final model was developed as a real-time polyp-detection program for endoscopy. To evaluate the model's performance, a prospective randomized controlled trial was conducted at six institutions to compare the polyp detection rates (PDR). A total of 805 patients were included. The group that utilized the AI model showed significantly higher PDR and adenoma detection rate (ADR) than the group that underwent colonoscopy without AI assistance. Multivariate analysis revealed an OR of 1.50 for cases where polyps were detected. The AI-assisted polyp-detection program is clinically beneficial for detecting polyps during colonoscopy. By utilizing this AI-assisted program, clinicians can improve adenoma detection rates, ultimately leading to enhanced cancer prevention.

Empirical Therapy Versus Tailored Therapy of Helicobacter pylori in Korea: Results of the K-CREATE Study.

BACKGROUND: The optimal duration of regimens for tailored therapy based on genotypic resistance for clarithromycin has yet to be established. AIM: This study was a nationwide, multicenter, randomized trial comparing empirical therapy with tailored therapy based on genotypic resistance for first-line eradication of Helicobacter pylori. We also compared the eradication rates of 7- and 14-day regimens for each group. PATIENTS AND METHODS: Patients with H. pylori infection were first randomized to receive empirical or tailored therapy. Patients in each group were further randomized into 7- or 14-day regimens. Empirical therapy consisted of a triple therapy (TT) regimen (twice-daily doses of pantoprazole 40 mg, amoxicillin 1 g, and clarithromycin 500 mg) for 7 or 14 days. Tailored therapy consisted of TT of 7 or 14 days in patients without genotypic resistance. Patients with genotypic resistance were treated with bismuth quadruple therapy (BQT) regimens (twice-daily doses of pantoprazole 40 mg, three daily doses of metronidazole 500 mg, and four times daily doses of bismuth 300 mg and tetracycline 500 mg) for 7 or 14 days. A 13C-urea breath test assessed eradication rates. The primary outcome was eradication rates of each group. RESULTS: A total of 593 patients were included in the study. The eradication rates were 65.7% (201/306) in the empirical therapy group and 81.9% (235/287) in the tailored therapy group for intention-to-treat analysis (p < 0.001). In the per-protocol analysis, the eradication rates of the empirical therapy and tailored groups were 70.3% (201/286) and 85.5% (235/274) (p < 0.001), respectively. There was no difference in compliance between the two groups. The rate of adverse events was higher in the tailored group compared to the empirical group (p < 0.001). DISCUSSION: Our study confirmed that tailored therapy based on genotypic resistance was more effective than empirical therapy for H. pylori eradication in Korea. However, no significant difference was found between 7- and 14-day regimens for each group. Future studies are needed to determine the optimal duration of therapy for empirical and tailored therapy regimens.

Deep Learning-Based Real-Time Organ Localization and Transit Time Estimation in Wireless Capsule Endoscopy.

