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Deep learning techniques can accurately detect and grade abnormal findings on images from Wireless Capsule Endoscopy (WCE). However, the prediction accuracy of handcrafted or deep learning in red Lesion, polyp and ulcer diseases is still under investigation. Knowing the utility of an automatic method for abnormalities detection from WCE images and how helpful it might be for the physicians, we proposed a new methodology in approaching this field. In this paper, patches with fixed size are extracted from WCE images, then, encoded using linear projection and position embedding and passed through an embedding model in a forward pass. Moreover, triplet loss is employed to adjust the embeddings. Afterwards, the trained embedding model is exploited for classification. Two strategies are followed in the design of the embedding model namely; training from scratch and fine tuning. The presented scheme, attains satisfactory results in different datasets compared to existing approaches. The detection accuracy has reached 99.9% in some used datasets. [ABSTRACT FROM AUTHOR]

This review presents an in-depth examination of implantable antennas for various biomedical purposes. The development of implantable antennas, including their designs, materials, and operating principles, are introduced at the beginning of the discussion. An overview of the many kinds of implantable antennas utilized in implantable medical devices (IMDs) are presented in this study. The article then discusses the important factors to consider when developing implantable antennas for biomedical purposes, including implant placement, frequency range, and power needs. This investigation additionally examines the challenges and limitations encountered with implantable antennas, including the limited space available within the human body, the requirement for biocompatible materials, the impact of surrounding tissue on antenna performance, tissue attenuation, and signal interference. This review also emphasizes the most recent advances in implanted antenna technology, such as wireless power transmission, multiband operation, and miniaturization. Furthermore, it offers illustrations of several biomedical uses for implantable antennas, including pacemaker, capsule endoscopy, intracranial pressure monitoring, retinal prostheses, and bone implants. This paper concludes with a discussion of the future of implantable antennas and their possible use in bioelectronic medicine and novel medical implants. Overall, this survey offers a thorough analysis of implantable antennas in biomedical applications, emphasizing their importance in the development of implantable medical technology. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to assess the effectiveness of capsule endoscopy in detecting gastric foreign bodies in normal dogs, considering variations in the number of foreign bodies and the gastric environment. Methods: Five healthy male beagles were administered virtual, non-harmful foreign objects that maintained their shape in the stomach. Capsule endoscopy was performed and the images were evaluated by veterinarians and nonveterinarians. Results: The overall sensitivity and specificity of capsule endoscopy were 99.1 and 90.4%, respectively. Sensitivity and specificity were comparable between veterinarians and non-veterinarians. Sensitivity and specificity in the veterinarian group were 98.7 and 91.2%, respectively, whereas those in the non-veterinarian group were 100 and 88.5%, respectively. Discussion: Capsule endoscopy is a valuable alternative diagnostic tool for identifying foreign bodies in the stomach, particularly in challenging cases in which conventional imaging or invasive approaches have limitations. [ABSTRACT FROM AUTHOR]

Introduction: Complicated perianal disease (cPD) may be the sole presentation of Crohn's disease (CD). The role of small-bowel capsule endoscopy (SBCE) in the diagnostic algorithm of cPD is unclear. We aimed to evaluate the role of SBCE as a diagnostic tool, in patients with cPD, after a negative standard workup for CD. Methods: A multicenter, retrospective, cross-sectional study, in patients with cPD, and negative standard workup for CD (ileocolonoscopy and cross-sectional imaging), who underwent SBCE for suspected CD. Demographics, biomarkers, and the Lewis Score (LS) were recorded and analyzed. An LS ≥ 135 was considered a positive SBCE for diagnosing CD. Results: Ninety-one patients were included: 65 (71.4%) males; median age: 37 (29–51) years; cPD duration: 25.1 (12.5–66.1) months. Positive SBCE: 24/91 (26.4%) patients. Fecal calprotectin (FC) positively correlated with LS (r = 0.81; p < 0.001). FC levels of 100 µg/g and 50 µg/g had a sensitivity of only 40% and 55% to rule out small-bowel CD, with a negative predictive value (NPV) of only 76% and 80%, respectively. Conclusions: SBCE contributed to CD diagnosis in a quarter of patients with cPD after a negative standard workup. FC levels correlated with the degree of inflammation defined by the LS. However, the NPV of FC was low, suggesting that SBCE should be considered for patients with cPD even after a negative standard workup. [ABSTRACT FROM AUTHOR]

Allogeneic stem cell transplantation (Allo-SCT) implies that a donor and a recipient are not genetically identical. Allo-SCT is used to cure a variety of conditions, including hematologic malignancies using the graft versus tumor effect, nonmalignant hematologic, immune deficiencies, and, more recently, genetic disorders and inborn errors of metabolism. Given the immunosuppressive and myeloablative nature of some of the conditioning chemotherapy regimens used during the Allo-SCT, patients are often at high risk of infection, including viral infections affecting the gastrointestinal tract, following the transplant. Furthermore, other complications such as hepatic sinusoidal obstruction syndrome (SOS) or graft-versus-host disease may occur post-transplant and may require endoscopy to assist in the diagnosis. This review will provide newer insights into the importance of endoscopic techniques in the diagnosis of post-Allo-SCT complications with a focus on safety and timing. [ABSTRACT FROM AUTHOR]

Background Pan-intestinal capsule endoscopy (PCE) evaluates the small bowel and colon noninvasively. This study evaluated diagnostic accuracy and safety of PCE vs. colonoscopy as first-line examination in suspected mid–lower gastrointestinal bleeding (MLGIB). Methods In this prospective, single-center, single-blinded cohort study, consecutive patients with suspected MLGIB underwent PCE followed by same-day colonoscopy. Diagnostic accuracy for potentially hemorrhagic lesions (PHLs; combined diagnosis by PCE + colonoscopy) and incidence of adverse events were assessed. Results 100 patients were included (median age 70 [range 18–92] years; 65% female). PHLs were diagnosed in 46 patients, including small-bowel and/or colon angioectasias in 32. PCE correctly identified 54 individuals without PHLs, and 95.7% (44/46) of those with PHLs vs. 50.0% (23/46) for colonoscopy (P<0.01). PHLs were detected by PCE alone in 65.2% (30/46), both examinations in 28.3% (13/46), and colonoscopy alone in 6.5% (3/46). PHLs were diagnosed at the ileocolonic region in 28% of patients, with PCE diagnosing 25/28 cases (89.3%) and colonoscopy diagnosing 23/28 (82.1%; P=0.13). Interventional procedures were performed at colonoscopy in 13/81 patients with iron-deficiency anemia (16.0%) vs. 6/19 patients with overt bleeding (31.6%; P<0.01). No significant adverse events occurred with PCE vs. 2% with colonoscopy. Conclusions In patients with MLGIB, PCE avoided further invasive procedures in >50% of patients. PCE was safe and more effective than colonoscopy in identifying PHL both in the small bowel and colon. These results support the potential use of PCE as first-line examination in patients with suspected MLGIB. [ABSTRACT FROM AUTHOR]

