Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology.

Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.

Artificial Intelligence and Panendoscopy-Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy.

Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE's diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm®, Porto, Portugal), 172 double-balloon enteroscopies (Olympus®, Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus®, Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images, n = 36,599) and testing dataset (10% of the images, n = 4066) used to evaluate the model. The CNN's output was compared to an expert consensus classification. The model was evaluated by its sensitivity, specificity, positive (PPV) and negative predictive values (NPV), accuracy and area under the precision recall curve (AUC-PR). The CNN had an 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and an AUC-PR of 0.97. Our group developed the first multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. The development of accurate deep learning models is of utmost importance for increasing the diagnostic yield of DAE-based panendoscopy.

Deep Learning in High-Resolution Anoscopy: Assessing the Impact of Staining and Therapeutic Manipulation on Automated Detection of Anal Cancer Precursors.

INTRODUCTION: High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell carcinoma (ASCC) precursors. Preliminary studies on the application of artificial intelligence (AI) models to this modality have revealed promising results. However, the impact of staining techniques and anal manipulation on the effectiveness of these algorithms has not been evaluated. We aimed to develop a deep learning system for automatic differentiation of high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion in HRA images in different subsets of patients (nonstained, acetic acid, lugol, and after manipulation). METHODS: A convolutional neural network was developed to detect and differentiate high-grade and low-grade anal squamous intraepithelial lesions based on 27,770 images from 103 HRA examinations performed in 88 patients. Subanalyses were performed to evaluate the algorithm's performance in subsets of images without staining, acetic acid, lugol, and after manipulation of the anal canal. The sensitivity, specificity, accuracy, positive and negative predictive values, and area under the curve were calculated. RESULTS: The convolutional neural network achieved an overall accuracy of 98.3%. The algorithm had a sensitivity and specificity of 97.4% and 99.2%, respectively. The accuracy of the algorithm for differentiating high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion varied between 91.5% (postmanipulation) and 100% (lugol) for the categories at subanalysis. The area under the curve ranged between 0.95 and 1.00. DISCUSSION: The introduction of AI to HRA may provide an accurate detection and differentiation of ASCC precursors. Our algorithm showed excellent performance at different staining settings. This is extremely important because real-time AI models during HRA examinations can help guide local treatment or detect relapsing disease.

AI-Driven Colon Cleansing Evaluation in Capsule Endoscopy: A Deep Learning Approach.

Gastroenterology is increasingly moving towards minimally invasive diagnostic modalities. The diagnostic exploration of the colon via capsule endoscopy, both in specific protocols for colon capsule endoscopy and during panendoscopic evaluations, is increasingly regarded as an appropriate first-line diagnostic approach. Adequate colonic preparation is essential for conclusive examinations as, contrary to a conventional colonoscopy, the capsule moves passively in the colon and does not have the capacity to clean debris. Several scales have been developed for the classification of bowel preparation for colon capsule endoscopy. Nevertheless, their applications are limited by suboptimal interobserver agreement. Our group developed a deep learning algorithm for the automatic classification of colonic bowel preparation, according to an easily applicable classification. Our neural network achieved high performance levels, with a sensitivity of 91%, a specificity of 97% and an overall accuracy of 95%. The algorithm achieved a good discriminating capacity, with areas under the curve ranging between 0.92 and 0.97. The development of these algorithms is essential for the widespread adoption of capsule endoscopy for the exploration of the colon, as well as for the adoption of minimally invasive panendoscopy.

Small Bowel Adenocarcinoma in a Patient with Crohn's Disease: The Role of Balloon-Assisted Enteroscopy.

