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 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.

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.

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

INTRODUCTION: Device-assisted enteroscopy (DAE) plays a major role in the investigation and endoscopic treatment of small bowel diseases. Recently, the implementation of artificial intelligence (AI) algorithms to gastroenterology has been the focus of great interest. Our aim was to develop an AI model for the automatic detection of protruding lesions in DAE images. METHODS: A deep learning algorithm based on a convolutional neural network was designed. Each frame was evaluated for the presence of enteric protruding lesions. The area under the curve, sensitivity, specificity, and positive and negative predictive values were used to assess the performance of the convolutional neural network. RESULTS: A total of 7,925 images from 72 patients were included. Our model had a sensitivity and specificity of 97.0% and 97.4%, respectively. The area under the curve was 1.00. DISCUSSION: Our model was able to efficiently detect enteric protruding lesions. The development of AI tools may enhance the diagnostic capacity of deep enteroscopy techniques.

Performance of a Deep Learning System for Automatic Diagnosis of Protruding Lesions in Colon Capsule Endoscopy.

BACKGROUND: Colon capsule endoscopy (CCE) is an alternative for patients unwilling or with contraindications for conventional colonoscopy. Colorectal cancer screening may benefit greatly from widespread acceptance of a non-invasive tool such as CCE. However, reviewing CCE exams is a time-consuming process, with risk of overlooking important lesions. We aimed to develop an artificial intelligence (AI) algorithm using a convolutional neural network (CNN) architecture for automatic detection of colonic protruding lesions in CCE images. An anonymized database of CCE images collected from a total of 124 patients was used. This database included images of patients with colonic protruding lesions or patients with normal colonic mucosa or with other pathologic findings. A total of 5715 images were extracted for CNN development. Two image datasets were created and used for training and validation of the CNN. The AUROC for detection of protruding lesions was 0.99. The sensitivity, specificity, PPV and NPV were 90.0%, 99.1%, 98.6% and 93.2%, respectively. The overall accuracy of the network was 95.3%. The developed deep learning algorithm accurately detected protruding lesions in CCE images. The introduction of AI technology to CCE may increase its diagnostic accuracy and acceptance for screening of colorectal neoplasia.

Automated detection of ulcers and erosions in capsule endoscopy images using a convolutional neural network.

Capsule endoscopy (CE) is an important tool in the management of patients with known or suspected inflammatory bowel disease. Ulcers and erosions of the enteric mucosa are prevalent findings in these patients. They frequently occur together, and their identification in CE is crucial for an accurate evaluation of disease severity. Nevertheless, reviewing CE images is a time-consuming task, and the risk of overlooking lesions is significant.Over the last decade, artificial intelligence (AI) has emerged as a means for overcoming these pitfalls. Of all AI methods, convolutional neural networks (CNN), due to their complex multilayer architecture present the best results in medical image analysis, particularly capsule endoscopy. Therefore, we aimed to develop a CNN for the automatic identification of ulcers and erosions in the small bowel mucosa. A total of 1483 CE exams (PillCam SB3®) performed at a single center between 2015 and 2020 were analysed. From these exams, a total of 6130 frames of the enteric mucosa were obtained, 4233 containing enteric ulcers and erosions, and the remaining containing normal mucosa or other findings. Ulcers and erosions were stratified according to Saurin's classification for bleeding potential: P1E-erosions with intermediate bleeding risk; P1U-ulcers with intermediate bleeding risk; P2U-ulcers with high bleeding risk. For automatic identification of these lesions, these images were inserted into a CNN model with transfer learning. The pool of images was divided for constitution of training and validation datasets, comprising 80% and 20% of the total number of images, respectively. The output provided by the CNN was compared to the classification provided by a consensus of specialists. After optimizing the neural architecture of the algorithm, our model was able to automatically detect and distinguish ulcers and erosions (any bleeding potential) in the small intestine mucosa with an accuracy of 95.6%, sensitivity of 90.8%, and a specificity of 97.1%. We believe that our study lays the foundation for the development and application of effective AI tools to CE. These techniques should improve diagnostic accuracy and reading efficiency. Schematic representation of the workflow and summary of the results.

Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study.