Background: Small bowel capsule endoscopy (SBCE) and device‐assisted enteroscopy (DAE) have an established role in the investigation and management of small bowel pathology. Previous studies have reported on the yield of SBCE (60%) and DAE (57%), but none have been in an Australian setting. Aims: To determine the yield of SBCE and any DAE performed as a direct consequence of SBCE in an Australian referral centre. Methods: A single‐centre retrospective study was conducted at a tertiary hospital in Australia, enrolling consecutive patients between 1 January 2009 and 31 December 2021 undergoing SBCE. Data were collected with respect to demographics, procedural factors and findings, as well as findings and interventions of any DAE procedures performed after the SBCE. Results: 1214 SBCEs were performed, with a median age of 66 years old (60.8% men). The predominant indications were anaemia (n = 853, 70.2%) and overt gastrointestinal bleeding (n = 320, 26.4%). Of the complete small bowel studies (1132/1214, 93.2%), abnormal findings were detected in 588 cases (51.9%), most commonly angioectasias (266/588, 45.2%), erosions (106/588, 18.0%) and ulcers (97/588, 8.6%). 165 patients underwent a DAE (117 antegrade, 48 retrograde). Antegrade DAE had a higher yield than retrograde DAE (77.8% vs 54.2%; P = 0.002) and a higher rate of intervention (69.2% vs 37.5%; P < 0.001). Conclusion: In this largest single‐centre cohort of patients undergoing SBCE to date, there is a similar yield of abnormal findings compared to existing literature. DAE, especially with an antegrade approach, had high diagnostic and therapeutic yield when pursued after a positive SBCE study. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Gastrointestinal disorders are a class of prevalent disorders in the world. Capsule endoscopy is considered an effective diagnostic modality for diagnosing such gastrointestinal disorders, especially in small intestinal regions. The aim of this work is to leverage the potential of deep convolutional neural networks for automated classification of gastrointestinal abnormalities from capsule endoscopy images. This method developed a deep learning architecture, GastroNetV1, an automated classifier, to detect abnormalities in capsule endoscopy images. The gastrointestinal abnormalities considered are ulcerative colitis, polyps, and esophagitis. The curated dataset consists of 6000 images with "ground truth" labeling. The input image is automatically classified as ulcerative colitis, a polyp, esophagitis, or a normal condition by a web-based application designed with the trained algorithm. The classifier produced 99.2% validation accuracy, 99.3% specificity, 99.3% sensitivity, and 0.991 AUC. These results exceed that of the state-of-the-art systems. Hence, the GastroNetV1 could be used to identify the different gastrointestinal abnormalities in the capsule endoscopy images, which will, in turn, improve healthcare quality. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (CE) has revolutionized gastrointestinal diagnostics, offering a non-invasive means to visualize and monitor the GI tract. This review traces the evolution of CE technology. Addressing the limitations of traditional white light (WL) CE, the paper explores non-WL technologies, integrating diverse sensing modalities and novel biomarkers to enhance diagnostic capabilities. Concluding with an assessment of Technology Readiness Levels, the paper emphasizes the transformative impact of non-WL colon CE devices on GI diagnostics, promising more precise, patient-centric, and accessible healthcare for GI disorders. [ABSTRACT FROM AUTHOR]

Video capsule endoscopy (VCE) is a non-invasive procedure to examine the human bowel. The VCE technology generates thousands of images from different parts of the gastrointestinal tract. Since the examination of these images is a tedious and time-consuming task for doctors, automated diagnosis of digestive diseases from VCE images is highly desired. The majority of the existing studies are based on CNN methods, which are not efficient enough in learning invariant global features in VCE images. Therefore, this paper presents a new framework that combines the learning of global and local features from VCE images. The proposed method utilizes a specific attention mechanism within a convolutional neural network to extract local features, while a vision transformer captures global features. Both local and global features are fused for final classification. Extensive experiments were performed on the public Kvasir Capsule Endoscopy dataset, revealing a promising accuracy of 97%. These results not only highlight the model's capabilities but also demonstrate its favorable standing when compared to the state-of-the-art methods. Additionally, achieving a recall of 85%, the proposed system demonstrated robust generalization capabilities, performing impressively on an unseen dataset. [ABSTRACT FROM AUTHOR]

Diagnosing inflammatory bowel disease (IBD) can often be challenging, and differentiating between Crohn's disease and ulcerative colitis can be particularly difficult. Diagnostic procedures for IBD include laboratory tests, endoscopy, pathological tests, and imaging tests. Serological and stool tests can be easily performed in an outpatient setting and provide critical diagnostic clues. Although endoscopy is an invasive procedure, it offers essential diagnostic information and allows for tissue biopsy and therapeutic procedures. Video capsule endoscopy and device-assisted enteroscopy are endoscopic procedures used to evaluate the small bowel. In addition to endoscopy, magnetic resonance imaging, computed tomography, and ultrasound (US) are valuable tools for small bowel assessment. Among these, US is noninvasive and easily utilized, making its use highly practical in daily clinical practice. Endoscopic biopsy aids in the diagnosis of IBD and is crucial for assessing the histological activity of the disease, facilitating a thorough evaluation of disease remission, and aiding in the development of treatment strategies. Recent advances in artificial intelligence hold promise for enhancing various aspects of IBD management, including diagnosis, monitoring, and precision medicine. This review compiles current procedures and promising future tools for the diagnosis of IBD, providing comprehensive insights. [ABSTRACT FROM AUTHOR]

We aimed to determine the diagnostic yield and outcome of patients receiving antithrombotic drug therapy subjected to small bowel capsule endoscopy (SBCE) for the investigation of small bowel bleeding (SBB). A multicenter retrospective analysis of collected data from all patients undergoing SBCE for the investigation of SBB from March 2003 to June 2023 was performed. The diagnostic yield of SBCE was defined as the detection of positive findings that could explain the cause of the patient's bleeding. Rebleeding was defined as evidence of bleeding within 1 year after the index episode. During the study period, 8401 patients underwent SBCE for SBB investigation. Bleeding lesions were detected in 1103/2535 (43.5%) antithrombotic users, compared to 1113/5866 (18.9%) in nonusers (p < 0.00001). Following capsule endoscopy, a therapeutic intervention was possible in 390/2216 (17.5%) patients with a bleeding lesion. Rebleeding occurred in 927 (36.5%) of antithrombotic users (36.5%), compared to 795 (13.5%) of nonusers (13.5%, p < 0.00001). Both the diagnostic yield of SBCE and the rebleeding rates were higher in patients with SBB receiving antithrombotics. Therapeutic intervention was possible in a real-world setting only for a minority of patients with positive findings. [ABSTRACT FROM AUTHOR]

