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 and Minimally Invasive Endoscopy: Automatic Classification of Pleomorphic Gastric Lesions in Capsule Endoscopy

INTRODUCTION

Capsule endoscopy (CE) is a minimally invasive examination for evaluating the gastrointestinal tract. However, its diagnostic yield for detecting gastric lesions is suboptimal. Convolutional neural networks (CNNs) are artificial intelligence models with great performance for image analysis. Nonetheless, their role in gastric evaluation by wireless CE (WCE) has not been explored.

METHODS

Our group developed a CNN-based algorithm for the automatic classification of pleomorphic gastric lesions, including vascular lesions (angiectasia, varices, and red spots), protruding lesions, ulcers, and erosions. A total of 12,918 gastric images from 3 different CE devices (PillCam Crohn's; PillCam SB3; OMOM HD CE system) were used from the construction of the CNN: 1,407 from protruding lesions; 994 from ulcers and erosions; 822 from vascular lesions; and 2,851 from hematic residues and the remaining images from normal mucosa. The images were divided into a training (split for three-fold cross-validation) and validation data set. The model's output was compared with a consensus classification by 2 WCE-experienced gastroenterologists. The network's performance was evaluated by its sensitivity, specificity, accuracy, positive predictive value and negative predictive value, and area under the precision-recall curve.

RESULTS

The trained CNN had a 97.4% sensitivity; 95.9% specificity; and positive predictive value and negative predictive value of 95.0% and 97.8%, respectively, for gastric lesions, with 96.6% overall accuracy. The CNN had an image processing time of 115 images per second.

DISCUSSION

Our group developed, for the first time, a CNN capable of automatically detecting pleomorphic gastric lesions in both small bowel and colon CE devices.

Artificial Intelligence and Anorectal Manometry: Automatic Detection and Differentiation of Anorectal Motility Patterns-A Proof-of-Concept Study

INTRODUCTION

Anorectal manometry (ARM) is the gold standard for the evaluation of anorectal functional disorders, prevalent in the population. Nevertheless, the accessibility to this examination is limited, and the complexity of data analysis and report is a significant drawback. This pilot study aimed to develop and validate an artificial intelligence model to automatically differentiate motility patterns of fecal incontinence (FI) from obstructed defecation (OD) using ARM data.

METHODS

We developed and tested multiple machine learning algorithms for the automatic interpretation of ARM data. Four models were tested: k-nearest neighbors, support vector machines, random forests, and gradient boosting (xGB). These models were trained using a stratified 5-fold strategy. Their performance was assessed after fine-tuning of each model's hyperparameters, using 90% of data for training and 10% of data for testing.

RESULTS

A total of 827 ARM examinations were used in this study. After fine-tuning, the xGB model presented an overall accuracy (84.6% ± 2.9%), similar to that of random forests (82.7% ± 4.8%) and support vector machines (81.0% ± 8.0%) and higher that of k-nearest neighbors (74.4% ± 3.8%). The xGB models showed the highest discriminating performance between OD and FI, with an area under the curve of 0.939.

DISCUSSION

The tested machine learning algorithms, particularly the xGB model, accurately differentiated between FI and OD manometric patterns. Subsequent development of these tools may optimize the access to ARM studies, which may have a significant impact on the management of patients with anorectal functional diseases.

Clinicopathological characteristics and prognostic factors of small bowel lymphomas: a retrospective single-center study

Background

There is little information on diagnosis and management of small bowel lymphomas, and optimal management strategies are still undefined. This study aims to describe their main clinical and pathological characteristics and identify poor prognostic factors.

Methods

A retrospective observational study of all patients with histological diagnosis of small bowel lymphoma between January 2010 and December 2020 was performed.

Results

We included 40 patients, with male predominance (60%) and mean age of 60.7 years. The ileum was the most common location, and the most common histological subtypes were follicular lymphoma and diffuse large B-cell lymphoma. Clinical presentation was variable from asymptomatic patients (30%) to acute surgical complications (35%) including perforation, intestinal obstruction, ileal intussusception, or severe bleeding. Diagnosis was established by endoscopy in 22 patients (55%), and the most common findings included polyps, single mass, diffuse infiltration, or ulceration, whereas 18 (45%) required surgery because of acute presentations or tumor resection, and lymphoma was diagnosed postoperatively. Surgery was curative in one-third of those patients. Median survival was 52 months. Acute presentation (P = 0.001), symptomatic disease (P = 0.003), advanced stage (P = 0.008), diffuse large B-cell lymphoma (P = 0.007), anemia (P = 0.006), hypoalbuminemia (P < 0.001), elevated lactate dehydrogenase (P = 0.02), elevated C-reactive protein (P < 0.001), and absence of treatment response (P < 0.001) were significant predictors of mortality.

Conclusion

Small bowel lymphoma is a rare malignancy with diverse clinical and endoscopic presentations that require a high index of suspicion. Primary factors associated with worse outcome included acute presentation, advanced stage, histological subtype, biochemical abnormalities, and absence of treatment response.

Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy

Background and objectives: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel cleansing to obtain conclusive results. Artificial intelligence (AI) algorithms have been seen to offer important benefits in the field of medical imaging over recent years, particularly through the adaptation of convolutional neural networks (CNNs) to achieve more efficient image analysis. Here, we aimed to develop a deep learning model that uses a CNN to automatically classify the quality of intestinal preparation in CE. Methods: A CNN was designed based on 12,950 CE images obtained at two clinical centers in Porto (Portugal). The quality of the intestinal preparation was classified for each image as: excellent, ≥90% of the image surface with visible mucosa; satisfactory, 50-90% of the mucosa visible; and unsatisfactory, <50% of the mucosa visible. The total set of images was divided in an 80:20 ratio to establish training and validation datasets, respectively. The CNN prediction was compared with the classification established by consensus of a group of three experts in CE, currently considered the gold standard to evaluate cleanliness. Subsequently, how the CNN performed in diagnostic terms was evaluated using an independent validation dataset. Results: Among the images obtained, 3633 were designated as unsatisfactory preparation, 6005 satisfactory preparation, and 3312 with excellent preparation. When differentiating the classes of small-bowel preparation, the algorithm developed here achieved an overall accuracy of 92.1%, with a sensitivity of 88.4%, a specificity of 93.6%, a positive predictive value of 88.5%, and a negative predictive value of 93.4%. The area under the curve for the detection of excellent, satisfactory, and unsatisfactory classes was 0.98, 0.95, and 0.99, respectively. Conclusions: A CNN-based tool was developed to automatically classify small-bowel preparation for CE, and it was seen to accurately classify intestinal preparation for CE. The development of such a system could enhance the reproducibility of the scales used for such purposes.

The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents

With modern society well entrenched in the digital area, the use of Artificial Intelligence (AI) to extract useful information from big data has become more commonplace in our daily lives than we perhaps realize. Medical specialties that rely heavily on imaging techniques have become a strong focus for the incorporation of AI tools to aid disease diagnosis and monitoring, yet AI-based tools that can be employed in the clinic are only now beginning to become a reality. However, the potential introduction of these applications raises a number of ethical issues that must be addressed before they can be implemented, among the most important of which are issues related to privacy, data protection, data bias, explainability and responsibility. In this short review, we aim to highlight some of the most important bioethical issues that will have to be addressed if AI solutions are to be successfully incorporated into healthcare protocols, and ideally, before they are put in place. In particular, we contemplate the use of these aids in the field of gastroenterology, focusing particularly on capsule endoscopy and highlighting efforts aimed at resolving the issues associated with their use when available.

Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network

BACKGROUND AND AIMS

capsule endoscopy (CE) revolutionized the study of the small intestine. Nevertheless, reviewing CE images is time-consuming and prone to error. Artificial intelligence algorithms, particularly convolutional neural networks (CNN), are expected to overcome these drawbacks. Protruding lesions of the small intestine exhibit enormous morphological diversity in CE images. This study aimed to develop a CNN-based algorithm for the automatic detection small bowel protruding lesions.

METHODS

a CNN was developed using a pool of CE images containing protruding lesions or normal mucosa from 1,229 patients. A training dataset was used for the development of the model. The performance of the network was evaluated using an independent dataset, by calculating its sensitivity, specificity, accuracy, positive and negative predictive values.

RESULTS

a total of 18,625 CE images (2,830 showing protruding lesions and 15,795 normal mucosa) were included. Training and validation datasets were built with an 80 %/20 % distribution, respectively. After optimizing the architecture of the network, our model automatically detected small-bowel protruding lesions with an accuracy of 92.5 %. CNN had a sensitivity and specificity of 96.8 % and 96.5 %, respectively. The CNN analyzed the validation dataset in 53 seconds, at a rate of approximately 70 frames per second.

CONCLUSIONS

we developed an accurate CNN for the automatic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE.

Primary small bowel follicular lymphoma: the role of balloon-assisted enteroscopy in diagnosis and follow-up

An asymptomatic 38-year-old male with no significant previous medical history performed routine laboratory studies that revealed iron-deficiency anemia. Esophagogastroduodenoscopy and colonoscopy were unremarkable and he undergone videocapsule endoscopy that revealed multiple small polyps along jejunum and ileum. Double-balloon enteroscopy confirmed the presence of scattered small whitish nodules and small polyps carpeting segments of jejunal mucosal and sometimes forming conglomerates with a nodular appearance. Histopathological examination showed lamina propria expansion by neoplastic follicles, predominantly composed by small lymphoid cells that, by immunohistochemistry, showed expression of CD20, CD10 and bcl-2. Computed tomography scan of abdomen and pelvis did not reveal systemic involvement, consistent with primary small bowel follicular lymphoma. Chemotherapy was started and, at reevaluation enteroscopy, although nodular jejunal segments persisted, biopsies did not show involvement by lymphoproliferative disease, which was interpreted as complete remission. Periodic clinical and biochemical evaluation and annual enteroscopic surveillance was maintained and, after three years, local recurrence of low-grade follicular lymphoma was detected. As previously, there was no evidence of systemic involvement and the decision was to maintain close surveillance. After one year, the patient remains asymptomatic and without evidence of disease progression. This case illustrates the essential role of balloon-assisted enteroscopy for diagnosis and surveillance of primary small bowel follicular lymphoma.

Hepatic amyloidosis: a prevalence study and clinical characterization of a rare and severe disease

BACKGROUND AND AIM

Amyloidosis is a systemic disease characterized by extracellular deposition of amyloid protein, most commonly in the heart and kidney. Hepatic amyloidosis is a rare form of presentation that ranges from mild hepatomegaly and altered liver biochemical tests to acute liver failure. The aims of this study were to evaluate the prevalence of amyloidosis in patients undergoing liver biopsy and describe its main clinical characteristics and prognostic impact.