Introduction: Small bowel adenocarcinoma is a rare but well-known complication of Crohn's disease. Diagnosis can be challenging, as clinical presentation may mimic an exacerbation of Crohn's disease and imaging findings may be indistinguishable from benign strictures. The result is that the majority of cases are diagnosed at the time of operation or postoperatively at an advanced stage. Case Presentation: A 48-year-old male with a previous 20-year history of ileal stenosing Crohn's disease presented with iron deficiency anemia. The patient reported melena approximately 1 month earlier but was currently asymptomatic. There were no other laboratory abnormalities. Anemia was refractory to intravenous iron replacement. The patient underwent computerized tomography enterography, which revealed multiple ileal strictures with features suggesting underlying inflammation and an area of sacculation with circumferential thickening of adjacent bowel loops. Therefore, the patient underwent retrograde balloon-assisted small bowel enteroscopy, where an area of irregular mucosa and ulceration was found at the region of ileo-ileal anastomosis. Biopsies were performed and histopathological examination revealed tubular adenocarcinoma infiltrating the muscularis mucosae. The patient underwent right hemicolectomy plus segmental enterectomy of the anastomotic region where the neoplasia was located. After 2 months, he is asymptomatic and there is no evidence of recurrence. Discussion: This case demonstrates that small bowel adenocarcinoma may have a subtle clinical presentation and that computed tomography enterography may not be accurate enough to distinguish benign from malignant strictures. Clinicians must, therefore, maintain a high index of suspicion for this complication in patients with long-standing small bowel Crohn's disease. In this setting, balloon-assisted enteroscopy may be a useful tool when there is raised concern for malignancy, and it is expected that its more widespread use could contribute to an earlier diagnosis of this severe complication.

Introdução: O adenocarcinoma do intestino delgado é uma complicação rara mas bem estabelecida da doença de Crohn. O seu diagnóstico pode ser desafiante, na medida em que a apresentação clínica pode mimetizar uma agudização da doença de Crohn e os achados imagiológicos podem ser indistinguíveis de estenoses benignas. Em consequência, a maioria dos casos são diagnosticados durante ou após a cirurgia em estadio avançado. Descrição do caso: Um homem de 48 anos com antecedentes de doença de Crohn ileal estenosante, com 20 anos de evolução, apresentou-se com anemia ferropénica. O doente referia melenas aproximadamente um mês antes, mas encontrava-se atualmente assintomático. Não apresentava outras alterações laboratoriais de relevo. A anemia era refratária a suplementação com ferro endovenoso. Foi submetido a enterografia por tomografia computorizada, que revelou múltiplas estenoses ileais com caraterísticas sugestivas de atividade inflamatória e uma área de saculação com espessamento circunferencial das ansas de intestino delgado adjacentes. Assim, foi submetido a enteroscopia assistida por balão, onde se identificou uma área de mucosa irregular e ulceração na região da anastomose ileo-ileal. Biópsias desta área revelaram a presença de adenocarcinoma tubular com infiltração até à muscularis mucosae. O doente foi submetido a hemicolectomia direita com enterectomia segmentar da região da anastomose onde a neoplasia se encontrava localizada. Ao fim de 2 meses, o doente encontra-se assintomático e sem evidência de recorrência. Discussão: Este caso demonstra que o adenocarcinoma do intestino delgado pode ter uma apresentação clínica subtil e que a enterografia por tomografia computorizada pode não ter precisão suficiente para distinguir estenoses benignas de neoplasias malignas. Os clínicos devem, portanto, manter um elevado índice de suspeição diagnóstica para esta complicação em doentes com doença de Crohn ileal de longa duração. Neste contexto, a enteroscopia assistida por balão pode ser uma ferramenta útil em casos de suspeita de neoplasia maligna, esperando- se que possa contribuir para um diagnóstico mais precoce desta complicação severa.

Artificial intelligence and colon capsule endoscopy: Automatic detection of ulcers and erosions using a convolutional neural network.

BACKGROUND AND AIM: Colon capsule endoscopy (CCE) has become a minimally invasive alternative for conventional colonoscopy. Nevertheless, each CCE exam produces between 50 000 and 100 000 frames, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNNs) are a type of artificial intelligence (AI) architecture with high performance in image analysis. This study aims to develop a CNN model for the identification of colonic ulcers and erosions in CCE images. METHODS: A CNN model was designed using a database of CCE images. A total of 124 CCE exams performed between 2010 and 2020 in two centers were reviewed. For CNN development, a total of 37 319 images were extracted, 33 749 showing normal colonic mucosa and 3570 showing colonic ulcers and erosions. Datasets for CNN training, validation, and testing were created. The performance of the algorithm was evaluated regarding its sensitivity, specificity, positive and negative predictive values, accuracy, and area under the curve. RESULTS: The network had a sensitivity of 96.9% and a specificity of 99.9% specific for the detection of colonic ulcers and erosions. The algorithm had an overall accuracy of 99.6%. The area under the curve was 1.00. The CNN had an image processing capacity of 90 frames per second. CONCLUSIONS: The developed algorithm is the first CNN-based model to accurately detect ulcers and erosions in CCE images, also providing a good image processing performance. The development of these AI systems may contribute to improve both the diagnostic and time efficiency of CCE exams, facilitating CCE adoption to routine clinical practice.