Background/Aims: Despite advances in imaging and endoscopic technology, diagnostic modalities for small bowel tumors are simultaneously performed. We investigated the discrepancy rate between each modality and predictive factors of discrepancy in patients with definite small bowel tumors. Methods: Data of patients with definite small bowel tumors who underwent both device-assisted enteroscopy (DAE) and computed tomography (CT) were retrieved from web-based enteroscopy registry database in Korea. Predictive risk factors associated with discrepancy were analyzed using logistic regression analysis. Results: Among 998 patients, 210 (21.0%) were diagnosed with small bowel tumor using DAE, in 193 patients with definite small bowel tumor, DAE and CT were performed. Of these patients, 12 (6.2%) showed discrepancy between examinations. Among 49 patients who underwent DAE and video capsule endoscopy (VCE) examination, 13 (26.5%) showed discrepancy between examinations. No significant independent risk factors were associated with concordance between DAE and CT in multivariate logistic regression analysis among the patients. In a multivariate logistic regression analysis, red blood cell transfusion was negatively associated with concordance between DAE and VCE in patients with small bowel tumor (odds ratio, 0.163; 95% confidence interval, 0.026 to 1.004; p=0.050). Conclusions: For small bowel tumors, the discrepancy rate between DAE and CT was 6.2%, and 26.5% between DAE and VCE. Despite developments in cross-sectional imaging (VCE and DAE modalities), discrepancies still exist. For small bowel bleeding that require significant transfusion while showing insignificant VCE findings, DAE should be considered as the next diagnostic approach, considering the possibility of missed small bowel tumor. [ABSTRACT FROM AUTHOR]

Background: Monitoring Intraabdominal Pressure (IAP) is essential in critical care, as elevated IAP can lead to severe complications, including Abdominal Compartment Syndrome (ACS). Advances in technology, such as digital capsules, have opened new avenues for measuring IAP non-invasively. This study assesses the feasibility and effectiveness of using a capsular device for IAP measurement in an animal model. Method: In our controlled experiment, we anesthetized pigs and simulated elevated IAP conditions by infusing CO2 into the peritoneal cavity. We compared IAP measurements obtained from three different methods: an intravesical catheter (IAPivp), a capsular device (IAPdot), and a direct peritoneal catheter (IAPdir). The data from these methods were analyzed to evaluate agreement and accuracy. Results: The capsular sensor (IAPdot) provided continuous and accurate detection of IAP over 144 h, with a total of 53,065,487 measurement triplets recorded. The correlation coefficient (R²) between IAPdot and IAPdir was excellent at 0.9241, demonstrating high agreement. Similarly, IAPivp and IAPdir showed strong correlation with an R² of 0.9168. Conclusion: The use of capsular sensors for continuous and accurate assessment of IAP marks a significant advancement in the field of critical care monitoring. The high correlation between measurements from different locations and methods underscores the potential of capsular devices to transform clinical practices by providing reliable, non-invasive IAP monitoring. [ABSTRACT FROM AUTHOR]

An 86-year-old man presented with anemia. He underwent abdominal contrast-enhanced computed tomography, gastroscopy, and colonoscopy without any bleeding detected. Small bowel capsule endoscopy (SBCE) revealed a reddish polypoid lesion with blood oozing into the jejunum. Antegrade double-balloon endoscopy (DBE) revealed a 5 mm sized protrusion into the jejunum. Endoscopic mucosal resection (EMR) was difficult; the lesion was snared and resected before energization. Clips prevented further bleeding and the lesion's position was marked with a tattoo. Histopathological examination of the lesion led to a diagnosis of capillary hemangioma. After 11 months, the patient was again anemic. A reddish polypoid lesion oozing blood near the tattoo was found by SBCE. Another antegrade DBE showed a 7 mm sized protrusion near the tattoo. The lesion was successfully treated by EMR. Histopathological examination revealed the residual recurrence of a small intestinal capillary hemangioma. The patient recovered from anemia after the EMR. Two months later, SBCE showed no findings around the tattoo. Hemangiomas account for 7–10% of benign small intestinal tumors; most are cavernous hemangiomas, and capillary hemangiomas are rare. We report a rare case of a recurring small intestinal capillary hemangioma detected by SBCE and treated using DBE. We also review the literature. [ABSTRACT FROM AUTHOR]

This research proposes a miniature circular polarization antenna used in a wireless capsule endoscopy system at 2.45 GHz for industrial, scientific, and medical bands. We propose a method of cutting a chamfer rectangular slot on a circular radiation patch and introducing a curved radiation structure into the centerline position of the chamfer rectangular slot, while a short-circuit probe is added to achieve miniaturization. Therefore, we significantly reduced the size of the antenna and made it exhibit circularly polarized radiation characteristics. A cross-slot is cut in the GND to enable the antenna to better cover the operating band while being able to meet the complex human environment. The effective axis ratio bandwidth is 120 MHz (2.38–2.50 GHz). Its size is π × 0.032λ02 × 0.007λ0 (where λ0 is the free-space wavelength of at 2.4 GHz). In addition, the effect of different organs such as muscle, stomach, small intestine, and big intestine on the antenna when it was embedded into the wireless capsule endoscopy (WCE) system was further discussed, and the results proved that the WCE system has better robustness in different organs. The antenna's specific absorption rate can follow the IEEE Standard Safety Guidelines (IEEE C95.1-1999). A prototype is fabricated and measured. The experimental results are consistent with the simulation results. [ABSTRACT FROM AUTHOR]

Background: Colon capsule endoscopy (CCE) has gained momentum as an alternative modality for the investigation of the lower gastrointestinal tract. Of the few challenges that remain, the comparison and – eventually – matching of polyps at different timestamps leads to the potential for double reporting and can contribute to false-positive findings and inaccuracies. With the impending artificial intelligence integration, the risk of double reporting the same polyp due to the lack of information on spatial orientation underscores the necessity for establishing criteria for polyp matching. Objectives: This RAND/University of California, Los Angeles (modified Delphi) process aims to identify the key factors or components used to match polyps within a CCE video. This involves exploring the attributes of each factor to create comprehensive polyp-matching criteria based on international expert consensus. Design: A systematic qualitative study using surveys. Methods: A panel of 11 international CCE experts convened to assess a survey comprised of 60 statements. Participants anonymously rated statement appropriateness on a 1–9 scale (1–3: inappropriate, 4–6: uncertain and 7–9: appropriate). Following a virtual group discussion of the Round 1 results, a Round 2 survey was developed and completed before the final analysis. Results: The factors that were agreed to be essential for polyp matching include (1) timestamp, (2) polyp localization, (3) polyp vascular pattern, (4) polyp size, (5) time interval of the polyp appearance between the green and yellow camera, (6) surrounding tissue, (7) polyp morphology and (8) polyp surface and contour. When five or more factors are satisfied, it was agreed that the comparing polyps are likely the same polyp. Conclusion: This study has established the first complete criteria for polyp matching in CCE. While it might not provide a definitive solution for matching difficult, small and common polyps, these criteria serve as a framework to guide and facilitate the process of polyp-matching. Plain language summary: Creating criteria and standards for matching polyps (abnormal growth in the bowels) on colon capsule video analysis: an international expert agreement using the RAND (modified Delphi process) process Background: Doctors often use colon capsule endoscopy (CCE), a high-tech capsule with two cameras, to record and check for diseases in the small and large bowels as the capsule travels through the intestines. One of the most common conditions in the large bowel is polyps, which are abnormal growths in the lining of the bowel. Comparing and matching polyps in the same video from the capsule can be tricky as they look very similar, leading to the possibility of incorrectly reporting the same polyp twice or more. This can lead to wrong results and inaccuracies. The literature did not have any criteria or standards for matching polyps in CCE before. Aim: Using the RAND/UCLA (modified Delphi) process, this study aims to identify the key factors or components used to match polyps within a CCE video. The goal is to explore each factor and create complete criteria for polyp matching based on the agreement from international experts. Method: A group of 11 international CCE experts came together to evaluate a survey with 60 statements. They anonymously rated each statement on a scale from 1 to 9 (1-3: inappropriate, 4-6: uncertain, and 7-9: appropriate). After discussing the Round 1 results virtually, a Round 2 survey with the same but revised questions was created and completed before the final analysis of their agreement. Results: The main factors for matching polyps are 1) the timing when the polyp was seen, 2) where it is in the bowel, 3) its blood vessel pattern, 4) size, 5) the timing of its appearance between cameras, 6) surrounding tissue features, 7) its shape, and 8) surface features. If five or more of these factors match, the compared polyps are likely the same. Conclusion: This study establishes the first complete criteria for matching polyps in CCE. While it may not provide a definitive solution for matching challenging and small polyps, these criteria serve as a guide to help and make the process of polyp matching easier. [ABSTRACT FROM AUTHOR]

