Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors-A Multicentric Study.

High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA images. Our aim was to develop a deep learning system for the automatic detection and differentiation of HSIL versus LSIL using HRA images from both conventional and digital proctoscopes. A convolutional neural network (CNN) was developed based on 151 HRA exams performed at two volume centers using conventional and digital HRA systems. A total of 57,822 images were included, 28,874 images containing HSIL and 28,948 LSIL. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images acetic acid and lugol iodine staining and after treatment of the anal canal. The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 94.6%. The algorithm had an overall sensitivity and specificity of 93.6% and 95.7%, respectively (AUC 0.97). For staining with acetic acid, HSIL was differentiated from LSIL with an overall accuracy of 96.4%, while for lugol and after therapeutic manipulation, these values were 96.6% and 99.3%, respectively. The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, and this system was shown to perform adequately across conventional and digital HRA interfaces.

Deep learning and minimally invasive inflammatory activity assessment: a proof-of-concept study for development and score correlation of a panendoscopy convolutional network.

Background: Capsule endoscopy (CE) is a valuable tool for assessing inflammation in patients with Crohn's disease (CD). The current standard for evaluating inflammation are validated scores (and clinical laboratory values) like Lewis score (LS), Capsule Endoscopy Crohn's Disease Activity Index (CECDAI), and ELIAKIM. Recent advances in artificial intelligence (AI) have made it possible to automatically select the most relevant frames in CE. Objectives: In this proof-of-concept study, our objective was to develop an automated scoring system using CE images to objectively grade inflammation. Design: Pan-enteric CE videos (PillCam Crohn's) performed in CD patients between 09/2020 and 01/2023 were retrospectively reviewed and LS, CECDAI, and ELIAKIM scores were calculated. Methods: We developed a convolutional neural network-based automated score consisting of the percentage of positive frames selected by the algorithm (for small bowel and colon separately). We correlated clinical data and the validated scores with the artificial intelligence-generated score (AIS). Results: A total of 61 patients were included. The median LS was 225 (0-6006), CECDAI was 6 (0-33), ELIAKIM was 4 (0-38), and SB_AIS was 0.5659 (0-29.45). We found a strong correlation between SB_AIS and LS, CECDAI, and ELIAKIM scores (Spearman's r = 0.751, r = 0.707, r = 0.655, p = 0.001). We found a strong correlation between LS and ELIAKIM (r = 0.768, p = 0.001) and a very strong correlation between CECDAI and LS (r = 0.854, p = 0.001) and CECDAI and ELIAKIM scores (r = 0.827, p = 0.001). Conclusion: Our study showed that the AI-generated score had a strong correlation with validated scores indicating that it could serve as an objective and efficient method for evaluating inflammation in CD patients. As a preliminary study, our findings provide a promising basis for future refining of a CE score that may accurately correlate with prognostic factors and aid in the management and treatment of CD patients.

Artificial intelligence in Crohn's disease: the development of an automated score for disease activity evaluation This study introduces an innovative AI-based approach to evaluate Crohn's Disease. The AI system automatically analyzes images from capsule endoscopy, focusing on finding ulcers and erosions to measure disease activity. The research reveals a robust correlation between the AI-generated score assessing inflammation in the small bowel and traditional clinical scores. This suggests that the AI solution could be a quicker and more consistent way to evaluate Crohn's Disease, speeding up the evaluation process and reducing manual scoring variability. While promising, the study acknowledges limitations and emphasizes the need for further validation with larger groups of patients. Overall, it represents a crucial step toward integrating AI into gastroenterology, offering a glimpse into a future of more objective and personalized Crohn's Disease evaluation.

Deep learning and capsule endoscopy: Automatic multi-brand and multi-device panendoscopic detection of vascular lesions.

Background and study aims Capsule endoscopy (CE) is commonly used as the initial exam for suspected mid-gastrointestinal bleeding after normal upper and lower endoscopy. Although the assessment of the small bowel is the primary focus of CE, detecting upstream or downstream vascular lesions may also be clinically significant. This study aimed to develop and test a convolutional neural network (CNN)-based model for panendoscopic automatic detection of vascular lesions during CE. Patients and methods A multicentric AI model development study was based on 1022 CE exams. Our group used 34655 frames from seven types of CE devices, of which 11091 were considered to have vascular lesions (angiectasia or varices) after triple validation. We divided data into a training and a validation set, and the latter was used to evaluate the model's performance. At the time of division, all frames from a given patient were assigned to the same dataset. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the precision-recall curve (AUC-PR). Results Sensitivity and specificity were 86.4% and 98.3%, respectively. PPV was 95.2%, while the NPV was 95.0%. Overall accuracy was 95.0%. The AUC-PR value was 0.96. The CNN processed 115 frames per second. Conclusions This is the first proof-of-concept artificial intelligence deep learning model developed for pan-endoscopic automatic detection of vascular lesions during CE. The diagnostic performance of this CNN in multi-brand devices addresses an essential issue of technological interoperability, allowing it to be replicated in multiple technological settings.