METHODS

A retrospective analysis of all patients with a histological diagnosis of hepatic amyloidosis between January 2010 and December 2019 was performed.

MAJOR RESULTS

A total of 7 patients were identified from a total of 1773 liver biopsy procedures (0.4%), with a female predominance (6/7) and median age of diagnosis of 62 years. The most common clinical manifestations included hepatomegaly (4/7), jaundice (2/7) and peripheral edema (2/7), whereas 3/7 patients were asymptomatic. Every patient presented abnormalities in liver biochemical tests, more commonly cholestasis (6/7), but also cytolysis (4/7) or hyperbilirubinemia (2/7). Abnormal imaging findings included hepatomegaly, steatosis or parenchymal heterogeneity. In most patients (5/7), other organs were involved, most commonly with nephrotic syndrome (3/7) and infiltrative cardiomyopathy (3/7). The most common type was AA amyloidosis (3/7) followed by AL amyloidosis (2/7). The 1-year mortality rate was 43% and the median survival was 24 months.

CONCLUSIONS

We report a low prevalence (0.4%) of amyloidosis among patients undergoing liver biopsy. Although rare, hepatic amyloidosis is associated with a dismal prognosis and a high index of suspicion is crucial to achieve an early diagnosis. .

Liver biopsy in inflammatory bowel disease patients with sustained abnormal liver function tests: a retrospective single-center study

Background

Inflammatory bowel disease (IBD) may be associated with a wide range of hepatobiliary manifestations. This study aimed to characterize the spectrum of hepatobiliary disorders in patients with IBD who underwent liver biopsy for sustained abnormal liver function tests (LFT).

Method

A retrospective study was performed of all patients with IBD who underwent liver biopsy between January 2010 and December 2020 for sustained abnormal LFT (at least 6-month duration).

Results

A total of 101 patients were included, mostly male (62.4%), with a mean age of 44.4±13.3 years. The most common IBD type was Crohn's disease (61.4%). Median time interval between abnormal LFT and biopsy was 14 (7-36) months. Abnormal LFT was predominantly hepatocellular in 40 patients (39.6%), cholestatic in 26 (25.7%) and mixed in 35 (34.7%). The most frequent diseases were nonalcoholic fatty liver disease (NAFLD) in 33 patients (32.7%), drug-induced liver disease (DILI) in 30 (29.7%), autoimmune hepatitis (AIH) in 13 (12.9%) and primary sclerosing cholangitis (PSC) in 13 (12.9%). Three patients had primary biliary cholangitis. Remarkably, 70 patients (69.3%) already had fibrosis by the time of liver biopsy and in 6 (5.9%) liver disease was already detected in the stage of cirrhosis.

Conclusions

Abnormal LFT in IBD patients had a wide range of etiologies and histology was often essential for reaching a correct diagnosis. NAFLD, DILI, AIH and PSC were the most common diagnoses and patients often presented in cirrhotic stage. Therefore, liver biopsy must be considered early in IBD patients with unexplained sustained abnormal LFT.

Splenic-hepatic elastography index is useful in differentiating between porto-sinusoidal vascular disease and cirrhosis in patients with portal hypertension

INTRODUCTION

In patients with portal hypertension (PH), the differential diagnosis between porto-sinusoidal vascular disease (PSVD) and cirrhosis is challenging. This study aims to evaluate the diagnostic accuracy of the SSM/LSM index in the diagnosis of PSVD.

METHODS

Prospective study of patients with PH and PSVD or cirrhosis. Transient liver and spleen elastography were performed and the ratio between spleen stiffness measurement (SSM) and liver stiffness measurement (LSM) was calculated. The relation of SSM/LSM with the diagnosis of PSVD was evaluated.

RESULTS

Forty-four patients with PSVD and 44 patients with cirrhosis were evaluated. Median age was 57.5 (IQR 49.0-64.5) years, 66.3% were males. In patients with PSVD, median SSM was 59.4 (33.5-77.7) kPa, median LSM was 6.2 (5.2-10.2) kPa and median SSM/LSM was 5.62 (3.15-9.68). In patients with cirrhosis, median SSM was 47.3 (24.3-60.3) kPa, median LSM was 27.8 (17.7-53.9) kPa and median SSM/LSM was 1.55 (1.06-3.24). The SSM/LSM AUROC was 0.940 (p<0.001). Using 2 as a cut-off, we obtained good sensitivity (86.5%), specificity (92.7%), and accuracy (89.7%) for the diagnosis of PSVD.

CONCLUSION

The SSM/LSM index is useful in the differential diagnosis between liver cirrhosis and PSVD. Using the cut-off of 2 we achieved a good sensitivity and specificity for diagnosing PSVD.

Peri-operative management of a patient with an ectodermal dysplasia (Rapp-Hodgkin) syndrome

We present the case of a 41-year-old man with Rapp-Hodgkin syndrome who underwent nasal septum deviation surgery under general anaesthesia. This syndrome is rare, with approximately 70 cases reported worldwide. It is one of a group of ectodermal dysplasia syndromes and results from the aberrant development of ectoderm during fetal development. Some of the clinical features may affect anaesthetic management. The most important considerations are potentially difficult airway management, the need for meticulous temperature control, and the importance of skin protection. This case was uneventful, but as there are few case reports on the management of patients with ectodermal dysplasia syndromes undergoing anaesthesia this report contributes useful knowledge. The pathogenesis and clinical features of Rapp-Hodgkin syndrome and the anaesthetic management for this patient are described.