BACKGROUND AND AIMS: The diagnosis and characterization of biliary strictures (BSs) is challenging. The introduction of digital single-operator cholangioscopy (DSOC) that allows direct visual inspection of the lesion and targeted biopsy sampling significantly improved the diagnostic yield in patients with indeterminate BSs. However, the diagnostic efficiency of DSOC remains suboptimal. Convolutional neural networks (CNNs) have shown great potential for the interpretation of medical images. We aimed to develop a CNN-based system for automatic detection of malignant BSs in DSOC images. METHODS: We developed, trained, and validated a CNN-based on DSOC images. Each frame was labeled as a normal/benign finding or as a malignant lesion if histopathologic evidence of biliary malignancy was available. The entire dataset was split for 5-fold cross-validation. In addition, the image dataset was split for constitution of training and validation datasets. The performance of the CNN was measured by calculating the area under the receiving operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. RESULTS: A total of 11,855 images from 85 patients were included (9695 malignant strictures and 2160 benign findings). The model had an overall accuracy of 94.9%, sensitivity of 94.7%, specificity of 92.1%, and AUC of .988 in cross-validation analysis. The image processing speed of the CNN was 7 ms per frame. CONCLUSIONS: The developed deep learning algorithm accurately detected and differentiated malignant strictures from benign biliary conditions. The introduction of artificial intelligence algorithms to DSOC systems may significantly increase its diagnostic yield for malignant strictures.

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

BACKGROUND: Capsule endoscopy (CE) is the first line for evaluation of patients with obscure gastrointestinal bleeding. A wide range of small intestinal vascular lesions with different hemorrhagic potential are frequently found in these patients. Nevertheless, reading CE exams is time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence tools with high performance levels in image analysis. This study aimed to develop a CNN-based model for identification and differentiation of vascular lesions with distinct hemorrhagic potential in CE images. METHODS: The development of the CNN was based on a database of CE images. This database included images of normal small intestinal mucosa, red spots, and angiectasia/varices. The hemorrhagic risk was assessed by Saurin's classification. For CNN development, 11,588 images (9525 normal mucosa, 1026 red spots, and 1037 angiectasia/varices) were ultimately extracted. Two image datasets were created for CNN training and testing. RESULTS: The network was 91.8% sensitive and 95.9% specific for detection of vascular lesions, providing accurate predictions in 94.4% of cases. In particular, the CNN had a sensitivity and specificity of 97.1% and 95.3%, respectively, for detection of red spots. Detection of angiectasia/varices occurred with a sensitivity of 94.1% and a specificity of 95.1%. The CNN had a frame reading rate of 145 frames/sec. CONCLUSIONS: The developed algorithm is the first CNN-based model to accurately detect and distinguish enteric vascular lesions with different hemorrhagic risk. CNN-assisted CE reading may improve the diagnosis of these lesions and overall CE efficiency.

Automatic Identification of Papillary Projections in Indeterminate Biliary Strictures Using Digital Single-Operator Cholangioscopy.

INTRODUCTION: Characterization of biliary strictures is challenging. Papillary projections (PP) are often reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy. In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of PP in digital single-operator cholangioscopy images. METHODS: A convolutional neural network (CNN) was developed. Each frame was evaluated for the presence of PP. The CNN's performance was measured by the area under the curve, sensitivity, specificity, and positive and negative predictive values. RESULTS: A total of 3,920 images from 85 patients were included. Our model had a sensitivity and specificity 99.7% and 97.1%, respectively. The area under the curve was 1.00. DISCUSSION: Our CNN was able to detect PP with high accuracy. Future development of AI tools may optimize the macroscopic characterization of biliary strictures.

Essential role of small bowel capsule endoscopy in reclassification of colonic inflammatory bowel disease type unclassified.

