Search Results (62)

Background/Objectives: The prevalence of inflammatory bowel disease (IBD) in spondyloarthritis (SpA) patients is unknown. Our objective was to assess the prevalence of undiagnosed IBD in SpA patients, including those with axial spondylarthritis (axSpA) or psoriatic arthritis (PsA). Additionally, we examined fecal calprotectin (FC) levels in relation to the accuracy of IBD diagnosis. Methods: EISER was a cross-sectional, multicenter, observational, rheumatologist–gastroenterologist collaborative study. Patients with SpA naïve to biologics were recruited. Demographic and clinical characteristics, disease activity, and treatment information were collected. Patients with FC ≥ 80 µg/g or IBD-related symptoms underwent a colonoscopy or video capsule endoscopy. Receiver operating characteristic analysis assessed the predictive value of FC for IBD diagnosis. Results: Of the 570 patients recruited, 494 were evaluable for the main outcome, 248 (50.2%) had axSpA, and 246 (49.8%) had PsA. Overall, 28/494 patients were diagnosed with IBD (5.7%, 95%CI 3.6–7.7). Sorted by clinical entity, 22 (8.9%, 95%CI 5.3–12.4) axSpA and 6 (2.4%, 95%CI 0.5–4.4) PsA patients had a diagnosis of IBD: 24 (86%, 95%CI 79.4–92.6) had ileal/ileocolonic Crohn’s disease (CD), 3 (11%, 95%CI 5.1–16.9) unclassified IBD, and 1 (3.5%, 95%CI 0.0–6.9) ulcerative colitis. The ROC curve for FC and IBD diagnosis (AUC: 0.870, p < 0.001, 95%CI 83.7–89.8) showed that an FC ≥ 147 µg/g had a positive predictive value of 17.4% (95%CI 14.5–20.8) Conclusions: In SpA, the prevalence of undiagnosed IBD was 5.7%, higher in axSpA (8.9%) than in PsA (2.4%) patients, with CD being the most common. SpA patients with FC levels < 147 µg/g had a very low probability of IBD. Full article

Artificial intelligence (AI) is rapidly transforming imaging modalities in inflammatory bowel disease (IBD), particularly in endoscopy and ultrasound. Despite their critical roles, both modalities are challenged by interobserver variability, subjectivity, and accessibility issues. AI offers significant potential to address these limitations by enhancing lesion detection, standardizing disease activity scoring, and supporting clinical decision-making. In endoscopy, deep convolutional neural networks have achieved high accuracy in detecting mucosal abnormalities and grading disease severity, reducing observer dependency and improving diagnostic consistency. AI-assisted colonoscopy systems have also demonstrated improvements in procedural quality metrics, including adenoma detection rates and withdrawal times. Similarly, AI applications in intestinal ultrasound show promise in automating measurements of bowel wall thickness, assessing vascularity, and distinguishing between inflammatory and fibrotic strictures, which are critical for tailored therapy decisions. Video capsule endoscopy has likewise benefited from AI, reducing interpretation times and enhancing the detection of subtle lesions. Despite these advancements, implementation challenges, including dataset quality, standardization, AI interpretability, clinician acceptance, and regulatory and ethical considerations, must be carefully addressed. The current review focuses on the most recent developments in the integration of AI into experimental designs, medical devices, and clinical workflows for optimizing diagnostic accuracy, treatment strategies, and patient outcomes in IBD management. Full article

Inflammatory bowel disease (IBD) refers to a chronic inflammatory condition involving the GI tract that includes Crohn’s disease (CD) and ulcerative colitis (UC). These conditions are believed to arise in genetically predisposed individuals who develop an exaggerated immune response to the intestinal microbiota. A timely and accurate diagnosis of IBD is essential because diagnostic delays can result in intestinal damage that is irreversible, leading in some cases to intestinal dysfunction and the need for surgery. Diagnostic delays are common in cases when GI symptoms are mild and nonspecific. When IBD is suspected, the common diagnostic algorithm includes laboratory analyses, cross-sectional radiologic imaging, and endoscopy with biopsy and histological analysis. Other diagnostic biomarkers, including those found in the serum, stool, and urine, have also been evaluated in IBD. Newer artificial intelligence (AI)-based technologies are now being developed, and these will likely play an important future role in the diagnosis and management of IBD. Full article