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.

Bronchial-biliary fistula secondary to cholangiocarcinoma: long-term efficacy of biliary self-expandable metal stent

A 77-year-old female with previous medical history of non-cirrhotic chronic hepatitis B and hepatocellular carcinoma treated with sequential partial hepatectomy followed by transarterial chemoembolization complained of pruritus and jaundice. Magnetic resonance cholangiopancreatography revealed a peri-hilar ill-defined stenosing lesion suggestive of malignancy. Endoscopic retrograde cholangiopancreatography with cholangioscopy confirmed a circumferential peri-hilar stenosis with fragile mucosa and tortuous dilated vessels and biopsies of this area were consistent cholangiocarcinoma. After 3 months, she presented with new-onset dyspnea and bilioptysis and abdominal computed tomography revealed a bronchial-biliary fistula. ERCP was performed to place a self-expandable metal stent in the biliary tract, which resulted in rapid clinical improvement. The patient has been followed for 2 years and remains globally stable with two episodes of worsening of bilioptysis secondary to stent obstruction by lithiasis that were easily resolved with Fogarty balloon-assisted extraction, with rapid improvement. This case demonstrates the long-term efficacy of endoscopic biliary drainage with self-expandable metallic stent for bronchial-biliary fistula in the setting of cholangiocarcinoma. .

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.

Artificial Intelligence and Device-Assisted Enteroscopy: Automatic Detection of Enteric Protruding Lesions Using a Convolutional Neural Network

INTRODUCTION

Device-assisted enteroscopy (DAE) plays a major role in the investigation and endoscopic treatment of small bowel diseases. Recently, the implementation of artificial intelligence (AI) algorithms to gastroenterology has been the focus of great interest. Our aim was to develop an AI model for the automatic detection of protruding lesions in DAE images.

METHODS

A deep learning algorithm based on a convolutional neural network was designed. Each frame was evaluated for the presence of enteric protruding lesions. The area under the curve, sensitivity, specificity, and positive and negative predictive values were used to assess the performance of the convolutional neural network.

RESULTS

A total of 7,925 images from 72 patients were included. Our model had a sensitivity and specificity of 97.0% and 97.4%, respectively. The area under the curve was 1.00.

DISCUSSION

Our model was able to efficiently detect enteric protruding lesions. The development of AI tools may enhance the diagnostic capacity of deep enteroscopy techniques.

Subarachnoid and subdural haematoma after attempted spinal anaesthesia for caesarean section

We report a case of a previously healthy patient who developed a vertebral canal haematoma in the subarachnoid and subdural spaces after a spinal puncture for elective caesarean section. Vertebral canal haematomas are rare. There are different mechanisms for haematoma formation, but coagulation disturbances and trauma, most often due to needle punctures, are the most important. Vertebral canal haematoma may warrant emergent surgical decompression. In this case report we discuss vertebral canal haematomas, including possible mechanisms, clinical diagnosis, imaging modalities, methods for management and advice for patients. We consider the possible association between a vertebral canal haematoma and non-steroidal anti-inflammatory drugs, and draw attention to an existing black box warning for ketorolac. In this case, we explain why a conservative approach was chosen with a good outcome. We also report the effect of this complication on the patient experience.

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

<b><i>Introduction:</i></b> 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. <b><i>Case Presentation:</i></b> 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. <b><i>Discussion:</i></b> 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.

Wilson disease in Northern Portugal: a long-term follow-up study

Introduction

Wilson disease is an autosomal recessive disease of liver copper metabolism with predominant hepatic and neurological manifestations. Long-term data on the clinical follow-up and treatment efficacy are limited due to the low frequency of the disease. We evaluated a large cohort of Wilson disease patients from Northern Portugal during a 20-year follow-up period.

Methods

Twenty-four patients, diagnosed from 1975 to 2020 in a tertiary care center in Portugal, were retrospectively evaluated according to their clinical presentation, therapies and outcomes.

Results

Most of the patients were males (54%), with a median age at diagnosis of 19 years old (interquartile range 15–25). The main manifestations of Wilson disease were hepatic (71%) and neurological (25%). Family history was positive in 5 (21%) patients. Four patients (17%) presented with acute liver failure and fifteen (63%) individuals had cirrhosis at diagnosis. Penicillamine therapy was used by 11 (46%) patients, while trientine and zinc were given to 8 (33%) and 1 (4%) patient, respectively. Ten (42%) individuals underwent liver transplantation. The majority of patients (83%) had stable disease or improved outcomes during follow-up.

Conclusion

This is the largest cohort of adult patients with Wilson disease reported in Northern Portugal. We show that Wilson disease has favorable outcomes with long overall survival, assuming adherence to therapy and lack of other insults to their liver.

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.