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.

Leg dystrophic calcification as a consequence of chronic diabetic foot infection: a case report.

Foot ulceration and infection is associated with a substantial increase in morbidity and mortality in patients with diabetes. We present a clinical case of recurrent diabetic foot infection with an atypical clinical evolution. A 58-year-old male patient with type 1 diabetes and a history of bilateral Charcot foot neuroarthropathy was followed at our Diabetic Foot Clinic for an unhealed plantar foot ulcer for >1.5 years with recurrent episodes of infection. He was admitted to hospital due to foot ulcer reinfection with sepsis and ipsilateral lower limb cellulitis. The foot infection was found to be associated with an underlying abscess in the anterior compartment of the leg, with a cutaneous fistulous course with extensive alterations of an inflammatory nature. Exudate from the lesion was drained and tissue biopsied, revealing Serratia marcescens and Klebsiella oxytoca with dystrophic calcification (DC). Surgical excision of dystrophic tissue with debridement of the fistulous tracts was performed. The excised material corroborated the presence of fibroadipose connective tissue with marked DC, as well as areas of mixed inflammation compatible with a chronic infectious aetiology. Targeted long-term antibiotic therapy was implemented, for a total of six weeks, with a favourable clinical evolution and complete closure of the lesion at the final follow-up. DC results from calcium deposition in degenerated tissues without evidence of systemic mineral imbalance and is a potential cause of non-healing ulcers. Few cases of DC have been reported in diabetic foot patients and its treatment remains challenging and controversial. A longer follow-up period is necessary to verify the effectiveness of our approach.

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.

Early Diagnosis of 46,XX Testicular Difference of Sexual Development: Unusual Presentation with Increased Nuchal Translucency.

INTRODUCTION: 46,XX testicular disorder of sexual development (DSD) may present prenatally as a mismatch between phenotype and karyotype. Enlarged nuchal translucency is an abnormal sign of many disorders. We present a first trimester fetus with increased nuchal translucency that was later determined to be a 46,XX testicular DSD. CASE PRESENTATION: A first-trimester pregnancy ultrasound revealed enlarged nuchal translucency. Chorionic villous sampling documented a 46,XX karyotype. Subsequent ultrasounds identified male external genitalia. FISH analysis documented a SRY gene translocation. At birth, the infant had normal male internal and external genitalia. CONCLUSIONS: 46,XX testicular DSD may present in the first trimester with an enlarged nuchal translucency.

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.

Shedding Light on Multiple Symmetric Lipomatosis: An Overlooked Syndrome in the Evaluation of Obesity.

BACKGROUND Multiple symmetric lipomatosis (MSL), also known as Launois-Bensaude syndrome, is a rare syndrome that is frequently misinterpreted as simple obesity. It has seldom been reported. Both conditions can coexist; however, unlike common obesity, in which total body fat is well distributed, patients affected by MSL present with symmetrical, painless fat masses that predominantly appear in the face, neck, occipital region and supraclavicular fossa. Launois-Bensaude syndrome's etiology is still poorly understood but an association with alcohol use has been documented. CASE REPORT A 49-year-old woman was referred to our department due to class II obesity (body mass index of 39.8 kg/m²). She had a history of arterial hypertension and daily wine consumption (2 glasses on average). She complained of weight gain after menopause and she reported having tried multiple times to lose weight by herself without success. On physical examination, the patient had an unusual pattern of fat distribution: exuberant symmetrical fat masses that were localized in her arms and thighs and spared her face, neck, forearms, and lower legs. She claimed that these masses had a rapid onset and then stabilized. The clinical history and the patient's phenotype were compatible with a non-classic type of Launois-Bensaude syndrome. CONCLUSIONS We concluded that our patient's condition encompasses more than just simple obesity; it involved a distinct form of adiposopathy that led to a completely different clinical approach. A detailed physical examination seems to be key for clinical suspicion of this rare syndrome, which can be a true pitfall in obesity evaluation.

Predicting Factors of Clinical Outcomes in Patients Hospitalized after Esophageal Foreign Body or Caustic Injuries: The Experience of a Tertiary Center.