Small bowel capsule endoscopy (SBCE) has emerged in the past two decades as the cornerstone for assessing small bowel disorders, and its use is supported by several guidelines. However, there are several limitations, such as the considerable time required for gastroenterologists to review these videos and reach a diagnosis. To address these limitations, researchers have explored the integration of artificial intelligence in the interpretation of these videos. In our review, we explore the evolving and emerging role of artificial intelligence in SBCE and examine the latest advancements and ongoing studies in these areas, aiming at overcoming current limitations. Full article

Background: Heyde syndrome is a rare condition characterized by the triad of severe aortic stenosis, gastrointestinal bleeding, and acquired type 2A von Willebrand syndrome. This case report highlights the diagnostic and therapeutic approach for a 72-year-old woman presenting with exertional chest pain, dyspnea, fatigue, and a history of melena. Methods: The diagnostic workup revealed severe microcytic anemia and a reduced vWF ristocetin-to-antigen ratio. Imaging confirmed severe degenerative aortic stenosis, while video capsule endoscopy identified angiodysplasia and telangiectasias in the small bowel as the source of gastrointestinal bleeding. Following evaluation by a multidisciplinary Heart Team, the patient underwent transcatheter aortic valve replacement (TAVR) with an Evolut Fx self-expanding prosthesis. Results: Post-procedural echocardiography showed mild paravalvular regurgitation. The patient’s clinical course was favorable, with resolution of anemia and no further gastrointestinal bleeding episodes. Conclusions: Heyde syndrome requires a high index of suspicion for diagnosis in patients with severe aortic stenosis and unexplained anemia or gastrointestinal bleeding. TAVR offers an effective treatment option that not only resolves valvular pathology, but also mitigates associated bleeding risks. Full article

This study aimed to evaluate the efficiency and sensitivity of real-time video capsule endoscopy (RT-VCE) when detecting surgical and nonsurgical gastric lesions in dogs that presented with acute vomiting. Additionally, the patient tolerance and the utility of the RT-VCE data for clinical decision-making were assessed. Eleven client-owned dogs with an acute onset of vomiting were included. Each dog received an antiemetic and antacid before undergoing an RT-VCE (MC1200, MiroCam^®, Intromedic, Seoul, Republic of Korea). We independently reviewed the RT-VCE images to assess the image quality, detect gastric lesions, and make clinical decisions. The time taken to reach a diagnosis and the inter-clinician agreement were evaluated. RT-VCEs were successfully completed in all the dogs, with a mean time to diagnosis of 21.82 ± 15.26 min (range: 1–48 min). Both clinicians reached unanimous agreement on the RT-VCE findings. Five dogs were diagnosed with gastric foreign bodies and underwent surgical removal, while the remaining six were diagnosed with gastritis, a gastric ulcer or erosion, or hemorrhages, which were managed medically. No adverse effects, including vomiting, were observed after the capsule administration. RT-VCE proved to be an effective, accurate, and well-tolerated diagnostic method for evaluating gastric conditions in dogs with acute vomiting. Its rapid diagnostic capability and non-invasive nature make it a valuable modality in emergency veterinary settings. Full article

Inflammatory bowel diseases (IBDs), comprising Crohn’s disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases of the gastrointestinal (GI) tract with still-elusive etiopathogeneses and an increasing prevalence worldwide. Despite the growing availability of more advanced therapies in the last two decades, there are still a number of unmet needs. For example, the achievement of mucosal healing has been widely demonstrated as a prognostic marker for better outcomes and a reduced risk of dysplasia and cancer; however, the accuracy of endoscopy is crucial for both this aim and the precise and reproducible evaluation of endoscopic activity and the detection of dysplasia. Artificial intelligence (AI) has drastically altered the field of GI studies and is being extensively applied to medical imaging. The utilization of deep learning and pattern recognition can help the operator optimize image classification and lesion segmentation, detect early mucosal abnormalities, and eventually reveal and uncover novel biomarkers with biologic and prognostic value. The role of AI in endoscopy—and potentially also in histology and imaging in the context of IBD—is still at its initial stages but shows promising characteristics that could lead to a better understanding of the complexity and heterogeneity of IBDs, with potential improvements in patient care and outcomes. The initial experience with AI in IBDs has shown its potential value in the differentiation of UC and CD when there is no ileal involvement, reducing the significant amount of time it takes to review videos of capsule endoscopy and improving the inter- and intra-observer variability in endoscopy reports and scoring. In addition, these initial experiences revealed the ability to predict the histologic score index and the presence of dysplasia. Thus, the purpose of this review was to summarize recent advances regarding the application of AI in IBD endoscopy as there is, indeed, increasing evidence suggesting that the integration of AI-based clinical tools will play a crucial role in paving the road to precision medicine in IBDs. Full article