Gastric ulcer with liver penetration

A 54-year-old male with previous history of chronic hepatitis C complained of postprandial epigastric discomfort and weight loss. Esophagogastroduodenoscopy revealed a large gastric ulcer on the lesser antral curvature, with biopsies showing granulation tissue and inflammatory activity without other significant changes. Despite therapy with double-dose proton pump inhibitor, the ulcer had not healed or diminished in size at reevaluation endoscopy eight weeks later. Biopsies were repeated and again only revealed granulation tissue and inflammation. The patient undergone partial gastrectomy where fibrotic adhesions to liver and peritoneum were noted. Remarkably, macroscopical analysis of the surgical specimen revealed an hepatic fragment adherent to the ulcer and histopathological examination was consistent with liver penetration. Liver penetration is a rare but important complication that must be considered in the differential diagnosis of medically refractory gastric ulcers, even in the absence of hepatic tissue in endoscopic biopsies.

Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia

Background and Objectives: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly angioectasia, are the most common etiology. Nevertheless, the diagnostic yield of DAE for the detection of these lesions is suboptimal. Deep learning algorithms have shown great potential for automatic detection of lesions in endoscopy. We aimed to develop an artificial intelligence (AI) model for the automatic detection of angioectasia DAE images. Materials and Methods: A convolutional neural network (CNN) was developed using DAE images. Each frame was labeled as normal/mucosa or angioectasia. The image dataset was split for the constitution of training and validation datasets. The latter was used for assessing the performance of the CNN. Results: A total of 72 DAE exams were included, and 6740 images were extracted (5345 of normal mucosa and 1395 of angioectasia). The model had a sensitivity of 88.5%, a specificity of 97.1% and an AUC of 0.988. The image processing speed was 6.4 ms/frame. Conclusions: The application of AI to DAE may have a significant impact on the management of patients with suspected mid-gastrointestinal bleeding.

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.

Transient splenic elastography predicts high-risk esophageal varices in patients with non-cirrhotic portal hypertension

BACKGROUND AND AIMS

Non-cirrhotic portal hypertension (NCPH) comprise a group of diseases that cause portal hypertension without cirrhosis, leading to a high risk of hemorrhage from esophageal varices. There are no non-invasive predictors of high-risk varices (HRV) described in the literature for NCPH. This study aimed to evaluate whether transient splenic elastography (TSE) can predict HRV in patients with NCPH.

METHODS

Prospective study of patients with NCPH who underwent a single timepoint evaluation with transient liver and spleen elastography, ultrasonography, upper endoscopy, and laboratory tests. The study was performed from January to September 2020. Patients were divided into two groups based on the presence of HRV. The relation between TSE, transient liver elastography (TLE), spleen size, and platelet count to the presence of HRV was evaluated.

RESULTS

Of 42 patients with NCPH, 50% (21/42) presented HRV. In univariate analysis, TSE (median, 58.4 vs. 28.3, p = 0.009) and spleen size (median, 17.5 vs. 14.5 cm, p = 0.013) were associated with HRV. No statistically significant relationship was found between the presence of HRV and platelet count or TLE. In multivariate analysis, TSE was the only variable related to HRV (OR 1.21, 95% CI 1.02-1.38). TSE had a good performance in predicting HRV in our population (AUROC 0.878; 95% CI 0.751-1000). TSE > 35.4 kPa presents 93.3% sensitivity, 60.0% specificity, and 90.9% negative predictive value.

CONCLUSION

In our population of patients with NCPH, TSE is useful in predicting HRV. TLE, spleen size, and platelet count were not related to HRV.

Artificial intelligence and capsule endoscopy: automatic detection of vascular lesions using a convolutional neural network

Background

Capsule endoscopy (CE) is the first line for evaluation of patients with obscure gastrointestinal bleeding. A wide range of small intestinal vascular lesions with different hemorrhagic potential are frequently found in these patients. Nevertheless, reading CE exams is time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence tools with high performance levels in image analysis. This study aimed to develop a CNN-based model for identification and differentiation of vascular lesions with distinct hemorrhagic potential in CE images.

Methods

The development of the CNN was based on a database of CE images. This database included images of normal small intestinal mucosa, red spots, and angiectasia/varices. The hemorrhagic risk was assessed by Saurin's classification. For CNN development, 11,588 images (9525 normal mucosa, 1026 red spots, and 1037 angiectasia/varices) were ultimately extracted. Two image datasets were created for CNN training and testing.

Results

The network was 91.8% sensitive and 95.9% specific for detection of vascular lesions, providing accurate predictions in 94.4% of cases. In particular, the CNN had a sensitivity and specificity of 97.1% and 95.3%, respectively, for detection of red spots. Detection of angiectasia/varices occurred with a sensitivity of 94.1% and a specificity of 95.1%. The CNN had a frame reading rate of 145 frames/sec.

Conclusions

The developed algorithm is the first CNN-based model to accurately detect and distinguish enteric vascular lesions with different hemorrhagic risk. CNN-assisted CE reading may improve the diagnosis of these lesions and overall CE efficiency.

A Predictive Model for Avoiding Follow-Up Paracentesis in Spontaneous Bacterial Peritonitis

Background

Recent studies suggest that follow-up paracentesis (FuP) in cases of spontaneous bacterial peritonitis (SBP) should only be performed if there is a clinical and/or analytic worsening. We aimed to evaluate which patients with SBP would benefit from the FuP.

Métodos

This retrospective study included consecutive patients diagnosed with SBP between January 2011 and June 2018 in our tertiary center. Clinical and analytical data were obtained at baseline and on the third day of antibiotic therapy. An adequate response on the third day of treatment was defined by a decrease of ≥25% in the neutrophil count of the ascitic fluid (AF).