BACKGROUND: Wireless capsule endoscopy (WCE) has significantly advanced the diagnosis of gastrointestinal (GI) diseases by allowing for the non-invasive visualization of the entire small intestine. However, machine learning-based methods for organ classification in WCE often rely on color information, leading to decreased performance when obstacles such as food debris are present. This study proposes a novel model that integrates convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to analyze multiple frames and incorporate temporal information, ensuring that it performs well even when visual information is limited. METHODS: We collected data from 126 patients using PillCam™ SB3 (Medtronic, Minneapolis, MN, USA), which comprised 2,395,932 images. Our deep learning model was trained to identify organs (stomach, small intestine, and colon) using data from 44 training and 10 validation cases. We applied calibration using a Gaussian filter to enhance the accuracy of detecting organ boundaries. Additionally, we estimated the transit time of the capsule in the gastric and small intestine regions using a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) designed to be aware of the sequence information of continuous videos. Finally, we evaluated the model's performance using WCE videos from 72 patients. RESULTS: Our model demonstrated high performance in organ classification, achieving an accuracy, sensitivity, and specificity of over 95% for each organ (stomach, small intestine, and colon), with an overall accuracy and F1-score of 97.1%. The Matthews Correlation Coefficient (MCC) and Geometric Mean (G-mean) were used to evaluate the model's performance on imbalanced datasets, achieving MCC values of 0.93 for the stomach, 0.91 for the small intestine, and 0.94 for the colon, and G-mean values of 0.96 for the stomach, 0.95 for the small intestine, and 0.97 for the colon. Regarding the estimation of gastric and small intestine transit times, the mean time differences between the model predictions and ground truth were 4.3 ± 9.7 min for the stomach and 24.7 ± 33.8 min for the small intestine. Notably, the model's predictions for gastric transit times were within 15 min of the ground truth for 95.8% of the test dataset (69 out of 72 cases). The proposed model shows overall superior performance compared to a model using only CNN. CONCLUSIONS: The combination of CNN and LSTM proves to be both accurate and clinically effective for organ classification and transit time estimation in WCE. Our model's ability to integrate temporal information allows it to maintain high performance even in challenging conditions where color information alone is insufficient. Including MCC and G-mean metrics further validates the robustness of our approach in handling imbalanced datasets. These findings suggest that the proposed method can significantly improve the diagnostic accuracy and efficiency of WCE, making it a valuable tool in clinical practice for diagnosing and managing GI diseases.

Efficacy and safety of three-dimensional magnetically assisted capsule endoscopy for upper gastrointestinal and small bowel examination.

BACKGROUND: Magnetically assisted capsule endoscopy (MACE) showed the feasibility for upper gastrointestinal examination. To further enhance the performance of conventional MACE, it is necessary to provide quality-improved and three-dimensional images. The aim of this clinical study was to determine the efficacy and safety of novel three-dimensional MACE (3D MACE) for upper gastrointestinal and small bowel examination at once. METHODS: This was a prospective, single-center, non-randomized, and sequential examination study (KCT0007114) at Dongguk University Ilsan Hospital. Adult patients who visited for upper endoscopy were included. The study protocol was conducted in two stages. First, upper gastrointestinal examination was performed using 3D MACE, and a continuous small bowel examination was performed by conventional method of capsule endoscopy. Two hours later, an upper endoscopy was performed for comparison with 3D MACE examination. The primary outcome was confirmation of major gastric structures (esophagogastric junction, cardia/fundus, body, angle, antrum, and pylorus). Secondary outcomes were confirmation of esophagus and duodenal bulb, accuracy for gastric lesions, completion of small bowel examination, 3D image reconstruction of gastric lesion, and safety. RESULTS: Fifty-five patients were finally enrolled. The examination time of 3D MACE was 14.84 ± 3.02 minutes and upper endoscopy was 5.22 ± 2.39 minutes. The confirmation rate of the six major gastric structures was 98.6% in 3D MACE and 100% in upper endoscopy. Gastric lesions were identified in 43 patients during 3D MACE, and 40 patients during upper endoscopy (Sensitivity 0.97). 3D reconstructed images were acquired for all lesions inspected by 3D MACE. The continuous small bowel examination by 3D MACE was completed in 94.5%. 3D MACE showed better overall satisfaction (3D MACE 9.55 ± 0.79 and upper endoscopy 7.75 ± 2.34, p<0.0001). There were no aspiration or significant adverse event or capsule retention in the 3D MACE examination. CONCLUSIONS: Novel 3D MACE system is more advanced diagnostic modality than the conventional MACE. And it is possible to perform serial upper gastrointestinal and small bowel examination as a non-invasive and one-step test. It would be also served as a bridge to pan-endoscopy.

Osteoporosis Is Associated with an Increased Risk of Colorectal Neoplasms Regardless of Sex: Nationwide Population-Based Cohort Study.

