Challenges in Extubation: A Case of a Non-removable Tracheal Tube Following Surgery.

This report details a challenging case of difficult extubation due to a lodged tracheal tube following surgery, presenting an unexpected and complex clinical situation. An inspection of the airway using videolaryngoscopy revealed an over-inflated cuff beneath the vocal cords. Initial efforts to deflate the cuff with various methods were unsuccessful. The situation was ultimately resolved through the intervention of an otolaryngology surgeon. This case not only reviews various mechanisms of difficult endotracheal tube removal reported in the literature, but also underscores the potential for serious complications and highlights the critical role of multidisciplinary collaboration in managing extubation challenges. Additionally, our manuscript discusses alternative strategies that can be employed in scenarios where an otolaryngology surgeon is not available, offering practical guidance for anesthesiologists confronted with similar situations.

From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?

The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.

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 cancer (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 (HSIL) versus low-grade (LSIL) squamous intraepithelial lesions in HRA images in different subsets of patients (non-stained, acetic acid, lugol, and after manipulation). METHODS: A convolutional neural network (CNN) was developed to detect and differentiate high and low-grade anal squamous intraepithelial lesions based on 27,770 images from 103 HRA exams 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 (AUC) were calculated. RESULTS: The CNN 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 HSIL vs LSIL varied between 91.5% (post-manipulation) and 100% (lugol) for the categories at subanalysis. The AUC 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 as real-time AI models during HRA exams can help guide local treatment or detect relapsing disease.

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

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

Effective Postoperative Pain Management in Thoracic Outlet Syndrome Surgery: The Role of the Erector Spinae Plane Block.

Thoracic outlet syndrome (TOS) often necessitates surgical intervention to alleviate neurovascular bundle compression, which can result in severe postoperative pain. The myriad of surgical techniques available for TOS treatment, the intricate involvement of diverse sensory pathways, and the limited literature on effective analgesic methods for these specific cases underscore the need for successful approaches. This report introduces an efficacious multimodal analgesic strategy that incorporates the erector spinae plane (ESP) block to enhance postoperative pain management after a supraclavicular surgical approach. By combining this fascial block with a comprehensive rationale for its implementation, this case offers valuable insights into improving the postoperative care of TOS patients, ultimately aiming to enhance their comfort and recovery.

The Future of Minimally Invasive Capsule Panendoscopy: Robotic Precision, Wireless Imaging and AI-Driven Insights.

In the early 2000s, the introduction of single-camera wireless capsule endoscopy (CE) redefined small bowel study. Progress continued with the development of double-camera devices, first for the colon and rectum, and then, for panenteric assessment. Advancements continued with magnetic capsule endoscopy (MCE), particularly when assisted by a robotic arm, designed to enhance gastric evaluation. Indeed, as CE provides full visualization of the entire gastrointestinal (GI) tract, a minimally invasive capsule panendoscopy (CPE) could be a feasible alternative, despite its time-consuming nature and learning curve, assuming appropriate bowel cleansing has been carried out. Recent progress in artificial intelligence (AI), particularly in the development of convolutional neural networks (CNN) for CE auxiliary reading (detecting and diagnosing), may provide the missing link in fulfilling the goal of establishing the use of panendoscopy, although prospective studies are still needed to validate these models in actual clinical scenarios. Recent CE advancements will be discussed, focusing on the current evidence on CNN developments, and their real-life implementation potential and associated ethical challenges.

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.

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.

Ultrasound-Guided Electroacupuncture for Thoracic Myofascial Pain Syndrome: A Case Report.

Myofascial pain syndrome is a painful condition characterized by trigger points in muscles that can be treated effectively with acupuncture. While cross-fiber palpation can help localize trigger points, needle accuracy may be limited and accidental puncture of delicate structures, such as the lung, is a risk, as evidenced by reports of pneumothorax after acupuncture. Ultrasound imaging can help in reducing the risk of iatrogenic pneumothorax from needling, but there is a paucity of papers describing the use of ultrasound imaging during acupuncture. We present a report on electroacupuncture treatment for myofascial pain syndrome using real-time ultrasound guidance, aimed at avoiding accidental puncture of the pleura when targeting deep muscle layers in the thoracic region.

Using a Kirschner Wire as a Stylet for the Management of a Difficult Neonatal Airway: A Case Report.

We report the successful management of a difficult airway in an extremely low birth weight neonate (700 g) using a Kirschner wire as a substitute for an endotracheal tube stylet. Several intubation attempts were unsuccessful because of the difficulty in guiding a very small and malleable tube under the epiglottis. This study highlights that every maternity hospital should be prepared to manage airways in unexpected extremely low birth weight neonates. Appropriate size equipment and protocols should be readily available.

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.

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.

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.

Development of a Model to Predict Liver Decompensation prior to Transarterial Chemoembolization Refractoriness in Patients with Intermediate-Stage Hepatocellular Carcinoma.