Results

Ninety-six episodes of PBE in 75 patients (79% male sex, mean age 61 ± 11 years old) were included. At admission, a higher serum neutrophil count (p = 0.043), a lower level of serum total proteins (p = 0.040), a positive culture in AF (p < 0.001) and a previous diagnosis of diabetes mellitus (p = 0.035) were related to inadequate response (IR). At day 3, acute kidney injury (p = 0.023), C-reactive protein >100 mg/L (p < 0.001), the presence of fever (p = 0.047) and abdominal pain (p < 0.001) were also associated with IR. In multivariate analysis, the presence of respiratory insufficiency (OR = 16.403; 95% CI: 2.315-116.222; p = 0.005) and abdominal pain (OR = 10.381; 95% CI: 1.807-59.626; p = 0.009) at admission, serum white blood cell count >9 × 10⁹ (OR = 5.832; 95% CI: 1.275-26.669; p = 0.023), and CRP >100 mg/L (OR = 5.043; 95% CI: 1.267-20.076; p = 0.022) at day 3 of antibiotic therapy were predictors of IR. The predictive model presented good accuracy [AUROC of 0.893 (p < 0.001)] - a cutoff of 0.090 had a sensitivity, specificity, positive predictive value, and negative predictive value for IR of 97, 46, 83, and 77%, respectively.

Conclusions

The performance of FuP on day 3 after the beginning of empiric therapy should be individualized, according to clinical and analytic variables of this predictive model.

Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Small Bowel Blood Content Using a Convolutional Neural Network

Introduction

Capsule endoscopy has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule endoscopy images is time-consuming and prone to overlooking significant lesions, thus limiting its diagnostic yield. We aimed to create a deep learning algorithm for automatic detection of blood and hematic residues in the enteric lumen in capsule endoscopy exams.

Methods

A convolutional neural network was developed based on a total pool of 22,095 capsule endoscopy images (13,510 images containing luminal blood and 8,585 of normal mucosa or other findings). A training dataset comprising 80% of the total pool of images was defined. The performance of the network was compared to a consensus classification provided by 2 specialists in capsule endoscopy. Subsequently, we evaluated the performance of the network using an independent validation dataset (20% of total image pool), calculating its sensitivity, specificity, accuracy, and precision.

Results

Our convolutional neural network detected blood and hematic residues in the small bowel lumen with an accuracy and precision of 98.5 and 98.7%, respectively. The sensitivity and specificity were 98.6 and 98.9%, respectively. The analysis of the testing dataset was completed in 24 s (approximately 184 frames/s).

Discussion/Conclusion

We have developed an artificial intelligence tool capable of effectively detecting luminal blood. The development of these tools may enhance the diagnostic accuracy of capsule endoscopy when evaluating patients presenting with obscure small bowel bleeding.

Duodenal-type follicular lymphoma -a silent tumor

A 79-year-old male, with a past medical history of hypertension, dyslipidemia and type 2 diabetes, underwent routine esophagogastroduodenoscopy and colonoscopy. The patient was asymptomatic, with unremarkable blood tests. Upper gastrointestinal endoscopy showed multiple whitish nodular lesions in the first part of the duodenum.

Artificial intelligence and colon capsule endoscopy: development of an automated diagnostic system of protruding lesions in colon capsule endoscopy

BACKGROUND

Colon capsule endoscopy (CCE) is a minimally invasive alternative for patients unwilling to undergo conventional colonoscopy, or for whom the latter exam is contraindicated. This is particularly important in the setting of colorectal cancer screening. Nevertheless, these exams produce large numbers of images, and reading them is a monotonous and time-consuming task, with the risk of overlooking important lesions. The development of automated tools based on artificial intelligence (AI) technology may improve some of the drawbacks of this diagnostic instrument.

METHODS

A database of CCE images was used for development of a Convolutional Neural Network (CNN) model. This database included anonymized images of patients with protruding lesions in the colon or patients with normal colonic mucosa or with other pathologic findings. A total of 3,387,259 frames from 24 CCE exams were retrospectively reviewed. For CNN development, 3640 images (860 protruding lesions and 2780 with normal mucosa or other findings) were ultimately extracted. Training and validation datasets were constructed for the development and testing of the CNN.

RESULTS

The CNN detected protruding lesions with a sensitivity, specificity, positive and negative predictive values of 90.7, 92.6, 79.2 and 96.9%, respectively. The area under the receiver operating characteristic curve for detection of protruding lesions was 0.97.

CONCLUSIONS

The deep learning algorithm we developed is capable of accurately detecting protruding lesions. The application of AI technology to CCE may increase its diagnostic accuracy and acceptance for screening of colorectal neoplasia.

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.

Lewis score: a useful tool for diagnosis and prognosis in Crohn's disease

BACKGROUND

videocapsule endoscopy (VCE) is currently the most sensitive diagnostic tool to detect early small bowel inflammation. A Lewis score (LS) of ≥ 135 as the cutoff value for the presence of significant inflammatory activity in patients undergoing VCE for suspected Crohn's disease (CD) has been suggested as a useful tool for the diagnosis of CD. The aim of this study was to evaluate the diagnostic and prognostic accuracy of the LS in patients with suspected CD undergoing VCE.

METHODS

a retrospective single-center study was performed that included patients who underwent VCE for suspected CD between January 2010 and December 2015. Inflammatory activity was assessed with the LS. Patients were grouped according to the criteria of the International Conference on Capsule Endoscopy (ICCE) for the definition of suspected CD; group 1: patients not fulfilling ICCE and group 2: patients with ≥ 2 ICCE criteria.