Vitamin D may have anticancer effects against colorectal cancer (CRC). Bone mineral density (BMD) reflects the long-term vitamin D status. This study investigated the association between osteoporosis and colorectal neoplasms (CRN). The data were obtained from the National Health Insurance Service sample cohort, which included 60,386 osteoporosis patients and 8224 controls who underwent BMD in 2002-2019. The logistic regression models included age, sex, income level, and comorbidity. Sensitivity tests were performed using the data from the National Health Screening Program. In total, 7706 (11.2%) patients were diagnosed with CRN, and the proportion was significantly higher in osteoporosis patients than in controls (11.7% vs. 8.1%). In the multivariate analysis, osteoporosis was associated with an increased risk of CRN (odds ratio (OR) = 1.91, 95% confidence interval = 1.75-2.09, p < 0.0001), which was significant for both colorectal adenomas and CRC (OR = 1.88 and 1.83, respectively). A subgroup analysis by sex revealed a significant association between osteoporosis and CRN in both women and men (OR = 2.06 and 1.66, respectively). The sensitivity tests revealed results similar to those of the original dataset. In conclusion, osteoporosis is significantly associated with CRN risk in both sexes. In high-risk patients with low BMD, appropriate screening for CRN and vitamin D supplementation are required, regardless of sex.

The Effect of Tegoprazan on the Treatment of Endoscopic Resection-Induced Artificial Ulcers: A Multicenter, Randomized, Active-Controlled Study.

Background/Aims: : Tegoprazan is a novel potassium-competitive acid blocker that has beneficial effects on acid-related disorders such as gastroesophageal reflux and peptic ulcer diseases. This study aimed to validate the effect of tegoprazan on endoscopic submucosal dissection (ESD)-induced artificial ulcers. Methods: : Patients from 16 centers in Korea who underwent ESD for gastric neoplasia were enrolled. After ESD, pantoprazole was administered intravenously for 48 hours. The patients were randomly allocated to either the tegoprazan or esomeprazole group. Tegoprazan 50 mg or esomeprazole 40 mg were administered for 4 weeks, after which gastroscopic evaluation was performed. If the artificial ulcer had not healed, the same dose of tegoprazan or esomeprazole was administered for an additional 4 weeks, and a gastroscopic evaluation was performed. Results: : One hundred sixty patients were enrolled in this study. The healing rates of artificial ulcers at 4 weeks were 30.3% (23/76) and 22.1% (15/68) in the tegoprazan and esomeprazole groups, respectively (p=0.006). At 8 weeks after ESD, the cumulative ulcer healing rates were 73.7% (56/76) and 77.9% (53/68) in the tegoprazan and esomeprazole groups, respectively (p=0.210). Delayed bleeding occurred in two patients in the tegoprazan group (2.6%) and in one patient in the esomeprazole group (1.5%). Other adverse events were negligible in both groups. Conclusions: : Tegoprazan showed similar effects on post-ESD artificial ulcer healing in comparison with esomeprazole.

Reading of small bowel capsule endoscopy after frame reduction using an artificial intelligence algorithm.

OBJECTIVES: Poorly visualized images that appear during small bowel capsule endoscopy (SBCE) can confuse the interpretation of small bowel lesions and increase the physician's workload. Using a validated artificial intelligence (AI) algorithm that can evaluate the mucosal visualization, we aimed to assess whether SBCE reading after the removal of poorly visualized images could affect the diagnosis of SBCE. METHODS: A study was conducted to analyze 90 SBCE cases in which a small bowel examination was completed. Two experienced endoscopists alternately performed two types of readings. They used the AI algorithm to remove poorly visualized images for the frame reduction reading (AI user group) and conducted whole frame reading without AI (AI non-user group) for the same patient. A poorly visualized image was defined as an image with < 50% mucosal visualization. The study outcomes were diagnostic concordance and reading time between the two groups. The SBCE diagnosis was classified as Crohn's disease, bleeding, polyp, angiodysplasia, and nonspecific finding. RESULTS: The final SBCE diagnoses between the two groups showed statistically significant diagnostic concordance (k = 0.954, p < 0.001). The mean number of lesion images was 3008.5 ± 9964.9 in the AI non-user group and 1401.7 ± 4811.3 in the AI user group. There were no cases in which lesions were completely removed. Compared with the AI non-user group (120.9 min), the reading time was reduced by 35.6% in the AI user group (77.9 min). CONCLUSIONS: SBCE reading after reducing poorly visualized frames using the AI algorithm did not have a negative effect on the final diagnosis. SBCE reading method integrated with frame reduction and mucosal visualization evaluation will help improve AI-assisted SBCE interpretation.