AIM: To evaluate the role of small bowel capsule endoscopy (SBCE) on the reclassification of colonic inflammatory bowel disease type unclassified (IBDU). METHODS: We performed a multicenter, retrospective study including patients with IBDU undergoing SBCE, between 2002 and 2014. SBCE studies were reviewed and the inflammatory activity was evaluated by determining the Lewis score (LS). Inflammatory activity was considered significant and consistent with Crohn's disease (CD) when the LS ≥ 135. The definitive diagnosis during follow-up (minimum 12 mo following SBCE) was based on the combination of clinical, analytical, imaging, endoscopic and histological elements. RESULTS: Thirty-six patients were included, 21 females (58%) with mean age at diagnosis of 33 ± 13 (15-64) years. The mean follow-up time after the SBCE was 52 ± 41 (12-156) mo. The SBCE revealed findings consistent with significant inflammatory activity in the small bowel (LS ≥ 135) in 9 patients (25%); in all of them the diagnosis of CD was confirmed during follow-up. In 27 patients (75%), the SBCE revealed no significant inflammatory activity (LS < 135); among these patients, the diagnosis of Ulcerative Colitis (UC) was established in 16 cases (59.3%), CD in 1 case (3.7%) and 10 patients (37%) maintained a diagnosis of IBDU during follow-up. A LS ≥ 135 at SBCE had a sensitivity = 90%, specificity = 100%, positive predictive value = 100% and negative predictive value = 94% for the diagnosis of CD. CONCLUSION: SBCE proved to be fundamental in the reclassification of patients with IBDU. Absence of significant inflammatory activity in the small intestine allowed exclusion of CD in 94% of cases.

Endoscopic Scores for Evaluation of Crohn's Disease Activity at Small Bowel Capsule Endoscopy: General Principles and Current Applications.

The small bowel is affected in the vast majority of patients with Crohn's Disease (CD). Small bowel capsule endoscopy (SBCE) has a very high sensitivity for the detection of CD-related pathology, including early mucosal lesions and/or those located in the proximal segments of the small bowel, which is a major advantage when compared with other small bowel imaging modalities. The recent guidelines of European Society of Gastrointestinal Endoscopy (ESGE) and European Crohn's and Colitis Organisation (ECCO) advocate the use of validated endoscopic scoring indices for the classification of inflammatory activity in patients with CD undergoing SBCE, such as the Lewis Score or the Capsule Endoscopy Crohn's Disease Activity Index (CECDAI). These scores aim to standardize the description of lesions and capsule endoscopy reports, contributing to increase inter-observer agreement and enabling a stratification of the severity of the disease. On behalf of the Grupo de Estudos Português do Intestino Delgado (GEPID) - Portuguese Small Bowel Study Group, we aimed to summarize the general principles and clinical applications of current endoscopic scoring systems for SBCE in the setting of CD, covering the topic of suspected CD as well as the evaluation of disease extent (with potential prognostic and therapeutic impact), evaluation of mucosal healing in response to treatment and evaluation of post-surgical recurrence in patients with previously established diagnosis of CD.

O intestino delgado encontra-se envolvido pela doença na maioria dos pacientes com Doença de Crohn (DC). A enteroscopia por cápsula (EC) apresenta uma elevada sensibilidade na detecção de lesões relacionadas com a DC, incluindo as lesões superficiais mais precoces e/ou localizadas no segmentos proximais do intestino delgado, o que representa uma clara mais-valia comparativamente com os demais exames imagiológicos do intestino delgado. As recentes recomendações da ESGE (European Society of Gastrointestinal Endoscopy) e da ECCO (European Crohn's and Colitis Organisation) recomendam a utilização dos scores endoscópicos validados para a classificação da actividade inflamatória em doentes com DC submetidos a EC, nomeadamente o Score de Lewis ou o CECDAI (Capsule Endoscopy Crohn's Disease Activity Index). Estes scores permitem uniformizar a descrição das lesões e os relatórios em EC, contribuindo para uma melhoria da concordância entre observadores e possibilitando a estratificação da gravidade da doença. Em nome do GEPID (Grupo de Estudos Português do Intestino Delgado - Portuguese Small Bowel Study Group), os autores pretendem sumariar neste documento os princípios gerais e as aplicações clínicas actuais dos scores endoscópicos em EC no contexto da DC, incluindo quer a suspeita de DC, quer a avaliação da extensão da doença (com potencial impacto prognóstico e na decisão terapêutica), avaliação da cicatrização da mucosa em resposta ao tratamento e avaliação da recorrência pós-cirúrgica em doentes com um diagnóstico prévio de DC.