RESULTS

one hundred and ninety-one patients were included, 61% were female and the mean age was 39 ± 14 years. VCE detected significant inflammatory activity (LS ≥ 135) in 81 patients (42%); 24 patients from group 1 (32%) and 57 patients from group 2 (50%) (p = 0.014). During a mean follow-up period of 41 ± 21 months (12-79), a CD diagnosis was determined in 60 patients (31%); 55 patients with LS ≥ 135 (92%) and five patients with LS < 135 (5%) (p < 0.001). The LS showed a good diagnostic accuracy with an AUROC of 0.93 (p < 0.001). During the first year after diagnosis, there was a significant association between a higher LS and the need for immunomodulatory therapy, biological therapy, bowel resection surgery or hospital admission due to a CD flare-up.

CONCLUSIONS

the LS (cutoff ≥ 135) is very useful in the diagnosis of CD in patients undergoing VCE. Moreover, higher values of this score was associated with prognostic variables.

Capsule enteroscopy versus scintigraphy for the diagnosis of obscure gastrointestinal bleeding

BACKGROUND

Capsule enteroscopy (CE) and 99mTc Red blood cell (RBC) scintigraphy are frequently used tests in the investigation of obscure gastrointestinal bleeding (OGIB). There is a scarcity of data comparing both diagnostic modalities. This study aims to assess the performance of CE and scintigraphy for the diagnosis of OGIB.

METHODS

Patients who underwent CE and scintigraphy for OGIB were selected and analyzed retrospectively. The hemorrhagic potential of CE findings was rated using Saurin's classification. The concordance between both diagnostic techniques for bleeding detection and localization was analyzed.

RESULTS

Eighty-five patients (62% female), with a median age of 63 years, were included. Capsule enteroscopy identified 37 patients (43%) with high hemorrhagic potential (P2) lesions. Most scintigraphy exams were positive for gastrointestinal bleeding (82%). No concordance was found between the detection of lesions with hemorrhagic potential in CE and scintigraphy (kappa <0). The distribution of P0, P1, and P2 findings was similar in patients with positive or negative scintigraphy (p=0.526). There was no agreement regarding the location of P2 findings in CE and the bleeding detected in the scintigraphy (kappa <0). Patients with P2 lesions had significantly lower median levels of hemoglobin (p=0.002) at presentation. No significant difference was found in hemoglobin values between patients with positive or negative scintigraphy (p=0.058).

CONCLUSION

Significant diagnostic discrepancy was observed between CE and scintigraphy. The findings of CE correlated better with hemoglobin values at presentation than the scintigraphy results. Therefore, scintigraphy didn't appear to be useful in the diagnostic workup of OGIB.  .

Artificial intelligence and capsule endoscopy: unravelling the future

The applicability of artificial intelligence (AI) in gastroenterology is a hot topic because of its disruptive nature. Capsule endoscopy plays an important role in several areas of digestive pathology, namely in the investigation of obscure hemorrhagic lesions and the management of inflammatory bowel disease. Therefore, there is growing interest in the use of AI in capsule endoscopy. Several studies have demonstrated the enormous potential of using convolutional neural networks in various areas of capsule endoscopy. The exponential development of the usefulness of AI in capsule endoscopy requires consideration of its medium- and long-term impact on clinical practice. Indeed, the advent of deep learning in the field of capsule endoscopy, with its evolutionary character, could lead to a paradigm shift in clinical activity in this setting. In this review, we aim to illustrate the state of the art of AI in the field of capsule endoscopy.

Treatment response in primary biliary cholangitis: The role of autoimmune hepatitis features

INTRODUCTION AND AIM

Primary biliary cholangitis is a rare disease with scarce epidemiological data in Southern Europe. The authors aimed to evaluate treatment response in a cohort of patients.

MATERIALS AND METHODS

This retrospective observational single-centre study included patients with diagnostic criteria of primary biliary cholangitis. Data on disease presentation, laboratory results, treatment and clinical endpoints were collected and analyzed.

RESULTS

Fifty-three patients were included, 89% women, with mean age of 62±15 years at diagnosis. The majority was asymptomatic (49%), tested positive for antimitochondrial antibodies (96%) and had increased alkaline phosphatase (median=214U/L). 75% of the patients had liver histology and the majority were in Ludwig's stage I (42%). Autoimmune hepatitis (AIH) features were found in seven patients (13%). All were treated with ursodeoxycholic acid (UDCA) and 56% achieved biochemical response at one year; patients with AIH features exhibited steeper decreases in alkaline phosphatase (p=0.007) and reached the endpoint of 40% decrease in alkaline phosphatase more frequently (p=0.017).

CONCLUSION

In conclusion a significant proportion of patients failed to achieve an adequate response to UDCA treatment. The response rate of patients with AIH features was better, which could be related to a different phenotype or to the potential impact of immunosuppressive agents.

Small Bowel Enteroscopy - A Joint Clinical Guideline from the Spanish and Portuguese Small Bowel Study Groups

The present evidence-based guidelines are focused on the use of device-assisted enteroscopy in the management of small-bowel diseases. A panel of experts selected by the Spanish and Portuguese small bowel study groups reviewed the available evidence focusing on the main indications of this technique, its role in the management algorithm of each indication and on its diagnostic and therapeutic yields. A set of recommendations were issued accordingly.