Deep Learning for Automatic Differentiation of Mucinous versus Non-Mucinous Pancreatic Cystic Lesions: A Pilot Study.

Endoscopic ultrasound (EUS) morphology can aid in the discrimination between mucinous and non-mucinous pancreatic cystic lesions (PCLs) but has several limitations that can be overcome by artificial intelligence. We developed a convolutional neural network (CNN) algorithm for the automatic diagnosis of mucinous PCLs. Images retrieved from videos of EUS examinations for PCL characterization were used for the development, training, and validation of a CNN for mucinous cyst diagnosis. The performance of the CNN was measured calculating the area under the receiving operator characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. A total of 5505 images from 28 pancreatic cysts were used (3725 from mucinous lesions and 1780 from non-mucinous cysts). The model had an overall accuracy of 98.5%, sensitivity of 98.3%, specificity of 98.9% and AUC of 1. The image processing speed of the CNN was 7.2 ms per frame. We developed a deep learning algorithm that differentiated mucinous and non-mucinous cysts with high accuracy. The present CNN may constitute an important tool to help risk stratify PCLs.

Deep learning and colon capsule endoscopy: automatic detection of blood and colonic mucosal lesions using a convolutional neural network.

Background and study aims Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. However, CCE produces long videos, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence (AI) algorithms with high performance levels in image analysis. We aimed to develop a deep learning model for automatic identification and differentiation of significant colonic mucosal lesions and blood in CCE images. Patients and methods A retrospective multicenter study including 124 CCE examinations was conducted for development of a CNN model, using a database of CCE images including anonymized images of patients with normal colon mucosa, several mucosal lesions (erosions, ulcers, vascular lesions and protruding lesions) and luminal blood. For CNN development, 9005 images (3,075 normal mucosa, 3,115 blood and 2,815 mucosal lesions) were ultimately extracted. Two image datasets were created and used for CNN training and validation. Results The mean (standard deviation) sensitivity and specificity of the CNN were 96.3 % (3.9 %) and 98.2 % (1.8 %) Mucosal lesions were detected with a sensitivity of 92.0 % and a specificity of 98.5 %. Blood was detected with a sensitivity and specificity of 97.2 % and 99.9 %, respectively. The algorithm was 99.2 % sensitive and 99.6 % specific in distinguishing blood from mucosal lesions. The CNN processed 65 frames per second. Conclusions This is the first CNN-based algorithm to accurately detect and distinguish colonic mucosal lesions and luminal blood in CCE images. AI may improve diagnostic and time efficiency of CCE exams, thus facilitating CCE adoption to routine clinical practice.

Identification of Ulcers and Erosions by the Novel Pillcam™ Crohn's Capsule Using a Convolutional Neural Network: A Multicentre Pilot Study.

BACKGROUND AND AIMS: Capsule endoscopy is a central element in the management of patients with suspected or known Crohn's disease. In 2017, PillCam™ Crohn's Capsule was introduced and demonstrated to have greater accuracy in the evaluation of extension of disease in these patients. Artificial intelligence [AI] is expected to enhance the diagnostic accuracy of capsule endoscopy. This study aimed to develop an AI algorithm for the automatic detection of ulcers and erosions of the small intestine and colon in PillCam™ Crohn's Capsule images. METHODS: A total of 8085 PillCam™ Crohn's Capsule images were extracted between 2017 and 2020, comprising 2855 images of ulcers and 1975 erosions; the remaining images showed normal enteric and colonic mucosa. This pool of images was subsequently split into training and validation datasets. The performance of the network was subsequently assessed in an independent test set. RESULTS: The model had an overall sensitivity and specificity of 90.0% and 96.0%, respectively. The precision and accuracy of this model were 97.1% and 92.4%, respectively. In particular, the algorithm detected ulcers with a sensitivity of 83% and specificity of 98%, and erosions with sensitivity and specificity of 91% and 93%, respectively. CONCLUSION: A deep learning model capable of automatically detecting ulcers and erosions in PillCam™ Crohn's Capsule images was developed for the first time. These findings pave the way for the development of automatic systems for detection of clinically significant lesions, optimizing the diagnostic performance and efficiency of monitoring Crohn's disease activity.