Ingestion of foreign bodies (IFB) and ingestion of caustic agents are frequent non-hemorrhagic causes of endoscopic urgencies, with the potential for severe complications. This study aimed to evaluate the predicting factors of the clinical outcomes of patients hospitalized as a result of IFB or ingestion of caustics (IC). This was a retrospective single-center study of patients admitted for IFB or IC between 2000 and 2019 at a tertiary center. Demographic and clinical data, as well as preliminary exams, were evaluated. Also, variables of the clinical outcomes, including the length of stay (LS) and other inpatient complications, were assessed. Sixty-six patients were included (44 IFB and 22 IC). The median LS was 7 days, with no differences between the groups (p = 0.07). The values of C-reactive protein (CRP) upon admission correlated with the LS in the IFB group (p < 0.01) but not with that of those admitted after IC. In the IFB patients, a diagnosis of perforation on both an endoscopy (p = 0.02) and CT scan (p < 0.01) was correlated with the LS. The Zargar classification was not correlated with the LS in the IC patients (p = 0.36). However, it was correlated with antibiotics, nosocomial pneumonia and an increased need for intensive care treatment. CT assessment of the severity of the caustic lesions did not correlate with the LS. In patients admitted for IFB, CRP values may help stratify the probability of complications. In patients admitted due to IC, the Zargar classification may help to predict inpatient complications, but it does not correlate with the LS.

Deep Learning for Automatic Diagnosis and Morphologic Characterization of Malignant Biliary Strictures Using Digital Cholangioscopy: A Multicentric Study.

Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures.

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.

Gender-Affirming Hormone Therapy: Physical and Sociopsychological Effects, Impact and Satisfaction.

Background Gender dysphoria treatment includes gender-affirming hormone therapy (GAHT). Studies are still lacking on how to characterize its effects and impact on transgender people's lives more effectively. Aim To study the physical and psychological effects of GAHT on transgender individuals, assess its impact on their lives, and rate their overall satisfaction. Methods Participants (n = 114; ages 18-62 years; median age 24.0 (21.0 - 33.0) years) included transgender adults residing in Portugal who were undergoing or had undergone hormonal therapy for at least one uninterrupted year. Participants completed an original questionnaire. For most items, an ordinal Likert-style scale ranging from 0 (worst result) to 6 (best result) was used. Descriptive statistics and non-parametric tests, including Pearson's chi-squared test, Wilcoxon signed-rank test, and Mann-Whitney U test were used to analyze categorical and continuous variables, with a significance level set at 0.05. Outcomes The outcomes included desired physical changes rating (perception and satisfaction with changes); side effects of GAHT; the sociopsychological impact of GAHT (on self-esteem, body image, psychological wellbeing, social and family relations); overall satisfaction (with treatment results and medical follow-up). Results The changes classified as the most perceptible in those undergoing masculinizing treatment (Group M) were amenorrhea (6 (5.0-6.0) points) and clitoris enlargement (6 (5.0-6.0) points). These were also the ones rated as the most satisfactory (6 (6.0-6.0) points for amenorrhea and 6 (4.0-6.0) points for clitoris enlargement). On those undergoing feminizing therapy (Group F), the alteration voted as the most perceptible was sperm production decrease (6 (2.0-6.0) points), and the ones classified as the most satisfactory were sperm production decrease (6 (4.0-6.0) points) and spontaneous erections decrease (6 (5.0-6.0) points). Side effects were reported by 89.7% of Group M (mood swings were the most common) and 96.3% of Group F (decreased libido was the most frequent). The sociopsychological impact of hormonal treatment was significantly positive in all analyzed variables (p<0.001). Overall satisfaction with treatment results and medical follow-up were rated with 5 points and 4.5 points, respectively. Clinical implications This study provides clinicians with more evidence that GAHT may improve the physical, psychological and social health of transgender people seeking medical transition. Strengths and limitations The strengths of the current study include a high participant count relative to the target population, the acquisition of data on previously unexplored variables, and the significance of being one of the few investigations of its kind conducted in Portugal. However, the study has limitations, including differences in participant characteristics, a small sample size for some variables, potential bias due to the retrospective nature of the study, individualized treatment regimens, and the inclusion of participants from different countries, which limit the generalization of the results. Conclusions This study provides further evidence that GAHT is effective, and that its physical effects are satisfactory while resulting in mostly non-severe nor life-threatening side effects. GAHT is an important therapy in gender dysphoria and has consistent results in improving numerous sociopsychological variables.

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.

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.

Translation, Cultural Adaptation and Validation of "PROMIS GI - Disrupted Swallowing" Scale for the Portuguese Language.