The combined use of anti-peptic agents is associated with an increased risk of osteoporotic fracture: a nationwide case-control study.

BACKGROUND/AIMS: Long-term use of acid suppressants such as proton pump inhibitors (PPIs) and histamine 2 receptor antagonist (H2RA) has been associated with the risk of osteoporotic fracture. Acid suppressants and muco-protective agents (MPAs) are often used together as anti-ulcer agents. We evaluated the association between the risk of osteoporotic fracture and the combined use of these anti-peptic agents. METHODS: A population-based case-control study was conducted by analyzing the Korean National Health Insurance Data from 2014 to 2020. Patients who had been prescribed anti-peptic agents, such as PPI, H2RA, or MPA, were included. Considering the incidence of osteoporotic fractures, the case group (n = 14,704) and control group (n = 58,816) were classified by 1:4 matching based on age and sex. RESULTS: The use of all types of anti-peptic agents was associated with an increased risk of osteoporotic fractures (PPI: hazard osteoratio [HR], 1.31; H2RA: HR, 1.44; and MPA: HR, 1.33; all p < 0.001). Compared to PPI alone, the combined use of "PPI and H2RA" (HR, 1.58; p = 0.010) as well as "PPI, H2RA, and MPA" (HR, 1.71; p = 0.001) was associated with an increased risk of osteoporotic fracture. However, compared with PPI alone, "MPA and PPI or H2RA" was not associated with an increased risk of osteoporotic fracture. CONCLUSION: This study found that the combined use of "PPI and H2RA" was associated with a higher risk of osteoporotic fractures. In cases where deemed necessary, the physicians may initially consider prescribing the combination use of MPA.

Reduced risk of gastrointestinal bleeding associated with eupatilin in aspirin plus acid suppressant users: nationwide population-based study.

BACKGROUND/AIMS: Mucoprotective agents, such as eupatilin, are often prescribed to prevent gastrointestinal (GI) bleeding in addition to an acid suppressant despite the absence of a large-scale study. We evaluated the additional effect of eupatilin on the prevention of GI bleeding in both the upper and lower GI tract in concomitant aspirin and acid suppressant users using the nationwide database of national claims data from the Korean National Health Insurance Service (NHIS). METHODS: An aspirin cohort was constructed using the NHIS claims data from 2013 to 2020. Patients who manifested with hematemesis, melena, or hematochezia were considered to have GI bleeding. A Cox proportional hazards regression model was used to determine the risk factors for GI bleeding associated with the concomitant use of GI drugs and other covariates among aspirin users. RESULTS: Overall, a total of 432,208 aspirin users were included. The concurrent use of an acid suppressant and eupatilin (hazard ratio [HR] = 0.85, p = 0.016, vs. acid suppressant only) was a statistically significant preventive factor for GI bleeding. Moreover, a more than 3-month duration (HR = 0.88, p = 0.030) of acid suppressant and eupatilin prescription (vs. acid suppressant only) was a statistically significant preventive factor for GI bleeding. CONCLUSION: Eupatilin administration for ≥ 3 months showed additional preventive effect on GI bleeding in concomitant aspirin and acid suppressant users. Thus, cotreatment with eupatilin with a duration of 3 months or longer is recommended for reducing GI bleeding among aspirin plus acid suppressant users.