Evaluation of Small-Bowel Patency in Crohn's Disease: Prospective Study with a Patency Capsule and Computed Tomography

Background and Purpose

Patency capsule (PC) examination is usually performed - previously to capsule endoscopy - to evaluate small-bowel patency in patients with established Crohn's disease (CD). The reported PC retention rate is significantly higher than expected. Our aims were to assess small-bowel patency, to determine the precise location of the retained PC in patients with CD, and to determine the false positive rate of evaluation with a radiofrequency identification tag (RFIT) scanner.

Methods

This is a prospective single-center study including CD patients with clinical indication for small-bowel capsule endoscopy. PillCam® PC examination was performed on all patients to assess small-bowel patency. On all patients with a positive identification of the PC using an RFIT scanner, 30 h after ingestion, an abdominal CT was performed in order to identify its precise location.

Results

Fifty-four patients were included. The PC retention rate, according to evaluation with the RFIT scanner, was 20% (in 11 patients) 30 h after ingestion. These patients were then submitted to abdominal CT, which revealed that there was small-bowel retention in 5 cases (9%). Higher CRP levels, penetrating disease, and a history of abdominal surgery were associated with an increased risk of PC retention (p = 0.007, p = 0.011, and p = 0.033, respectively). On multivariate analysis, there was an independent association between small-bowel PC retention and CRP levels >5 mg/dL (OR = 15.5; p = 0.03).

Discussion

The small-bowel PC retention rate (9%) was considerably lower than those found in previous reports. Our results show that, with this protocol, the false-positive cases of RFIT scans or plain abdominal X-rays may be avoided. This may contribute to more extensive application of capsule endoscopy without the risk of small-bowel retention.

Duodenal Vascular Spider's Web: Radiofrequency Ablation to the Rescue!

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A Rare Cause of Acute Liver Failure: Diffuse Liver Metastization of Merkel Cell Carcinoma

The authors describe a case of a 76-year-old male with a medical history of Merkel cell carcinoma (MCC) of the right lower eyelid. He was admitted to the emergency department due to abdominal pain in the right hypochondrium, nauseas, asthenia, and choluria with 3 days of evolution. Biochemical liver workup revealed a cytocholestase pattern as well as a prolonged prothrombin time. After admission, the patient developed hepatic encephalopathy, and a clinical and analytical worsening was observed. Abdominal ultrasound showed a reduction in the caliber of the hepatic veins, in apparent relation to a parenchymal compression. Liver biopsy was performed and showed an extensive infiltration of the hepatic parenchyma by a solid neoplasm, which, upon immunohistochemical study, was compatible with a diffuse metastization of a MCC. We report this clinical case due to its rarity of presentation and to show the important role of liver biopsy in cases of acute hepatitis.

How should we select suspected Crohn's disease patients for capsule enteroscopy?

Background: In suspected Crohn's disease (CD), non-diagnostic ileocolonoscopies are often followed by small bowel capsule endoscopy (SBCE). Adequate pre-selection of patients for SBCE is a key to optimize allocation of resources. We aimed to establish a rational approach for the CD diagnostic workflow, based on biochemical profile of patients with suspected CD, targeting an optimization of patients' selection for SBCE. Methods: Multicenter cohort study includes consecutive patients with suspected undergoing SBCE after non-diagnostic ileocolonoscopy. Minimum follow-up period after the capsule enteroscopy was six months. The outcome was confirmation of CD diagnosis. Univariate analysis and logistic regression were performed. Results: In included 220 patients, 62.3% of women were with a mean age of 41 years [26-54]. A confirmed diagnosis of CD was established in 98 patients (44.5%). The initial univariate analysis identified variables above the threshold of marginal statistical association toward CD diagnosis (p < .15). The regression model identified high CRP levels (OR 1.028 p = .128) and low serum Iron (OR 0.990 p = .025) as the independent variables with consistent correlation with CD diagnosis. Those two variables present a suitable discriminative power (AUC = 0.669, p < .001) for the diagnosis of CD. Conclusion: In suspected CD, low serum iron and elevated CRP had a statistically significant association with CD diagnosis, being helpful to identify patients with higher CD probability before SBCE. However, the lack of a proper validation of the model leads us to currently recommend SBCE to all patients with suspected CD and negative ileocolonoscopy, as no specific biochemical profile can be used to confidently exclude small bowel CD.

Rheumatologic manifestations of hepatic diseases

The course of hepatic diseases may be complicated by a multitude of rheumatologic manifestations, which can complicate the diagnostic approach and alter the natural history of primary liver disease, sometimes worsening prognosis due to associated multiple organ dysfunction. These manifestations can occur in association with a multitude of liver diseases, including viral hepatitis, autoimmune hepatitis, alcoholic liver disease, nonalcoholic fatty liver disease, hemochromatosis, or Wilson’s disease. It is necessary not only for rheumatologists, but also for other clinicians, to be aware that these atypical manifestations may reflect an undiagnosed hepatic disease. On the other hand, it is crucial that, in a patient with known hepatic disease presenting with rheumatologic symptoms, an accurate distinction be made between the rheumatologic manifestations of hepatic disease and primary rheumatologic disease, since the treatment is often different. This review aims to summarize the current evidence regarding rheumatologic manifestations of hepatic diseases, how to distinguish them from primary rheumatologic disorders, and how to provide adequate management.