Software for automated classification of probe-based confocal laser endomicroscopy videos of colorectal polyps

Machine Learning-Based Prediction of Responsiveness to Neoadjuvant Chemoradiotheapy in Locally Advanced Rectal Cancer Patients from Endomicroscopy

The protocol for treating locally advanced rectal cancer consists of the application of chemoradiotherapy (neoCRT) followed by surgical intervention. One issue for clinical oncologists is predicting the efficacy of neoCRT in order to adjust the dosage and avoid treatment toxicity in cases when surgery should be conducted promptly. Biomarkers may be used for this purpose along with in vivo cell-level images of the colorectal mucosa obtained by probe-based confocal laser endomicroscopy (pCLE) during colonoscopy. The aim of this article is to report our experience with Motiro, a computational framework that we developed for machine learning (ML) based analysis of pCLE videos for predicting neoCRT response in locally advanced rectal cancer patients. pCLE videos were collected from 47 patients who were diagnosed with locally advanced rectal cancer (T3/T4, or N+). The patients received neoCRT. Response to treatment by all patients was assessed by endoscopy along with biopsy and magnetic resonance imaging (MRI). Thirty-seven patients were classified as non-responsive to neoCRT because they presented a visible macroscopic neoplastic lesion, as confirmed by pCLE examination. Ten remaining patients were considered responsive to neoCRT because they presented lesions as a scar or small ulcer with negative biopsy, at post-treatment follow-up. Motiro was used for batch mode analysis of pCLE videos. It automatically characterized the tumoral region and its surroundings. That enabled classifying a patient as responsive or non-responsive to neoCRT based on pre-neoCRT pCLE videos. Motiro classified patients as responsive or non-responsive to neoCRT with an accuracy of ~ 0.62 when using images of the tumor. When using images of regions surrounding the tumor, it reached an accuracy of ~ 0.70. Feature analysis showed that spatial heterogeneity in fluorescence distribution within regions surrounding the tumor was the main contributor to predicting response to neoCRT. We developed a computational framework to predict response to neoCRT by locally advanced rectal cancer patients based on pCLE images acquired pre-neoCRT. We demonstrate that the analysis of the mucosa of the region surrounding the tumor provides stronger predictive power.

Diagnostic accuracy of endocytoscopy via artificial intelligence in colorectal lesions: A systematic review and meta‑analysis

BACKGROUND

Endocytoscopy (EC) is a nuclei and micro-vessels visualization in real-time and can facilitate "optical biopsy" and "virtual histology" of colorectal lesions. This study aimed to investigate the significance of employing artificial intelligence (AI) in the field of endoscopy, specifically in diagnosing colorectal lesions. The research was conducted under the supervision of experienced professionals and trainees.

METHODS

EMBASE, PubMed, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure (CNKI) database, and other potential databases were surveyed for articles related to the EC with AI published before September 2023. RevMan (5.40), Stata (14.0), and R software (4.1.0) were used for statistical assessment. Studies that measured the accuracy of EC using AI for colorectal lesions were included. Two authors independently assessed the selected studies and their extracted data. This included information such as the country, literature, total study population, study design, characteristics of the fundamental study and control groups, sensitivity, number of samples, assay methodology, specificity, true positives or negatives, and false positives or negatives. The diagnostic accuracy of EC by AI was determined by a bivariate random-effects model, avoiding a high heterogeneity effect. The ANOVA model was employed to determine the more effective approach.