Vedolizumab-Induced Liver Injury.

Drug-induced liver injury is an important cause of acute liver injury. Immunomodulatory therapies, such as vedolizumab (VDZ), are being increasingly used for the treatment of several diseases, most importantly inflammatory bowel disease. Several studies have demonstrated the safety of this substance. To date, only one post-marketing study has reported a case of hepatotoxicity attributable to VDZ. The authors present the case of a 41-year-old woman followed at the gastroenterology outpatient clinic for ulcerative colitis (UC) and autoimmune hepatitis (AIH). This patient was being treated with low-dose glucocorticoids for AIH (prednisolone 10 mg), with adequate disease control. Additionally, she was being treated with oral salicylates (mesalamine 3 g/day) and oral budesonide (9 mg/day) for her UC. For uncontrolled UC, she was started on VDZ. Two weeks after the first infusion of VDZ, the patient developed a clinical and analytical phenotype compatible with acute hepatitis. Diagnostic workup for causes of hepatocellular liver injury retrieved no results. A liver biopsy corroborated the diagnosis of toxic hepatitis overlapping chronic liver disease. VDZ was withdrawn and the patient experienced complete recovery of liver tests over the following weeks. In this case report, we present the first post-marketing case of hepatocellular liver injury in probable relation to VDZ.

A hepatotoxicidade induzida por fármacos é uma causa importante de lesão hepática aguda. As terapêuticas imunomoduladoras, como o vedolizumab (VDZ), são cada vez mais utilizadas para o tratamento de diversas patologias, particularmente a doença inflamatória do intestino. Vários estudos comprovaram o perfil de segurança favorável do VDZ. Até à data, apenas foi relatado um caso de hepatotoxicidade atribuída ao VDZ em estudos de farmacovigilância pós comercialização. Os autores apresentam o caso de uma mulher de 41 anos, seguida em Gastrenterologia por colite ulcerosa (CU) e hepatite autoimune (HAI). A doença hepática desta doente encontrava-se eficazmente controlada com corticoterapia em baixa dose (prednisolona 10 mg). Concomitantemente, a terapêutica dirigida à CU incluía salicilatos (messalazina 3 g/dia) e budesonido (9 mg/dia) orais. Por apresentar CU não controlada com o esquema referido, iniciou VDZ. Duas semanas após a primeira infusão de VDZ, a doente desenvolve manifestações clínicas e analíticas compatíveis com quadro de hepatite. Do estudo etiológico realizado, nenhuma causa foi identificada como responsável pela lesão hepatocelular. Foi realizada uma biópsia hepática que corroborou o diagnóstico de hepatite tóxica a sobreporse a características de doença hepática crónica. A terapêutica com VDZ foi interrompida e a doente apresentou recuperação de valores normais do perfil hepático durante as semanas que se sucederam. Com este trabalho, os autores expõem o primeiro caso pós-comercialização de lesão hepatocelular em provável relação com VDZ.

Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network.

OBJECTIVE: Capsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the small bowel. CE frequently identifies a wide range of lesions with different bleeding potentials in these patients. However, reading CE examinations is a time-consuming task. Convolutional neural networks (CNNs) are highly efficient artificial intelligence tools for image analysis. This study aims to develop a CNN-based model for identification and differentiation of multiple small bowel lesions with distinct haemorrhagic potential using CE images. DESIGN: We developed, trained, and validated a denary CNN based on CE images. Each frame was labelled according to the type of lesion (lymphangiectasia, xanthomas, ulcers, erosions, vascular lesions, protruding lesions, and blood). The haemorrhagic potential was assessed by Saurin's classification. The entire dataset was divided into training and validation sets. The performance of the CNN was measured by the area under the receiving operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: A total of 53 555 CE images were included. The model had an overall accuracy of 99%, a sensitivity of 88%, a specificity of 99%, a PPV of 87%, and an NPV of 99% for detection of multiple small bowel abnormalities and respective classification of bleeding potential. CONCLUSION: We developed and tested a CNN-based model for automatic detection of multiple types of small bowel lesions and classification of the respective bleeding potential. This system may improve the diagnostic yield of CE for these lesions and overall CE efficiency.