The Advent of Domain Adaptation into Artificial Intelligence for Gastrointestinal Endoscopy and Medical Imaging.

Artificial intelligence (AI) is a subfield of computer science that aims to implement computer systems that perform tasks that generally require human learning, reasoning, and perceptual abilities. AI is widely used in the medical field. The interpretation of medical images requires considerable effort, time, and skill. AI-aided interpretations, such as automated abnormal lesion detection and image classification, are promising areas of AI. However, when images with different characteristics are extracted, depending on the manufacturer and imaging environment, a so-called domain shift problem occurs in which the developed AI has a poor versatility. Domain adaptation is used to address this problem. Domain adaptation is a tool that generates a newly converted image which is suitable for other domains. It has also shown promise in reducing the differences in appearance among the images collected from different devices. Domain adaptation is expected to improve the reading accuracy of AI for heterogeneous image distributions in gastrointestinal (GI) endoscopy and medical image analyses. In this paper, we review the history and basic characteristics of domain shift and domain adaptation. We also address their use in gastrointestinal endoscopy and the medical field more generally through published examples, perspectives, and future directions.

Video Analysis of Small Bowel Capsule Endoscopy Using a Transformer Network.

Although wireless capsule endoscopy (WCE) detects small bowel diseases effectively, it has some limitations. For example, the reading process can be time consuming due to the numerous images generated per case and the lesion detection accuracy may rely on the operators' skills and experiences. Hence, many researchers have recently developed deep-learning-based methods to address these limitations. However, they tend to select only a portion of the images from a given WCE video and analyze each image individually. In this study, we note that more information can be extracted from the unused frames and the temporal relations of sequential frames. Specifically, to increase the accuracy of lesion detection without depending on experts' frame selection skills, we suggest using whole video frames as the input to the deep learning system. Thus, we propose a new Transformer-architecture-based neural encoder that takes the entire video as the input, exploiting the power of the Transformer architecture to extract long-term global correlation within and between the input frames. Subsequently, we can capture the temporal context of the input frames and the attentional features within a frame. Tests on benchmark datasets of four WCE videos showed 95.1% sensitivity and 83.4% specificity. These results may significantly advance automated lesion detection techniques for WCE images.

Impact of Diet on Colorectal Cancer Progression and Prevention: From Nutrients to Neoplasms.

Colorectal cancer (CRC), one of the most common cancers worldwide, continues to increase in incidence and mortality rates. This trend is closely linked to changes in dietary habits, which are major risk factors for colorectal cancer. The increase in the incidence of CRC in countries previously considered low-risk and with low socioeconomic status is most likely due to lifestyle and dietary changes. Understanding the influence of dietary factors on the onset of colorectal cancer is essential for prevention and treatment. This review explores the complex interplay between dietary factors and colorectal cancer, focusing on the key nutrients and dietary habits that influence disease onset and progression. The impact of diet on colorectal microbiota and the influence of diet on early-onset colorectal cancer are also reviewed, reviewing recent research on how dietary interventions affect the treatment and recurrence of colorectal cancer. Finally, the future research directions for developing and applying effective dietary intervention strategies are discussed.

Association between eupatilin and reduction in small bowel bleeding in aspirin users and aspirin plus acid suppressant users.