Wireless capsule endoscopy (WCE) offers a non-invasive diagnostic alternative for the gastrointestinal tract using a battery-powered capsule. Despite advantages, WCE encounters issues with video quality and diagnostic accuracy, often resulting in missing rates of 1–20%. These challenges stem from weak texture characteristics due to non-Lambertian tissue reflections, uneven illumination, and the necessity of color fidelity. Traditional Retinex-based methods used for image enhancement are suboptimal for endoscopy, as they frequently compromise anatomical detail while distorting color. To address these limitations, we introduce QRNet, a novel quaternion-based Retinex framework. QRNet performs image decomposition into reflectance and illumination components within hypercomplex space, maintaining inter-channel relationships that preserve color fidelity. A quaternion wavelet attention mechanism refines essential features while suppressing noise, balancing enhancement and fidelity through an innovative loss function. Experiments on Kvasir-Capsule and Red Lesion Endoscopy datasets demonstrate notable improvements in metrics such as PSNR (+2.3 dB), SSIM (+0.089), and LPIPS (−0.126). Moreover, lesion segmentation accuracy increases by up to 5%, indicating the framework’s potential for improving early-stage lesion detection. Ablation studies highlight the quaternion representation’s pivotal role in maintaining color consistency, confirming the promise of this advanced approach for clinical settings. Full article

Capsule endoscopy is a common method for detecting digestive diseases. The location of a capsule endoscope should be constantly monitored through a visual inspection of the endoscopic images by medical staff to confirm the examination’s progress. In this study, we proposed a computer-aided analysis (CADx) method for the localization of a capsule endoscope. At first, a classifier based on a Swin Transformer was proposed to classify each frame of the capsule endoscopy videos into images of the stomach, small intestine, and large intestine, respectively. Then, a K-means algorithm was used to correct outliers in the classification results. Finally, a localization algorithm was proposed to determine the position of the capsule endoscope in the alimentary tract. The proposed method was developed and validated using videos of 204 consecutive cases. The proposed CADx, based on a Swin Transformer, showed a precision of 93.46%, 97.28%, and 98.68% for the classification of endoscopic images recorded in the stomach, small intestine, and large intestine, respectively. Compared with the landmarks identified by endoscopists, the proposed method demonstrated an average transition time error of 16.2 s to locate the intersection of the stomach and small intestine, as well as 13.5 s to locate that of the small intestine and the large intestine, based on the 20 validation videos with an average length of 3261.8 s. The proposed method accurately localizes the capsule endoscope in the alimentary tract and may replace the laborious real-time visual inspection in capsule endoscopic examinations. Full article

Objectives: In this single-center retrospective study, we aimed to verify the extent of duodenal follicular lymphoma (DFL) and investigate the role and clinical significance of video capsule endoscopy (VCE) in the treatment process. Methods: We analyzed the clinical and imaging data of 40 patients diagnosed with DFL. Results: Imaging workup and bone marrow biopsies revealed DFL only in the gastrointestinal tract (stage I) in 22 patients and in local lymph nodes (stage II₁), distant lymph nodes (stage II₂), pancreas (stage II₂E_pancreas), and extranodal regions (stage IV) in 1, 3, 1, and 13 patients, respectively. Fifteen of the 23 patients with localized (stages I and II₁) DFL underwent VCE for comprehensive small bowel evaluation, which revealed lesion extension beyond the duodenum in 10 patients (66.7%). A watch-and-wait strategy was implemented for one patient and systemic chemotherapy was administered to the remaining nine. Of the eight patients without VCE, seven and one received radiotherapy and observation, respectively. Nine of the 23 patients (39.1%) received systemic treatment based on positive VCE results. Only one of the 17 patients with advanced-stage DFL (stages II₂ and IV) accepted radiotherapy; 16 underwent systemic chemotherapy. During follow-up (median, 48.4 months), two relapse events occurred in the advanced stage, with no lymphoma-associated deaths. DFL tends to be indolent and has favorable outcomes. Conclusions: Proactive VCE for diagnosing DFL is recommended to determine small bowel involvement, which may influence subsequent treatment decisions. Full article