Background/Aims: The purpose of this study was to determine the factors affecting positive diagnostic yields in patients with obscure gastrointestinal bleeding (OGIB) according to the guideline of small bowel capsule endoscopy (SBCE). Method: Patients with a complaint of melena or hematochezia who were admitted were consecutively enrolled. In patients with gastrointestinal bleeding, examination was performed according to the guideline. When OGIB was suspected, SBCE was performed. Patients were categorized into two subgroups based on the SBCE results: a positive group (n = 78) and a negative group (n = 67). Results: The rate of the positive diagnostic yield of SBCE was 53.8% (78/145). In patients over 60 years, the diagnostic yield was 61.5%, which was higher than in patients younger than 60 years (40.7%). In the multivariate analysis, there was a significant difference in the positive diagnostic yield in the patients aged over 60 years (p < 0.01). Factors related to the procedure and clinical characteristics also showed significant differences in the positive predictive rates according to the degree of bowel preparation, small bowel transit time, and transfusion requirements. Conclusions: SBCE could be recommended as a diagnostic tool for OGIB, especially in those with old ages (>60 years) and those who need transfusion, because of its relatively high diagnostic yields in these populations. Proper bowel preparation and a prolonged small bowel transit time may have clinical significance in relation to the positive diagnostic yield of SBCE in patients with OGIB. [ABSTRACT FROM AUTHOR]

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Wireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature for the patients. It uses remote imaging techniques to explore various segments of the gastrointestinal (GI) tract, particularly the hard-to-reach small intestine, making it an effective alternative to traditional endoscopic techniques. However, physicians face a significant challenge when it comes to analyzing a large number of endoscopic images due to the effort and time required. It is therefore imperative to implement aided-diagnostic systems capable of automatically detecting suspicious areas for subsequent medical assessment. In this paper, we present a novel approach to identify gastrointestinal tract abnormalities from WCE images, with a particular focus on ulcerated areas. Our approach involves the use of the Median Robust Extended Local Binary Pattern (MRELBP) descriptor, which effectively overcomes the challenges faced when WCE image acquisition, such as variations in illumination and contrast, rotation, and noise. Using machine learning algorithms, we conducted experiments on the extensive Kvasir-Capsule dataset, and subsequently compared our results with recent relevant studies. Noteworthy is the fact that our approach achieved an accuracy of 97.04% with the SVM (RBF) classifier and 96.77% with the RF classifier. [ABSTRACT FROM AUTHOR]

In previous works, when studying the frictional behaviour of the small intestine, the small intestine was always in a natural state. However, when a capsule endoscope is travelling through the small intestine, since the diameter of the capsule endoscope may larger than the small intestine, the small intestine is then expanded in circumferential direction, which implies that previous works are not sufficient. This work uses flat–flat contact to simulate a capsule travelling through the small intestine, comparing the frictional behaviour of the small intestine in its natural and circumferentially expanding states, analysing the effects of other factors such as load, velocity and lubrication, which provides a realistic basis for the optimisation and control of magnetically controlled endoscopes. [ABSTRACT FROM AUTHOR]

Esophageal carcinoma is the sixth-leading cause of cancer death worldwide. A precursor to esophageal adenocarcinoma (EAC) is Barrett's Esophagus (BE). Early-stage diagnosis and treatment of esophageal neoplasia (Barrett's with high-grade dysplasia/intramucosal cancer) increase the five-year survival rate from 10% to 98%. BE is a global challenge; however, current endoscopes for early BE detection are costly and require extensive infrastructure for patient examination and sedation. We describe the design and evaluation of the first prototype of ScanCap, a high-resolution optical endoscopy system with a reusable, low-cost tethered capsule, designed to provide high-definition, blue-green illumination imaging for the early detection of BE in unsedated patients. The tethered capsule (12.8 mm diameter, 35.5 mm length) contains a color camera and rotating mirror and is designed to be swallowed; images are collected as the capsule is retracted manually via the tether. The tether provides electrical power and illumination at wavelengths of 415 nm and 565 nm and transmits data from the camera to a tablet. The ScanCap prototype capsule was used to image the oral mucosa in normal volunteers and ex vivo esophageal resections; images were compared to those obtained using an Olympus CV-180 endoscope. Images of superficial capillaries in intact oral mucosa were clearly visible in ScanCap images. Diagnostically relevant features of BE, including irregular Z-lines, distorted mucosa, and dilated vasculature, were clearly visible in ScanCap images of ex vivo esophageal specimens. [ABSTRACT FROM AUTHOR]

Background and Aims: Small bowel gastrointestinal bleeding (GIB) is associated with multiple blood transfusions, prolonged and/or multiple hospital admissions, utilization of significant healthcare resources, and negative effects on patient quality of life. There is a well-recognized association between antithrombotic medications and small bowel GIB. We aimed to identify the diagnostic yield of small bowel capsule endoscopy (SBCE) in patients on antithrombotic medications and the impact of SBCE on treatment course. Methods: The electronic medical records of nineteen hundred eighty-six patients undergoing SBCE were retrospectively reviewed. Results: The diagnostic yield for detecting stigmata of recent bleeding and/or actively bleeding lesions in SBCE was higher in patients that were on antiplatelet agents (21.6%), patients on anticoagulation (22.5%), and in patients that had their SBCE performed while they were inpatient (21.8%), when compared to the patients not on antiplatelet agents (12.1%), patients not on anticoagulation (13.5%), and with patients that had their SBCE performed in the outpatient setting (12%). Of 318 patients who had stigmata of recent bleeding and/or actively bleeding lesion(s) identified on SBCE, SBCE findings prompted endoscopic evaluation (small bowel enteroscopy, esophagogastroduodenoscopy (EGD), and/or colonoscopy) in 25.2%, with endoscopic hemostasis attempted in 52.5%. Conclusions: Our study, the largest conducted to date, emphasizes the importance of performing SBCE as part of the evaluation for suspected small bowel bleeding, particularly in patients taking antithrombotic therapy, and especially during their inpatient hospital stay. [ABSTRACT FROM AUTHOR]