BACKGROUND/AIMS: Capsule endoscopy (CE) has shown that low-dose aspirin occasionally causes small bowel (SB) bleeding. We herein evaluated the protective effects of mucoprotective agents (MPAs) on SB bleeding in aspirin users using the nationwide database of claims data from the National Health Insurance Service (NHIS). METHODS: As CE is an insured procedure, we constructed an aspirin-SB cohort using NHIS claims data, with a maximum follow- up period of 24 months. Patients with anemia, melena, or hematochezia that occurred within 4 weeks before and after performing CE were suspected to have SB bleeding. A Cox proportional hazards regression model was used to determine the risk factors for SB bleeding. Subgroup analyses were conducted among patients who used acid suppressants, such as proton pump inhibitors (PPIs) and histamine-2 receptor antagonists. RESULTS: A total of 15,542 aspirin users were included. Anticoagulant use (hazard ratio [HR], 3.22), high Charlson comorbidity index score (≥ 2) (HR, 3.54), and PPI use (HR, 2.85) were significantly associated with SB bleeding, whereas eupatilin use (HR, 0.35) was a preventive factor. SB bleeding occurred more frequently in concurrent users of acid suppressants than in nonusers (1.3% vs. 0.5%). Subgroup analysis revealed that eupatilin significantly reduced the risk of SB bleeding in aspirin users with concurrent use of acid suppressants (HR, 0.23 vs. 2.55). CONCLUSION: Eupatilin was associated with a reduced risk of SB bleeding in both aspirin users and those with concomitant use of acid suppressants. Eupatilin use should be considered for aspirin users, especially for those concomitantly taking acid suppressants.

Automatic Classification of GI Organs in Wireless Capsule Endoscopy Using a No-Code Platform-Based Deep Learning Model.

The first step in reading a capsule endoscopy (CE) is determining the gastrointestinal (GI) organ. Because CE produces too many inappropriate and repetitive images, automatic organ classification cannot be directly applied to CE videos. In this study, we developed a deep learning algorithm to classify GI organs (the esophagus, stomach, small bowel, and colon) using a no-code platform, applied it to CE videos, and proposed a novel method to visualize the transitional area of each GI organ. We used training data (37,307 images from 24 CE videos) and test data (39,781 images from 30 CE videos) for model development. This model was validated using 100 CE videos that included "normal", "blood", "inflamed", "vascular", and "polypoid" lesions. Our model achieved an overall accuracy of 0.98, precision of 0.89, recall of 0.97, and F1 score of 0.92. When we validated this model relative to the 100 CE videos, it produced average accuracies for the esophagus, stomach, small bowel, and colon of 0.98, 0.96, 0.87, and 0.87, respectively. Increasing the AI score's cut-off improved most performance metrics in each organ (p < 0.05). To locate a transitional area, we visualized the predicted results over time, and setting the cut-off of the AI score to 99.9% resulted in a better intuitive presentation than the baseline. In conclusion, the GI organ classification AI model demonstrated high accuracy on CE videos. The transitional area could be more easily located by adjusting the cut-off of the AI score and visualization of its result over time.

Small Bowel Capsule Endoscopy within 6 Hours Following Bowel Preparation with Polyethylene Glycol Shows Improved Small Bowel Visibility.

Although bowel preparation influences small bowel visibility for small bowel capsule endoscopy (SBCE), the optimal timing for bowel preparation has not been established yet. Thus, the aim of the study was to evaluate the optimal timing of polyethylene glycol (PEG) for small bowel preparation before SBCE. This multicenter prospective observational study was conducted on patients who underwent SBCE following bowel preparation with polyethylene glycol (PEG). Patients were categorized into three groups according to the time used for completing PEG ingestion: group A, within 6 h; group B, 6-12 h; and group C, over 12 h. The percentage of unclean segment in small bowel (unclean image duration / small bowel transit time × 100) and small bowel visibility quality (SBVQ) were evaluated according to the time interval between the last ingestion of PEG and swallowing of small bowel capsule endoscope. A total of 90 patients were enrolled and categorized into group A (n = 40), group B (n = 27), and group C (n = 23). The percentage of unclean segment in the entire small bowel increased gradually from group A to C (6.6 ± 7.6% in group A, 11.3 ± 11.8% in group B, and 16.2 ± 10.7% in group C, p = 0.001), especially in the distal small bowel (11.4 ± 13.6% in group A, 20.7 ± 18.7% in group B, and 29.5 ± 16.4% in group C, p < 0.001). The proportion of patients with adequate SBVQ in group A was significantly (p < 0.001) higher (30/40, 75.0%) than that in group B (17/27, 63.0%) or group C (5/23, 21.7%). In multivariate analysis, group A was associated with an increased likelihood of adequate SBVQ compared with group C (odds ratio [OR]: 13.05; 95% confidence interval [CI]: 3.53-48.30, p < 0.001). Completing PEG ingestion within 6 h prior to SBCE could enhance small bowel visibility.