Artificial intelligence and colon capsule endoscopy: automatic detection of blood in colon capsule endoscopy using a convolutional neural network.

Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. Most studies on CCE focus on colorectal neoplasia detection. The development of automated tools may address some of the limitations of this diagnostic tool and widen its indications for different clinical settings. We developed an artificial intelligence model based on a convolutional neural network (CNN) for the automatic detection of blood content in CCE images. Training and validation datasets were constructed for the development and testing of the CNN. The CNN detected blood with a sensitivity, specificity, and positive and negative predictive values of 99.8 %, 93.2 %, 93.8 %, and 99.8 %, respectively. The area under the receiver operating characteristic curve for blood detection was 1.00. We developed a deep learning algorithm capable of accurately detecting blood or hematic residues within the lumen of the colon based on colon CCE images.

A case-control study demonstrates an improved visualization when capsule endoscopy is performed after preparation with polyethylene glycol and ascorbic acid.

BACKGROUND: capsule endoscopy is increasingly used to obtain images of the gastrointestinal tract, although the best preparation for this type of exploration remains unclear. AIMS: the aim of this study was to compare the results of capsule endoscopy explorations performed after a basic preparation with a clear liquid diet, reduced iron intake and fasting or following preparation with a polyethylene glycol (PEG)/ascorbate solution. METHODS: the results obtained from a prospective intervention group that used a PEG/ascorbate solution to prepare for capsule endoscopy were compared with those from a retrospective group of patients who followed a more basic preparation. The quality of visualization was assessed with the Park score, the visualization of the mucosal surface and the cleanliness of the intestinal lumen were assessed. The capsule transit time in different segments of the gastrointestinal tract was also evaluated. RESULTS: a significant improvement in the quality of small intestine visualization was observed in individuals prepared with the PEG/ascorbate solution as opposed to the basic preparation. In fact, there were significant differences in the two separate components that contribute to the overall visualization score, with better mucosa visualization and lumen content scores in the intervention group, thus reflecting an improved performance. The presence of diabetes appeared to affect the results of these explorations, at least when using the PEG/ascorbate preparation. CONCLUSIONS: preparation with a PEG/ascorbate solution improved the results of capsule endoscopy when compared to a basic preparation, without the inconvenience of the more stringent preparations used for colonoscopies.

Endoscopic diagnosis of primary and recurrent mantle cell lymphomas.

A 64-year-old male presented to our center with epigastric pain, anorexia, fatigue, weight loss and anemia on a laboratory study. An abdominal ultrasound revealed thickening of the gastric walls. Upper endoscopy demonstrated a 40 mm ulcerated lesion at the incisura angularis. A biopsy of the lesion revealed a lymphocytic infiltrate expressing CD5, CD20 and cyclin D1, typical of mantle-cell lymphoma (MCL).

Effectiveness and tolerability of linaclotide in the treatment of IBS-C in a "real-life" setting: Results from a Portuguese single-center study.