Background: Capsule endoscopy (CE) is a valuable tool for assessing inflammation in patients with Crohn's disease (CD). The current standard for evaluating inflammation are validated scores (and clinical laboratory values) like Lewis score (LS), Capsule Endoscopy Crohn's Disease Activity Index (CECDAI), and ELIAKIM. Recent advances in artificial intelligence (AI) have made it possible to automatically select the most relevant frames in CE. Objectives: In this proof-of-concept study, our objective was to develop an automated scoring system using CE images to objectively grade inflammation. Design: Pan-enteric CE videos (PillCam Crohn's) performed in CD patients between 09/2020 and 01/2023 were retrospectively reviewed and LS, CECDAI, and ELIAKIM scores were calculated. Methods: We developed a convolutional neural network-based automated score consisting of the percentage of positive frames selected by the algorithm (for small bowel and colon separately). We correlated clinical data and the validated scores with the artificial intelligence-generated score (AIS). Results: A total of 61 patients were included. The median LS was 225 (0–6006), CECDAI was 6 (0–33), ELIAKIM was 4 (0–38), and SB_AIS was 0.5659 (0–29.45). We found a strong correlation between SB_AIS and LS, CECDAI, and ELIAKIM scores (Spearman's r = 0.751, r = 0.707, r = 0.655, p = 0.001). We found a strong correlation between LS and ELIAKIM (r = 0.768, p = 0.001) and a very strong correlation between CECDAI and LS (r = 0.854, p = 0.001) and CECDAI and ELIAKIM scores (r = 0.827, p = 0.001). Conclusion: Our study showed that the AI-generated score had a strong correlation with validated scores indicating that it could serve as an objective and efficient method for evaluating inflammation in CD patients. As a preliminary study, our findings provide a promising basis for future refining of a CE score that may accurately correlate with prognostic factors and aid in the management and treatment of CD patients. Plain language summary: Artificial intelligence in Crohn's disease: the development of an automated score for disease activity evaluation This study introduces an innovative AI-based approach to evaluate Crohn's Disease. The AI system automatically analyzes images from capsule endoscopy, focusing on finding ulcers and erosions to measure disease activity. The research reveals a robust correlation between the AI-generated score assessing inflammation in the small bowel and traditional clinical scores. This suggests that the AI solution could be a quicker and more consistent way to evaluate Crohn's Disease, speeding up the evaluation process and reducing manual scoring variability. While promising, the study acknowledges limitations and emphasizes the need for further validation with larger groups of patients. Overall, it represents a crucial step toward integrating AI into gastroenterology, offering a glimpse into a future of more objective and personalized Crohn's Disease evaluation. [ABSTRACT FROM AUTHOR]

Background: Splenic artery pseudoaneurysm is a rare complication of recurrent pancreatitis usually presenting as an incidental finding on abdominal computed tomography. Case presentation: We present the case of a 66-year-old north African male with a known history of previous pancreatitis who presented with upper gastrointestinal bleeding along with recurrent epigastric pain for 3 days. Investigations did not reveal any particular pancreatitis etiology. Computed tomography of the abdomen with contrast showed splenic artery pseudoaneurysm along with findings suggestive of acute pancreatitis. Upper and lower endoscopies failed to identify gastrointestinal the bleed source. The patient underwent intervention radiology embolization of the aneurysm sac with multiple coils via right retrograde common femoral artery–celiac access. The patient was discharged with a plan for capsule endoscopy in outpatient setting. Conclusion: Splenic artery pseudoaneurysm is a life-threatening complication and carries a high mortality rate if left untreated. Prompt identification through various imaging modalities, followed by urgent intervention, is crucial to avoid adverse outcomes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice. [ABSTRACT FROM AUTHOR]

A reliable, quick‐to‐assess, and automatic scoring system for cleanliness assessment in video capsule endoscopy (VCE) is presently not available. The present study proposes an approach to automatically assess the cleanliness in VCE frames as per the latest scoring system, that is, Korea‐Canada (KODA). First, a new multi‐label frame dataset containing medical scores of 28 VCE videos was generated through the proposed mobile‐based application called Artificial Intelligence‐KODA (AI‐KODA) score. The scores were saved automatically in real‐time through the application. The generated dataset was transformed into three datasets based on the scores, and each of the dataset was then randomly split into train:validate:test ratio of 60:20:20. Second, a comprehensive evaluation, interpretation, and benchmarking of the three classification tasks were performed with the help of eight transfer learning algorithms on NVIDIA RTX A5000 workstation. Thorough analysis indicates that AI‐KODA utilized with AI is reliable, quick‐to‐access, and free from observer bias. It promotes automatic scoring system for cleanliness assessment in VCE. The meta‐data is available here (link). [ABSTRACT FROM AUTHOR]

The classification of medical images has had a significant influence on the diagnostic techniques and therapeutic interventions. Conventional disease diagnosis procedures require a substantial amount of time and effort to accurately diagnose. Based on global statistics, gastrointestinal cancer has been recognized as a major contributor to cancer‐related deaths. The complexities involved in resolving gastrointestinal tract (GIT) ailments arise from the need for elaborate methods to precisely identify the exact location of the problem. Therefore, doctors frequently use wireless capsule endoscopy to diagnose and treat GIT problems. This research aims to develop a robust framework using deep learning techniques to effectively classify GIT diseases for therapeutic purposes. A CNN based framework, in conjunction with the feature selection method, has been proposed to improve the classification rate. The proposed framework has been evaluated using various performance measures, including accuracy, recall, precision, F1 measure, mean absolute error, and mean squared error. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) captures huge number of images, but only a fraction are medically relevant. We propose automated real‐time small bowel visualization quality (SBVQ) assessment to eliminate transmission of irrelevant frames. Our aim is to design lightweight color‐based models for segmenting clean and contaminated regions with minimal parameters, short training, and fast inference, suitable for WCE hardware integration. Using the Kvasir Capsule endoscopy dataset, we constructed models based on distinctive color patterns of clean and contaminated regions. While different classifiers have been trained and evaluated, the k‐nearest neighbors (KNNs), multilayer perceptron (MLP), and gradient‐boosted machine (GBM) obtained superior performance (accuracy: 0.87±0.12, Dice similarity score (DSC): 0.87±0.15, intersection over union (IOU): 0.80±0.19). Logistic regression (LR) had the shortest training and inference times. Our models offer simplicity, compactness, and robustness, delivering satisfactory real‐time performance. Evaluation on the SEE‐AI project dataset confirms good generalization capabilities, demonstrating practical solutions for WCE image analysis. [ABSTRACT FROM AUTHOR]

In the past studies, the resistance of magnetically controlled capsules running through the small intestine has been modeled assuming that the small intestine was a circular tube with a constant diameter. Peristalsis is an important character of the human gastrointestinal system, and it would result in some changes in the diameter of the intestine, meaning that the existing resistance models would no longer be applicable. In this paper, based on the assumption that intestinal peristalsis is actually a sinusoidal wave, a resistance model of the capsule running in the peristaltic intestine is established, and then it is validated experimentally. The model provides a realistic foundation for the optimization and control of the magnetically controlled endoscopy. [ABSTRACT FROM AUTHOR]