INTRODUCTION: Dysphagia is a prevalent condition (20%), and occurs more frequently in women and in older people. It negatively impacts innumerous aspects of patient's personal and professional lives. Patient-reported outcomes allow patients to directly quantify their experience regarding dysphagia and evaluate its true impact on quality of life. Among the scales available, Patient-Reported Outcomes Measurement Information System Gastrointestinal (PROMIS GI) Disrupted Swallowing stands out because it is a robust instrument that can be applied regardless of the type and etiology of dysphagia. The aim of this study was to translate, culturally adapt and validate PROMIS GI Disrupted Swallowing scale for the Portuguese-speaking population. MATERIAL AND METHODS: Firstly, the seven items of the scale were translated and transculturally reviewed following the systematic method proposed by the Functional Assessment of Chronic Illness Therapy (FACIT). Afterwards, the pre-test version of the questionnaire was administered to a convenience sample (n = 6) for semantic evaluation, with the aim of detection and subsequent correction of possible problems in the translation. The final translated and certified version of the scale was administered to 200 voluntary adult participants (n = 123 healthy; n = 77 dysphagia) in Portugal, for evaluation of reliability and validity. RESULTS: The Portuguese version of PROMIS GI Disrupted Swallowing presented acceptable internal consistency (coefficient of Cronbach's α of 0.919) and adequate test-retest reliability (intraclass correlation coefficient of 0.941). The translated version of the scale revealed a strong correlation with both Eckardt score (p < 0.001; ρ = 0.782) and the quality-of-life questionnaire EuroQol-5D (p < 0.001; ρ = -0.551), demonstrating evidence of convergent validity. CONCLUSION: The Portuguese version of PROMIS GI Disrupted Swallowing scale presented conceptual, semantic, cultural and measurement equivalence relatively to the original items. The results attained demonstrated that the translation of this scale to Portuguese is reliable and valid for use both in clinical practice and for research purposes.

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.

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.

Inappropriate gestational weight gain impact on maternofetal outcomes in gestational diabetes.

OBJECTIVE: To evaluate the association between the dimension of deviation from appropriate gestational weight gain (GWG) and adverse maternofetal outcomes in women with gestational diabetes mellitus (GDM). METHODS: We performed a multicentric retrospective study based on the Portuguese GDM Database. Women were classified as within GWG, insufficient (IGWG) or excessive (EGWG) than the Institute of Medicine recommendations. EGWG and IGWG were calculated for each prepregnancy BMI category. Large-for-gestational-age (LGA) and macrosomia were defined as a birthweight more than the 90th percentile for the gestational age and newborn weight greater than 4000 g, respectively. Logistic regression models (adjusted odds ratio [aOR] plus 95% confidence interval [95%CI]) were derived to evaluate the association between EGWG or IGWG and adverse maternofetal outcomes. RESULTS: A total of 18961 pregnant women were included: 39.7% with IGWG and 27.8% with EGWG. An EGWG over 3 kg was associated with a higher risk of LGA infants (aOR 1.95, 95%CI 1.17-3.26) and macrosomia (aOR 2.01, 95%CI 1.23-3.27) in prepregnancy normal weight women. An EGWG greater than 4 kg was associated with a higher risk of LGA infants (aOR 1.67, 95%CI 1.23-2.23) and macrosomia (aOR 1.90, 95%CI 1.38-2.61) in obese women. In overweight women, an EGWG above 3.5 kg was associated with a higher risk of LGA infants (aOR 1.65, 95%CI 1.16-2.34), macrosomia (aOR 1.85, 95%CI 1.30-2.64), preeclampsia (aOR 2.40, 95%CI 1.45-3.98) and pregnancy-induced hypertension (aOR 2.21, 95%CI 1.52-3.21). An IGWG below -3.1 kg or -3kg was associated with a higher risk of small-for-gestational-age [SGA] infants in women with normal (OR 1.40, 95%CI 1.03-1.90) and underweight (OR 2.29, 95%CI 1.09-4.80), respectively. CONCLUSIONS: Inappropriate gestational weight gain seems to be associated with an increased risk for adverse maternofetal outcomes, regardless of prepregnancy BMI. Beyond glycemic control, weight management in women with GDM must be a focus of special attention to prevent adverse pregnancy outcomes.KEY MESSAGESThe dimension of deviation from appropriate gestational weight gain was associated with an increased risk for adverse maternofetal outcomes among women with gestational diabetes.Weight management must be a focus of special attention in women with gestational diabetes to prevent adverse pregnancy outcomes.