Conventional endoscopy is limited in its ability to examine the small bowel and perform long-term monitoring due to the risk of infection and tissue perforation. Wireless Capsule Endoscopy (WCE) is a painless and non-invasive method of examining the body’s internal organs using a small camera that is swallowed like a pill. The existing active locomotion technologies do not have a practical localization system to control the capsule’s movement within the body. A robust localization system is essential for safely guiding the WCE device through the complex gastrointestinal (GI) tract. Moreover, having access to the capsule’s trajectory data is highly desirable for drug delivery and surgery, as well as for creating accurate user profiles for diagnosis and future reference. Therefore, a robust, real-time, and practical localization system is imperative to advance the field of WCE and make it desirable for clinical trials. In this work, we have identified salient features of different localization techniques and categorized studies in comprehensive tables. This study is self-contained as it offers a comprehensive overview of emerging localization techniques based on magnetic field, radio frequency (RF), video, and hybrid methods. A summary at the end of each method is provided to point out the potential gaps and give directions for future research. The main point of this work is to present an in-depth review of the most recent localization techniques published in the past five years. This will assist researchers in comprehending current techniques and pinpointing potential areas for further investigation. This review can be a significant reference and guide for future research on WCE localization. Full article

Background: Colon capsule endoscopy (CCE) is becoming more widely available across Europe, but its uptake is slow due to the need for follow-up colonoscopy for therapeutic procedures and biopsies, which impacts its cost-effectiveness. One of the major factors driving the conversion to colonoscopy is the detection of excess polyps in CCE that cannot be matched during subsequent colonoscopy. The capsule’s rocking motion, which can lead to duplicate reporting of the same polyp when viewed from different angles, is likely a key contributor. Objectives: This review aims to explore the types of polyp matching reported in the literature, assess matching techniques and matching accuracy, and evaluate the development of machine learning models to improve polyp matching in CCE and subsequent colonoscopy. Methods: A systematic literature search was conducted in EMBASE, MEDLINE, and PubMed. Due to the scarcity of research in this area, the search encompassed clinical trials, observational studies, reviews, case series, and editorial letters. Three directly related studies were included, and ten indirectly related studies were included for review. Results: Polyp matching in colon capsule endoscopy still needs to be developed, with only one study focused on creating criteria to match polyps within the same CCE video. Another study established that experienced CCE readers have greater accuracy, reducing interobserver variability. A machine learning algorithm was developed in one study to match polyps between initial CCE and subsequent colonoscopy. Only around 50% of polyps were successfully matched, requiring further optimisation. As Artificial Intelligence (AI) algorithms advance in CCE polyp detection, the risk of duplicate reporting may increase when clinicians are presented with polyp images or timestamps, potentially complicating the transition to AI-assisted CCE reading in the future. Conclusions: Polyp matching in CCE is a developing field with considerable challenges, especially in matching polyps within the same video. Although AI shows potential for decent accuracy, more research is needed to refine these techniques and make CCE a more reliable, non-invasive alternative to complement conventional colonoscopy for lower GI investigations. Full article

Neuroendocrine tumours (NETs) are relatively rare neoplasms but represent one of the most frequent types of primary small-bowel tumours. Their incidence is rising, and this is most likely because of their more frequent early-stage detection, physician awareness, and increasing availability and use of imaging and small-bowel endoscopic techniques, such as video capsule endoscopy and device-assisted enteroscopy, which enable the detection, localisation, and histological sampling of previously inaccessible and underdiagnosed small-bowel lesions. This review summarises the role of small-bowel endoscopy in the diagnosis and management of small-bowel NETs to assist clinicians in their practice. Small-bowel endoscopy may play a complementary role in the diagnosis of these tumours alongside other diagnostic tests, such as biomarkers, conventional radiology, and functional imaging. In addition, small-bowel enteroscopy may play a role in the preoperative setting for the identification and marking of these tumours for surgical resection and the management of rare complications, such as small-bowel variceal bleeding, in cases of portal hypertension due to the encasement of mesenteric vessels in fibrotic small-bowel NETs. Full article

A 79 year old female individual presented to the hospital and complained of 1 month melena and anemia due to chronic gastrointestinal bleeding because of cavernous hemangiomatosis of the small bowel. After undergoing an initial video laparoscopic jejunal–ileal resection surgery 7 days after first hospitalization, given the persistence of anemia, she underwent laparotomic duodenojejunal resection surgery again 2 months later. Multiple cavernous hemangiomatosis is a rare vascular disease (7–10% of all benign small bowel tumors), and it often manifests with bleeding, which may be occult or massive; more rarely, it manifests with intestinal occlusion or perforation. Diagnoses often require the use of multiple radiological and endoscopic methods; video capsule endoscopy has significantly increased the diagnostic rate. The gold standard of treatment is surgical resection, whenever possible, balancing the need for radicality with the possible metabolic consequences of massive small intestine resections. Full article

Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives. Full article