A retrospective study in patients who underwent video capsule endoscopy (VCE) between 2006 and 2016 was conducted in the Clinic for gastroenterology and Hepatology, University Clinical Center of Serbia. A total of 245 patients underwent VCE. In 198 patients the indication was obscure gastrointestinal bleeding (OGIB), with 92 patients having overt and the other 106 occult bleeding. The remaining 47 patients underwent VCE due to suspected small bowel (SB) disease (i.e., Von Hippel–Lindau syndrome, familial adenomatous polyposis, Peutz Jeghers syndrome, Crohn's disease, prolonged diarrhea, abdominal pain, congenital lymphangiectasia, protein-losing enteropathy, tumors, refractory celiac disease, etc.). VCE identified a source of bleeding in 38.9% of patients (in the obscure overt group in 48.9% of patients, and in the obscure occult group in 30.2% of patients). The most common findings were angiodysplasias, tumors, Meckel's diverticulum and Crohn's disease. In the smaller group of patients with an indication other than OGIB, 38.3% of patients had positive VCE findings. The most common indication is OGIB, and the best candidates are patients with overt bleeding; patients with IBD should be evaluated in this setting. [ABSTRACT FROM AUTHOR]

Simple Summary: Recent technological advances, including capsule endoscopy (CE) and balloon-assisted endoscopy (BAE), have revealed that small intestinal disease is more common than previously thought. Early diagnosis of small intestinal tumors is essential for favorable outcomes. For early diagnosis, after examination of the upper and lower gastrointestinal tract, the possibility of small bowel lesions should be considered in patients with unexplained symptoms and signs, including gastrointestinal bleeding, chronic anemia, abdominal pain, obstructive symptoms, body weight loss, palpable abdominal mass, and fever of unknown origin. Recent technological advances, including capsule endoscopy (CE) and balloon-assisted endoscopy (BAE), have revealed that small intestinal disease is more common than previously thought. CE has advantages, including a high diagnostic yield, discomfort-free, outpatient basis, and physiological images. BAE enabled endoscopic diagnosis and treatment in the deep small bowel. Computed tomography (CT) enterography with negative oral contrast can evaluate masses, wall thickening, and narrowing of the small intestine. In addition, enhanced CT can detect abnormalities outside the gastrointestinal tract that endoscopy cannot evaluate. Each modality has its advantages and disadvantages, and a good combination of multiple modalities leads to an accurate diagnosis. As a first-line modality, three-phase enhanced CT is preferred. If CT shows a mass, stenosis, or wall thickening, a BAE should be selected. If there are no abnormal findings on CT and no obstructive symptoms, CE should be selected. If there are significant findings in the CE, determine the indication for BAE and its insertion route based on these findings. Early diagnosis of small intestinal tumors is essential for favorable outcomes. For early diagnosis, the possibility of small bowel lesions should be considered in patients with unexplained symptoms and signs after examination of the upper and lower gastrointestinal tract. [ABSTRACT FROM AUTHOR]

Background: The clinicopathological features and prognosis of primary small bowel adenocarcinoma (PSBA), excluding duodenal cancer, remain undetermined due to its rarity in Japan. Methods: We analyzed 354 patients with 358 PSBAs, between January 2008 and December 2017, at 44 institutions affiliated with the Japanese Society for Cancer of the Colon and Rectum. Results: The median age was 67 years (218 males, 61.6%). The average tumor size was 49.9 (7–100) mm. PSBA sites consisted of jejunum (66.2%) and ileum (30.4%). A total of 219 patients (61.9%) underwent diagnostic small bowel endoscopy, including single-balloon endoscopy, double-balloon endoscopy, and capsule endoscopy before treatment. Nineteen patients (5.4%) had Lynch syndrome, and 272 patients (76.8%) had symptoms at the initial diagnosis. The rates for stages 0, I, II, III, and IV were 5.4%, 2.5%, 27.1%, 26.0%, and 35.6%, respectively. The 5-year overall survival rates at each stage were 92.3%, 60.0%, 75.9%, 61.4%, and 25.5%, respectively, and the 5-year disease-specific survival (DSS) rates were 100%, 75.0%, 84.1%, 59.3%, and 25.6%, respectively. Patients with the PSBA located in the jejunum, with symptoms at the initial diagnosis or advanced clinical stage had a worse prognosis. However, multivariate analysis using Cox-hazard model revealed that clinical stage was the only significant predictor of DSS for patients with PSBA. Conclusions: Of the patients with PSBA, 76.8% had symptoms at the initial diagnosis, which were often detected at an advanced stage. Detection during the early stages of PSBA is important to ensure a good prognosis. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) is a technology that uses a pill-sized camera to visualize images of the digestive tract. It presents several advantages, since it is far less invasive, does not require sedation and has less potential complications compared to standard endoscopy. Hence, it might be exploited as alternative to the standard procedure. WCE is used to diagnosis a variety of gastro-intestinal diseases such as polyps, ulcers, Crohn's disease and hemorrhages. Nevertheless, WCE videos can contain thousands of images per patient that must be screened by medical specialists, besides, the capsule free mobility and technological limits cause production of a low quality images. In this paper, a Nouvel method based on Dense-UNet deep learning segmentation model is presented. This approach aims at red lesion, ulcer and polyp detection from WCE images. Then, we propose a modified residual attention network for images classification. The proposed methods training and validation accuracies are 97.57% and 92.70%, with training and validation intersection over union and dice coefficients of 75.31%, 71.29%, 83.50% and 80.66%, respectively, in the red lesion dataset. In the polyp dataset, the method achieved a training and validation accuracies of 98.26% and 92.33%, with training and validation intersection over union and dice coefficients of 92.09%, 95.62%, 80.13% and 87.14%, respectively. Besides, the proposed architecture reached an average accuracy of 99.30%, sensitivity, specificity and F1 score of 98.61%, 100% and 99.30%, respectively. These results demonstrate that the proposed approach is satisfactory in polyp and red lesion segmentation and ulcer detection. [ABSTRACT FROM AUTHOR]

