A Comprehensive Review of Artificial Intelligence and Colon Capsule Endoscopy: Opportunities and Challenges.

Colon capsule endoscopy (CCE) enables a comprehensive, non-invasive, and painless evaluation of the colon, although it still has limited indications. The lengthy reading times hinder its wider implementation, a drawback that could potentially be overcome through the integration of artificial intelligence (AI) models. Studies employing AI, particularly convolutional neural networks (CNNs), demonstrate great promise in using CCE as a viable option for detecting certain diseases and alterations in the colon, compared to other methods like colonoscopy. Additionally, employing AI models in CCE could pave the way for a minimally invasive panenteric or even panendoscopic solution. This review aims to provide a comprehensive summary of the current state-of-the-art of AI in CCE while also addressing the challenges, both technical and ethical, associated with broadening indications for AI-powered CCE. Additionally, it also gives a brief reflection of the potential environmental advantages of using this method compared to alternative ones.

Software as a Medical Device (SaMD) in Digestive Healthcare: Regulatory Challenges and Ethical Implications.

The growing integration of software in healthcare, particularly the rise of standalone software as a medical device (SaMD), is transforming digestive medicine, a field heavily reliant on medical imaging for both diagnosis and therapeutic interventions. This narrative review aims to explore the impact of SaMD on digestive healthcare, focusing on the evolution of these tools and their regulatory and ethical challenges. Our analysis highlights the exponential growth of SaMD in digestive healthcare, driven by the need for precise diagnostic tools and personalized treatment strategies. This rapid advancement, however, necessitates the parallel development of a robust regulatory framework to ensure SaMDs are transparent and deliver universal clinical benefits without the introduction of bias or harm. In addition, the discussion highlights the importance of adherence to the FAIR principles for data management-findability, accessibility, interoperability, and reusability. However, enhanced accessibility and interoperability require rigorous protocols to ensure compliance with data protection guidelines and adequate data security, both of which are crucial for effective integration of SaMDs into clinical workflows. In conclusion, while SaMDs hold significant promise for improving patients' outcomes in digestive medicine, their successful integration into clinical workflow depends on rigorous data protection protocols and clinical validation. Future directions include the need for adequate clinical and real-world studies to demonstrate that these devices are safe and well-suited to healthcare settings.

Epidural Block in a Patient With Melkersson-Rosenthal Syndrome: A Case Report.

This case report describes the first known application of an epidural block for labor analgesia in a patient with Melkersson-Rosenthal syndrome (MRS), a rare disorder that may present sudden and threatening airway complications. A tailored epidural protocol effectively mitigated symptom exacerbation, facilitating a complication-free vaginal delivery. This report not only enriches the sparse literature on anesthesia in patients with MRS but also provides a crucial review of perioperative considerations for administering either general or regional anesthesia in similar cases.

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.

Higher frequency of gastric neoplasia in advanced chronic liver disease patients: Impact of screening endoscopy in an intermediate-high risk country.

BACKGROUND: The Baveno VII guidelines were proposed to identify which patients could safely avoid screening esophagogastroduodenoscopy (EGD) for gastroesophageal varices. We aimed to evaluate the frequency of gastric neoplasia in compensated advanced chronic liver disease (cACLD) patients who underwent EGD for screening of gastroesophageal varices (GOEV) compared to a healthy population. METHODS: Retrospective study that enrolled all cACLD patients who underwent EGD for GOEV screening (January 2008-June 2018) in a tertiary reference center. cACLD patients were compared with asymptomatic healthy individuals who underwent EGD in a private hospital setting (April 2017-March 2018). RESULTS: We evaluated 1845 patients (481 cACLD patients, 1364 healthy individuals). A significantly higher frequency of gastric neoplasia was observed in patients with cACLD compared to healthy individuals (4.0% vs. 1.0 %; p < 0.001). Rare histopathological subtypes (WHO Classification) accounted for 28.7 % of gastric carcinoma cases in the cACLD cohort. Seven cases of gastric neoplasia (36.8 % of gastric neoplasia cases in the cACLD patients) were diagnosed in patients who, according to the Baveno VII criteria, would have not been submitted to EGD. CONCLUSION: We found an increased frequency of gastric neoplasia in patients with cACLD in comparison with healthy individuals. In countries with intermediate-high risk for GC, continuing to perform EGD could be beneficial.

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.

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.

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.

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.

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.

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.

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.