Introduction: Transarterial chemoembolization (TACE) is the first-line treatment for patients with intermediate-stage hepatocellular carcinoma (HCC). For patients without an adequate response, current finding suggests that treatment with molecular target agents, approved for advanced stage, might present benefits. However, this requires a preserved liver function. This study aims to evaluate possible predictors of early deterioration of hepatic reserve, prior to TACE refractoriness, in a cohort of patients treated with TACE. Methods: Retrospective analysis of 99 patients with Child-Pugh class A and intermediate-stage HCC who underwent TACE as the first-line treatment. All patients were submitted to a biochemical and medical evaluation prior to initial TACE and every month afterward. Response to initial TACE was evaluated at 1 month. The time to Child-Pugh class deterioration before TACE refractoriness was assessed. Results: Ninety-nine patients were included. Objective response rate (ORR) to initial TACE was assessed as present in 59 (63.4%) and as absent in 34 (36.6%) patients. Liver decompensated before TACE refractoriness in 51 (51.5%) patients, and the median time to liver decompensation was 14 (IQR 8-20) months after first TACE. In multivariate analysis, beyond up-to-7 criteria (HR 2.4, p = 0.031), albumin <35 mg/dL (HR 3.5, p < 0.001) and absence of ORR (HR 2.4, p = 0.020) were associated with decreased overall survival free of liver decompensation. Moreover, beyond up-to-7 criteria, albumin <35 mg/dL and absence of ORR associated negatively with 6-month survival free of liver decompensation. Our model created using those variables was able to predict liver decompensation at 6 months with an AUROC of 0.701 (p = 0.02). Conclusions: The absence of ORR after initial TACE, beyond up-to-7 criteria and albumin <35 mg/dL, was a predictive factor for early liver decompensation before TACE refractoriness in our population. Such patients might benefit from treatment escalation to systemic therapy, in monotherapy or in combination with TACE.

Introdução: A quimioembolização transarterial (TACE) é o tratamento de primeira linha para doentes com carcinoma hepatocelular (HCC) em estadio intermédio. Em doentes sem resposta adequada, a evidência atual sugere que o tratamento com agentes de alvo molecular, aprovado para estágio avançado, pode apresentar benefícios. Porém, isso requer função hepática preservada. O objetivo deste estudo é avaliar possíveis preditores de deterioração precoce da reserva hepática, antes da refratariedade ao TACE, em uma coorte de doentes tratados com TACE. Métodos: Análise retrospectiva de noventa e nove doentes com Child-Pugh classe A e HCC em estadio intermédio que foram submetidos a TACE como tratamento de primeira linha. Todos os doentes foram submetidos a uma avaliação bioquímica e médica antes do TACE inicial e a cada mês após. A resposta ao TACE inicial foi avaliada em 1 mês. O tempo para a deterioração da classe Child-Pugh antes da refratariedade a TACE foi avaliado. Resultados: Noventa e nove doentes foram incluídos. A resposta radiológica objetiva (ORR) ao TACE inicial foi avaliada como presente em 59 (63.4%) e ausente em 34 (36.6%) doentes. Descompensação hepática ocorreu, antes da refratariedade a TACE, em 51 (51.5%) doentes e o tempo médio para a descompensação hepática foi de 14 (IQR 8­20) meses, após o primeiro TACE. Na análise multivariada, além dos critérios up-to-7 (HR 2,4, p = 0.031), albumina <35 mg/dL (HR 3,5, p < 0.001) e ausência de ORR (HR 2,4, p = 0.020) foram associados a diminuição da sobrevida livre de descompensação hepática. Além disso, a sobrevida de 6 meses livre de descompensação hepática apresentou associação, além dos critérios up-to-7 , albumina <35 mg/dL e ausência de ORR. Foi criado um modelo com essas variáveis, capaz de prever a descompensação hepática com AUROC de 0,701 (p = 0.02). Conclusões: A ausência de ORR após TACE inicial, além dos critérios up-to-7 e albumina <35 mg/dL foram fatores preditivos para descompensação hepática antes da refratariedade a TACE na nossa população. Esses doentes podem beneficiar do escalonamento do tratamento para a terapia sistêmica, em monoterapia ou em combinação com TACE.

Spinal Anesthesia in Hemodynamic Instability: A Case Report.

Anesthetic dilemmas are not rare in daily practice. Frequently, patients present with comorbid conditions that make general anesthesia risky (e.g., difficult airway and severe pulmonary dysfunction) and contraindications to neuraxial anesthesia at the same time. Reports on the successful anesthetic management of these patients can provide useful information. We report a case of a patient with severe hemodynamic instability who underwent spinal anesthesia for surgical hip debridement. General anesthesia and airway manipulation were avoided because the patient had recently recovered from SARS-CoV-2 pneumonia amid the first wave of the coronavirus disease 2019 (COVID-19) pandemic when very little was known about the disease and no ventilators were available for postoperative care. We explain in detail the continuous spinal anesthesia technique using a conventional epidural catheter and prophylactic norepinephrine when cardiovascular instability was the major concern.

Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions.

Background and Objectives: Device-assisted enteroscopy (DAE) has a significant role in approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and can be associated with many nosological entities, namely Crohn's disease. Although the application of artificial intelligence (AI) is growing exponentially in various imaged-based gastroenterology procedures, there is still a lack of evidence of the AI technical feasibility and clinical applicability of DAE. This study aimed to develop and test a multi-brand convolutional neural network (CNN)-based algorithm for automatically detecting ulcers and erosions in DAE. Materials and Methods: A unicentric retrospective study was conducted for the development of a CNN, based on a total of 250 DAE exams. A total of 6772 images were used, of which 678 were considered ulcers or erosions after double-validation. Data were divided into a training and a validation set, the latter being used for the performance assessment of the model. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the curve precision-recall curve (AUC-PR). Results: Sensitivity, specificity, PPV, and NPV were respectively 88.5%, 99.7%, 96.4%, and 98.9%. The algorithm's accuracy was 98.7%. The AUC-PR was 1.00. The CNN processed 293.6 frames per second, enabling AI live application in a real-life clinical setting in DAE. Conclusion: To the best of our knowledge, this is the first study regarding the automatic multi-brand panendoscopic detection of ulcers and erosions throughout the digestive tract during DAE, overcoming a relevant interoperability challenge. Our results highlight that using a CNN to detect this type of lesion is associated with high overall accuracy. The development of binary CNN for automatically detecting clinically relevant endoscopic findings and assessing endoscopic inflammatory activity are relevant steps toward AI application in digestive endoscopy, particularly for panendoscopic evaluation.