Objectives: Although several studies have shown the usefulness of artificial intelligence to identify abnormalities in small‐bowel capsule endoscopy (SBCE) images, few studies have proven its actual clinical usefulness. Thus, the aim of this study was to examine whether meaningful findings could be obtained when negative SBCE videos were reanalyzed with a deep convolutional neural network (CNN) model. Methods: Clinical data of patients who received SBCE for suspected small‐bowel bleeding at two academic hospitals between February 2018 and July 2020 were retrospectively collected. All SBCE videos read as negative were reanalyzed with the CNN algorithm developed in our previous study. Meaningful findings such as angioectasias and ulcers were finally decided after reviewing CNN‐selected images by two gastroenterologists. Results: Among 202 SBCE videos, 103 (51.0%) were read as negative by humans. Meaningful findings were detected in 63 (61.2%) of these 103 videos after reanalyzing them with the CNN model. There were 79 red spots or angioectasias in 40 videos and 66 erosions or ulcers in 35 videos. After reanalysis, the diagnosis was changed for 10 (10.3%) patients who had initially negative SBCE results. During a mean follow‐up of 16.5 months, rebleeding occurred in 19 (18.4%) patients. The rebleeding rate was 23.6% (13/55) for patients with meaningful findings and 16.1% (5/31) for patients without meaningful findings (P = 0.411). Conclusion: Our CNN algorithm detected meaningful findings in negative SBCE videos that were missed by humans. The use of deep CNN for SBCE image reading is expected to compensate for human error. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) enables imaging and diagnostics of the gastrointestinal (GI) tract to be performed without any discomfort. Despite this, several characteristics, including efficacy, tolerance, safety, and performance, make it difficult to apply and modify widely. The use of automated WCE to collect data and perform the analysis is essential for finding anomalies. Medical specialists need a significant amount of time and expertise to examine the data generated by WCE imaging of the patient's digestive tract. To address these challenges, several computer vision-based solutions have been designed; nevertheless, they do not achieve an acceptable level of accuracy, and more advancements are required. Thus, in this study, we proposed four multi-classification deep learning (DL) models i.e., Vgg-19 + CNN, ResNet152V2, Gated Recurrent Unit (GRU) + ResNet152V2, and ResNet152V2 + Bidirectional GRU (Bi-GRU) and applied it on different publicly available databases for diagnosing ulcerative colitis, polyps, and dyed-lifted polyps using WCE images. To our knowledge, this is the only study that uses a single DL model for the classification of three different GI diseases. We compared the classification performance of the proposed DL classifiers in terms of many parameters such as accuracy, loss, Matthew's correlation coefficient (MCC), recall, precision, negative predictive value (NPV), positive predictive value (PPV), and F1-score. The results revealed that the Vgg-19 + CNN outperforms the three other proposed DL models in classifying GI diseases using WCE images. The Vgg-19 + CNN model achieved an accuracy of 99.45%. The results of four proposed DL classifiers are also compared with recent state-of-the-art classifiers and the proposed Vgg-19 + CNN model has performed better in terms of improved accuracy. [ABSTRACT FROM AUTHOR]

While the adoption of wireless capsule endoscopy (WCE) has been steadily increasing, its primary application remains limited to observing the small intestine, with relatively less application in the upper gastrointestinal tract. However, there is a growing anticipation that advancements in capsule endoscopy technology will lead to a significant increase in its application in upper gastrointestinal examinations. This study addresses the underexplored domain of landmark identification within the upper gastrointestinal tract using WCE, acknowledging the limited research and public datasets available in this emerging field. To contribute to the future development of WCE for gastroscopy, a novel approach is proposed. Utilizing color transfer techniques, a simulated WCE dataset tailored for the upper gastrointestinal tract is created. Using Euclidean distance measurements, the similarity between this color-transferred dataset and authentic WCE images is verified. Pioneering the exploration of anatomical landmark classification with WCE data, this study integrates similarity evaluation with image preprocessing and deep learning techniques, specifically employing the DenseNet169 model. As a result, utilizing the color-transferred dataset achieves an anatomical landmark classification accuracy exceeding 90% in the upper gastrointestinal tract. Furthermore, the application of sharpen and detail filters demonstrates an increase in classification accuracy from 91.32% to 94.06%. [ABSTRACT FROM AUTHOR]

Introduction: Deep learning models can assess the quality of images and discriminate among abnormalities in small bowel capsule endoscopy (CE), reducing fatigue and the time needed for diagnosis. They serve as a decision support system, partially automating the diagnosis process by providing probability predictions for abnormalities. Methods: We demonstrated the use of deep learning models in CE image analysis, specifically by piloting a bowel preparation model (BPM) and an abnormality detection model (ADM) to determine frame‑level view quality and the presence of abnormal findings, respectively. We used convolutional neural network‑based models pretrained on large‑scale open‑domain data to extract spatial features of CE images that were then used in a dense feed‑forward neural network classifier. We then combined the open‑source Kvasir‑Capsule dataset (n = 43) and locally collected CE data (n = 29). Results: Model performance was compared using averaged five‑fold and two‑fold cross‑validation for BPMs and ADMs, respectively. The best BPM model based on a pre-trained ResNet50 architecture had an area under the receiver operating characteristic and precision-recall curves of 0.969±0.008 and 0.843±0.041, respectively. The best ADM model, also based on ResNet50, had top-1 and top-2 accuracies of 84.03±0.051 and 94.78±0.028, respectively. The models could process approximately 200–250 images per second and showed good discrimination on time‑critical abnormalities such as bleeding. Conclusion: Our pilot models showed the potential to improve time to diagnosis in CE workflows. To our knowledge, our approach is unique to the Singapore context. The value of our work can be further evaluated in a pragmatic manner that is sensitive to existing clinician workflow and resource constraints. [ABSTRACT FROM AUTHOR]

Background: During esophagogastroduodenoscopy performed with colonoscopy, gastric and duodenal erythema, erosions, and ulcerations are often observed. This investigation was designed to review the prevalence of gastroduodenal lesions in patients who have undergone wireless capsule endoscopy using standard bowel cleansing preparations, but no endoscopy or sedation. Methods: A retrospective analysis was conducted on patients referred for capsule endoscopy. Records and capsule reports were reviewed for the patient demographics, preparation prescribed, procedural indications, and gastroduodenal findings. The preparations studied included polyethylene glycol lavage (PEG), PEG plus bisacodyl (PEG + bis), bisacodyl (bis), oral sulfate solution (OSS), and no prep. Results: Among the 1236 records, 498 (40.3%) were men and 738 (59.7%) were women. The mean age was 56 years +/− 18 years SD. The percentage of patients with lesions after any bowel preparation was 52.7% for gastric lesions and 23.6% for duodenal lesions. The percentage of patients with gastroduodenal lesions was 58.3% with prep, compared to 38.2% without prep. These findings were statistically significant, with an RR of 1.53 [1.19–1.94] (p-value = 0.00004). This difference was more pronounced in the OSS group RR of 1.65 [1.29–2.1] and bisacodyl group RR of 1.64 [1.25–2.15] compared to the PEG group RR of 0.95 [0.7–1.3]. Conclusions: This study showed that patients undergoing wireless capsule endoscopy who received bowel preparations had a significant increase in gastric and duodenal lesions. Of the preparations studied, OSS was associated with a greater number of gastroduodenal lesions, while PEG was the least associated with lesions, with an occurrence similar to the non-prep group. The clinical significance of these lesions remains undetermined. Endoscopists should be aware that preparations are associated with gastroduodenal lesions to avoid the misinterpretation and misdiagnosis of these lesions. [ABSTRACT FROM AUTHOR]

The colorectal cancer (CRC) is gaining attention in the context of gastrointestinal tract diseases as it ranks third among the most prevalent type of cancer. The early diagnosis of the CRC can be done by periodic examination of the colon and rectum for innocuous tissue abnormality called polyp as it has the potential to evolve as malignant in future. The CRC diagnosis using wireless capsule endoscopy requires the dedicated commitment of the medical expert demanding significant time, focus and effort. The accuracy of manual analysis in identifying polyps is extensively reliant on the cognitive condition of the physician, thus emphasizing the requirement for automatic polyp identification. The artificial intelligence integrated computer‐aided diagnosis system could assist the clinician in better diagnosis, thereby reducing the miss‐rates of polyps. In our proposed study, we developed an application program interface to aid the clinician in automatic segmentation of the polyp and evaluation of its dimension by manual placement of four landmarks on the predicted polyp. The segmentation is performed by the proposed light weight Padded U‐Net for the effective polyp segmentation in the colorectal images. We trained and validated the Padded U‐Net with augmented images of Kvasir dataset and calculated the performance parameters. In order to facilitate image augmentation, a graphical user interface called Augment Tree was developed, which incorporates 92 augmentation techniques. The accuracy, recall, precision, IoU, F1‐score, loss achieved during validation of Padded U‐Net were 95.6%, 0.946%, 0.985%, 0.933%, 0.965% and 0.080% respectively. We demonstrated that accuracy was improved and loss was reduced when the model was trained with augmented images rather than only the limited original dataset images. On comparison of our Padded U‐net architecture with recently developed architectures, our model attained optimal performance in all the metrics except accuracy in which it attained marginal performance to the highest value. [ABSTRACT FROM AUTHOR]