Taking Oral Sulfate Tablets with Simethicone for Bowel Preparation Leads to Higher Adenoma Detection Rate than Polyethylene Glycol: A Propensity Score Analysis.

BACKGROUND: Efficient bowel preparation is essential for preventing colorectal cancer by improving endoscopic adenoma detection. Tablet for bowel preparation containing sulfate salts, OSTs (oral sulfate tablets), has been developed and it is gaining more popularity. However, its efficacy compared to standard preparation agent, PEG-AA (polyethylene glycol), has not been well discovered. We assessed the efficacy of PEG and OSTs using a real-time clinical data warehouse (CDW) model. METHODS: We performed a propensity score-matched (PSM) analysis of consecutive adult patients undergoing colonoscopy who received PEG-AA or OSTs prior to colonoscopy at a tertiary academic hospital. The endoscopic records of 992 adult patients were retrospectively analyzed. The clinical data warehouse collected data including bowel preparation, insertion time, observation time, and the detection of polyps and adenomas. Multivariate regression analysis was performed to reveal the factors associated with endoscopic outcomes. RESULTS: Among 992 patients included in the study, 770 and 222 patients received PEG-AA and OSTs, respectively. Among the propensity score-matched population (n = 1897), OSTs resulted in better bowel cleansing quality (8.16 vs 7.84, p = 0.014) and a higher adenoma detection rate (38.6% vs 27.1%, p = 0.003). Using PEG-AA, older age, inadequate bowel preparation (BBPS score < 6) and endoscopy by fellows were found to be factors associated with poor adenoma detection. In the elderly over 65 years of age, a significant difference in cleansing quality between the two groups (7.21 vs 8.19, p < 0.001) was found, but its impact on ADR was not prominent (49.5% vs 45.4%, p = 0.653). CONCLUSIONS: OSTs with simethicone achieved better endoscopic cleanliness, improving adenoma detection rate compared to the conventional PEG-AA protocol. The synergistic effect of both the convenience of taking tablets and the reduction of intraluminal bubble by adjunctive simethicone improves the clinical efficacy of colonoscopy.

Small bowel cleanliness in capsule endoscopy: a case-control study using validated artificial intelligence algorithm.

Small bowel capsule endoscopy (SBCE) may need to be performed immediately after colonoscopy without additional bowel preparation if active small bowel diseases are suspected. However, it is unclear whether the small bowel cleanliness is adequately maintained even after SBCE is performed immediately after colonoscopy. We compared the small bowel cleanliness scores of the study group (SBCE immediately after colonoscopy) and control group (SBCE alone) using a validated artificial intelligence (AI) algorithm (cut-off score > 3.25 for adequate). Cases of SBCE in which polyethylene glycol was used were included retrospectively. Among 85 enrolled cases, 50 cases (58.8%) were the study group. The mean time from the last dose of purgative administration to SBCE was 6.86 ± 0.94 h in the study group and 3.00 ± 0.18 h in the control group. Seventy-five cases (88.2%) were adequate small bowel cleanliness, which was not different between the two groups. The mean small bowel cleanliness score for the study group was 3.970 ± 0.603, and for the control group was 3.937 ± 0.428. In the study group, better colon preparation resulted in a higher small bowel cleanliness score (p = 0.015). Small bowel cleanliness was also adequately maintained in SBCE immediately after colonoscopy. There was no difference between the time and volume of purgative administration and small bowel cleanliness.

Vitamin D and Colorectal Cancer: Current Perspectives and Future Directions.