BACKGROUND: Although linaclotide has been approved to treat moderate to severe IBS-C, no data are available on its effectiveness and tolerability in patients in a real-life setting. METHODS: A prospective single-center study of the effectiveness and tolerability of linaclotide was carried out on patients (n = 40) with moderate to severe IBS-C, all fulfilling the Rome IV criteria. Clinical information was recorded using a dietary questionnaire at baseline, and 3 and 6 months after initiating treatment. The end-points to measure effectiveness included abdominal pain and bloating (11-NRS), the number of bowel movements and patient satisfaction. Tolerability was assessed through the frequency of adverse events. KEY RESULTS: In terms of efficacy, an improvement in abdominal pain and in the intensity of bloating was evident in the cohort after 6 months of linaclotide therapy. The proportion of patients with moderate or severe symptoms of bloating fell from 93.3% to 33.3% and those with pain from 93.4% to 20%. Weekly bowel movements also improved and accordingly, 97% of the patients were moderately or very satisfied with the treatment. At the end of the study, diarrhea was the most frequent adverse event (10%), although it was considered mild in 66.7% of these subjects and moderate in 33.3%. A lack of efficacy (n = 3) and excessive diarrhea (n = 7) were motives for discontinuing the treatment. CONCLUSIONS AND INFERENCES: Linaclotide proved to be a safe and effective drug to reduce the main symptoms of IBS-C in everyday clinical practice, with an improvement comparable to that seen in clinical trials.

Impact of gastroesophageal reflux control through tailored proton pump inhibition therapy or fundoplication in patients with Barrett's esophagus.

AIM: To determine the impact of upwards titration of proton pump inhibition (PPI) on acid reflux, symptom scores and histology, compared to clinically successful fundoplication. METHODS: Two cohorts of long-segment Barrett's esophagus (BE) patients were studied. In group 1 (n = 24), increasing doses of PPI were administered in 8-wk intervals until acid reflux normalization. At each assessment, ambulatory 24 h pH recording, endoscopy with biopsies and symptom scoring (by a gastroesophageal reflux disease health related quality of life questionnaire, GERD/HRLQ) were performed. Group 2 (n = 30) consisted of patients with a previous fundoplication. RESULTS: In group 1, acid reflux normalized in 23 of 24 patients, resulting in improved GERD/HRQL scores (P = 0.001), which were most pronounced after the starting dose of PPI (P < 0.001). PPI treatment reached the same level of GERD/HRQL scores as after a clinically successful fundoplication (P = 0.5). Normalization of acid reflux in both groups was associated with reduction in papillary length, basal cell layer thickness, intercellular space dilatation, and acute and chronic inflammation of squamous epithelium. CONCLUSION: This study shows that acid reflux and symptom scores co-vary throughout PPI increments in long-segment BE patients, especially after the first dose of PPI, reaching the same level as after a successful fundoplication. Minor changes were found among GERD markers at the morphological level.

Is capsule colonoscopy the solution for incomplete conventional colonoscopy?

The era of colon capsule endoscopy (CCE) started in 2007. Few years later second-generation CCE (CCE-2) (Medtronic, Minneapolis, USA) was launched, featuring an improved optical system allowing for nearly 360° coverage via two 172° angle cameras, and adaptive frame rate function (ranging from 4 to 35 images per second depending on capsule motion). At present the main clinical indications for CCE are: a) completion of incomplete colonoscopy; b) polyp detection; and c) investigation of inflammatory bowel disease (IBD).

Multicenter survey on the use of device-assisted enteroscopy in Portugal.

BACKGROUND: Device-assisted enteroscopies (DAEs) are recent endoscopic techniques that enable direct endoscopic small-bowel evaluation. OBJECTIVE: The objective of this article is to evaluate the implementation of DAEs in Portugal and assess the main indications, diagnoses, diagnostic yield, therapeutic yield and complication rate. METHODS: We conducted a multicenter retrospective series using a national Web-based survey on behalf of the Portuguese Small-Bowel Study Group. Participants were asked to fill out two online databases regarding procedural data, indications, diagnoses, endoscopic therapy and complications using prospectively collected institutional data records. RESULTS: A total of eight centers were enrolled in the survey, corresponding to 1411 DAEs. The most frequent indications were obscure gastrointestinal bleeding (OGIB), inflammatory bowel disease and small-bowel tumors. The pooled diagnostic yield was 63%. A relation between the diagnostic yield and the indications was clear, with a diagnostic yield for OGIB of 69% (p = 0.02) with a 52% therapeutic yield. Complications occurred in 1.2%, with a major complication rate of 0.57%. Perforations occurred in four patients (0.28%). CONCLUSION: DAEs are safe and effective procedures, with complication rates of 1.2%, the most serious of which is perforation. Most procedures are performed in the setting of OGIB. Diagnostic and therapeutic yields are dependent on the indication, hence appropriate patient selection is crucial.