Gastrointestinal (GI) tract disorders are a significant public health issue. They are becoming more common and can cause serious health problems and high healthcare costs. Small bowel tumours (SBTs) and colorectal cancer (CRC) are both becoming more prevalent, especially among younger adults. Early detection and removal of polyps (precursors of malignancy) is essential for prevention. Wireless Capsule Endoscopy (WCE) is a procedure that utilises swallowable camera devices that capture images of the GI tract. Because WCE generates a large number of images, automated polyp segmentation is crucial. This paper reviews computer-aided approaches to polyp detection using WCE imagery and evaluates them using a dataset of labelled anomalies and findings. The study focuses on YOLO-V8, an improved deep learning model, for polyp segmentation and finds that it performs better than existing methods, achieving high precision and recall. The present study underscores the potential of automated detection systems in improving GI polyp identification. [ABSTRACT FROM AUTHOR]

This document is a conference report summarizing the discussions and expert opinions from the REFLECT symposium on minimally invasive gastrointestinal (GI) diagnostics. The report focuses on capsule endoscopy as a primary modality for GI investigation, particularly in the small bowel. It discusses the current practice and field reports of colon capsule endoscopy (CCE), highlighting its potential as an alternative to conventional colonoscopy. The report also explores technological advancements in GI capsules, including non-white light capsule endoscopes and the integration of artificial intelligence. Additionally, it discusses the combination of capsule endoscopy with traditional endoscopy and the clinical aspects of early and late-stage solutions. The document emphasizes the need for collaboration between engineers, health professionals, and the industry to drive advanced capsule capabilities and improve diagnostic accuracy. [Extracted from the article]

Introduction: In the late 1960s, probable cases of eosinophilic esophagitis (EoE) emerged, initially linked to esophageal rings and congenital causes or gastroesophageal reflux disease (GERD). Doubts about the GERD association arose due to poor responses to antisecretory therapy. EoE is now a significant pediatric health concern, impacting 1 to 7 cases per 10,000 children. Purpose of the Study: This study aims to comprehensively explore EoE's epidemiology, etiology, subtypes, clinical manifestations, diagnostic methods, and treatment modalities. Materials and Methods: The study employs a cross-sectional analysis of pediatric EoE patients, utilizing endoscopic evaluation and histological assessment. Three distinct EoE subtypes are identified based on pathogenic characteristics. Clinical manifestations, diagnostic methods, and treatments, including endoscopy, are examined. Results: Clinical manifestations span a broad spectrum in the pediatric population. Diagnostic methods include endoscopy, esophageal manometry, impedance-pH monitoring, capsule endoscopy, and string tests. Three distinct EoE subtypes are identified with unique characteristics. Treatment modalities involve dietary management, proton pump inhibitors, topical corticosteroids, biologic therapies, allergen immunotherapy, and endoscopic interventions. Conclusion: Formerly associated with GERD, EoE is now a significant pediatric health concern. The study underscores the importance of comprehensive diagnostic approaches and diverse treatment modalities, including promising biologic therapies and allergen immunotherapy. Understanding these aspects is crucial for effective management and improved outcomes in patients. [ABSTRACT FROM AUTHOR]

Gastrointestinal diseases cause around two million deaths globally. Wireless capsule endoscopy is a recent advancement in medical imaging, but manual diagnosis is challenging due to the large number of images generated. This has led to research into computer-assisted methodologies for diagnosing these images. Endoscopy produces thousands of frames for each patient, making manual examination difficult, laborious, and error-prone. An automated approach is essential to speed up the diagnosis process, reduce costs, and potentially save lives. This study proposes transfer learning-based efficient deep learning methods for detecting gastrointestinal disorders from multiple modalities, aiming to detect gastrointestinal diseases with superior accuracy and reduce the efforts and costs of medical experts. The Kvasir eight-class dataset was used for the experiment, where endoscopic images were preprocessed and enriched with augmentation techniques. An EfficientNet model was optimized via transfer learning and fine tuning, and the model was compared to the most widely used pre-trained deep learning models. The model's efficacy was tested on another independent endoscopic dataset to prove its robustness and reliability. [ABSTRACT FROM AUTHOR]

Background: Neuroendocrine tumors (NETs) are rare epithelial neoplasms that arise most commonly from the gastrointestinal tract. In pediatrics, the most common site of origin is in the appendix, with the liver being the most common site of metastasis. Neuroendocrine tumors arising from the biliary tract are extremely rare. Methods: We describe a case of a nine‐year‐old girl who presented with obstructive cholestasis and was found to have multiple liver masses identified on biopsy as well‐differentiated neuroendocrine tumor with an unknown primary tumor site. Result: The patient underwent extensive investigation to identify a primary tumor site, including endoscopy, endoscopic ultrasound, and capsule endoscopy. The patient ultimately underwent definitive management with liver transplant, and on explant was discovered to have multiple well‐differentiated neuroendocrine tumors, WHO Grade 1, with extensive infiltration into the submucosa of bile duct, consistent with primary biliary tract neuroendocrine tumor. Conclusion: Identifying the site of the primary tumor in NETs found within the liver can be challenging. To determine if an extrahepatic primary tumor exists, workup should include endoscopy, EUS, and capsule endoscopy. Children with well‐differentiated hepatic NETs, with no identifiable primary tumor, and an unresectable tumor, are considered favorable candidates for liver transplantation. [ABSTRACT FROM AUTHOR]

Capsule endoscopy is a valuable tool for diagnosis of the gastrointestinal tract. As a result, active‐control capsule endoscopy, which can be controlled via external stimulation, has emerged as a prominent diagnosis tool, unlike traditional passive capsule endoscopy. However, owing to its rigid cover, the high rotating radii limit its observation range at anchor points, such as polyps. There are also persistent challenges with pass‐through in narrow spaces in the intestines. This study proposes a soft‐shell capsule endoscope (SSCE) to improve this limitation. Experiments ranging from virtual phantoms to ex vivo investigations in the stomach, small intestine, and large intestine were conducted to ensure comprehensive coverage of the entire digestive system by the SSCE. Additionally, in the stomach and intestine experiments, we compared the performances of the SSCE and hard‐shell capsule endoscope (HSCE) to highlight the advantages of the SSCE. The results demonstrate that the SSCE achieves a 37.3% increase in the observable angles through bending motions at the anchor point compared to the HSCE, in addition to increased efficiency of pass‐through in the intestines. This revolutionary innovation is expected to significantly impact and promote high efficiency via pass‐through and the observation area in the clinical application of capsule endoscopy. [ABSTRACT FROM AUTHOR]

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