Vitamin D is considered to be the main mediator of the beneficial effects of sun exposure. In humans, highest expression of Vitamin D receptors is found in the intestinal tract. In addition, 1α,25-dihydroxyvitamin D3 (or calcitriol), the most active Vitamin D metabolite, plays important homeostatic roles in the intestine, particularly calcium absorption. Vitamin D deficiency is defined as a serum 25-hydroxyvitamin D [25(OH)D] level of < 20 ng/mL. Previous studies show that higher circulating 25(OH)D levels are associated with reduced risk of colorectal cancer (CRC) and improved survival. Most research to date has been conducted in animals, specifically mice. Although human studies have a limited number of participants, one study recruiting a large cohort of patients with advanced or metastatic CRC revealed that higher plasma 25(OH)D levels are associated with improved overall and progression-free survival. However, the effects of Vitamin D supplementation on incidence and mortality of CRC remain inconclusive. Although Vitamin D may help to prevent cancer, there is a paucity of research demonstrating conclusively that Vitamin D alters prognosis after chemotherapy. Here, we review the mechanisms by which Vitamin D affects CRC, as well as the results of clinical, epidemiological, and human intervention studies. We also discuss current perspectives and future directions regarding Vitamin D and CRC.

Dietary Intervention for Preventing Colorectal Cancer: A Practical Guide for Physicians.

Colorectal cancer (CRC) is a disease with high prevalence and mortality. Estimated preventability for CRC is approximately 50%, indicating that altering modifiable factors, including diet and body weight, can reduce CRC risk. There is strong evidence that dietary factors including whole grains, high-fiber, red and processed meat, and alcohol can affect the risk of CRC. An alternative strategy for preventing CRC is use of a chemopreventive supplement that provides higher individual exposure to nutrients than what can be obtained from the diet. These include calcium, vitamin D, folate, n-3 polyunsaturated fatty acids, and phytochemicals. Several intervention trials have shown that these dietary chemopreventives have positive protective effects on development and progression CRC. Research on chemoprevention with phytochemicals that possess anti-inflammatory and/or, anti-oxidative properties is still in the preclinical phase. Intentional weight loss by bariatric surgery has not been effective in decreasing long-term CRC risk. Physicians should perform dietary education for patients who are at high risk of cancer for changing their dietary habits and behaviour. An increased understanding of the role of individual nutrients linked to the intestinal micro-environment and stages of carcinogenesis would facilitate the development of the best nutritional formulations for preventing CRC.

Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering.

By automatically classifying the stomach, small bowel, and colon, the reading time of the wireless capsule endoscopy (WCE) can be reduced. In addition, it is an essential first preprocessing step to localize the small bowel in order to apply automated small bowel lesion detection algorithms based on deep learning. The purpose of the study was to develop an automated small bowel detection method from long untrimmed videos captured from WCE. Through this, the stomach and colon can also be distinguished. The proposed method is based on a convolutional neural network (CNN) with a temporal filtering on the predicted probabilities from the CNN. For CNN, we use a ResNet50 model to classify three organs including stomach, small bowel, and colon. The hybrid temporal filter consisting of a Savitzky-Golay filter and a median filter is applied to the temporal probabilities for the "small bowel" class. After filtering, the small bowel and the other two organs are differentiated with thresholding. The study was conducted on dataset of 200 patients (100 normal and 100 abnormal WCE cases), which was divided into a training set of 140 cases, a validation set of 20 cases, and a test set of 40 cases. For the test set of 40 patients (20 normal and 20 abnormal WCE cases), the proposed method showed accuracy of 99.8% in binary classification for the small bowel. Transition time errors for gastrointestinal tracts were only 38.8 ± 25.8 seconds for the transition between stomach and small bowel and 32.0 ± 19.1 seconds for the transition between small bowel and colon, compared to the ground truth organ transition points marked by two experienced gastroenterologists.