RESULTS

A total of 223 studies were reviewed; 8 articles were selected that included 2984 patients (4241 lesions) for systematic review and meta-analysis. AI assessed 4069 lesions; experts diagnosed 3165 and 5014 by trainees. AI demonstrated high accuracy, sensitivity, and specificity levels in detecting colorectal lesions, with values of 0.93 (95% CI: 0.90, 0.95) and 0.94 (95% CI: 0.73, 0.99). Expert diagnosis was 0.90 (95% CI: 0.85, 0.94), 0.87 (95% CI: 0.78, 0.93), and trainee diagnosis was 0.74 (95% CI: 0.67, 0.79), 0.72 (95% CI: 0.62, 0.80). With the EC by AI, the AUC from SROC was 0.95 (95% CI: 0.93, 0.97), therefore classified as excellent category, expert showed 0.95 (95% CI: 0.93, 0.97), and the trainee had 0.79 (95% CI: 0.75, 0.82). The superior index from the ANOVA model was 4.00 (1.15,5.00), 2.00 (1.15,5.00), and 0.20 (0.20,0.20), respectively. The examiners conducted meta-regression and subgroup analyses to evaluate the presence of heterogeneity. The findings of these investigations suggest that the utilization of NBI technology was correlated with variability in sensitivity and specificity. There was a lack of solid evidence indicating the presence of publishing bias.

CONCLUSIONS

The present findings indicate that using AI in EC can potentially enhance the efficiency of diagnosing colorectal abnormalities. As a valuable instrument, it can enhance prognostic outcomes in ordinary EC procedures, exhibiting superior diagnostic accuracy compared to trainee-level endoscopists and demonstrating comparability to expert endoscopists. The research is subject to certain constraints, namely a limited number of clinical investigations and variations in the methodologies used for identification. Consequently, it is imperative to conduct comprehensive and extensive research to enhance the precision of diagnostic procedures.

Artificial intelligence in endoscopy: Overview, applications, and future directions

Since the emergence of artificial intelligence (AI) in medicine, endoscopy applications in gastroenterology have been at the forefront of innovations. The ever-increasing number of studies necessitates the need to organize and classify applications in a useful way. Separating AI capabilities by computer aided detection (CADe), diagnosis (CADx), and quality assessment (CADq) allows for a systematic evaluation of each application. CADe studies have shown promise in accurate detection of esophageal, gastric and colonic neoplasia as well as identifying sources of bleeding and Crohn's disease in the small bowel. While more advanced CADx applications employ optical biopsies to give further information to characterize neoplasia and grade inflammatory disease, diverse CADq applications ensure quality and increase the efficiency of procedures. Future applications show promise in advanced therapeutic modalities and integrated systems that provide multimodal capabilities. AI is set to revolutionize clinical decision making and performance of endoscopy.

Image enhanced colonoscopy: updates and prospects-a review

Colonoscopy has been proven to be a successful approach in both identifying and preventing colorectal cancer. The incorporation of advanced imaging technologies, such as image-enhanced endoscopy (IEE), plays a vital role in real-time diagnosis. The advancements in endoscopic imaging technology have been continuous, from replacing fiber optics with charge-coupled devices to the introduction of chromoendoscopy in the 1970s. Recent technological advancements include "push-button" technologies like autofluorescence imaging (AFI), narrowed-spectrum endoscopy, and confocal laser endomicroscopy (CLE). Dye-based chromoendoscopy (DCE) is falling out of favor due to the longer time required for application and removal of the dye and the difficulty of identifying lesions in certain situations. Narrow band imaging (NBI) is a technology that filters the light used for illumination leading to improved contrast and better visibility of structures on the mucosal surface and has shown a consistently higher adenoma detection rate (ADR) compared to white light endoscopy. CLE has high sensitivity and specificity for polyp detection and characterization, and several classifications have been developed for accurate identification of normal, regenerative, and dysplastic epithelium. Other IEE technologies, such as blue laser imaging (BLI), linked-color imaging (LCI), i-SCAN, and AFI, have also shown promise in improving ADR and characterizing polyps. New technologies, such as Optivista, red dichromatic imaging (RDI), texture and color enhancement imaging (TXI), and computer-aided detection (CAD) using artificial intelligence (AI), are being developed to improve polyp detection and pathology prediction prior to widespread use in clinical practice.

Probe - Based Confocal Endomicroscopy and Endoscopy in NBI Module - The Role in Clinical Decision - Case Reports

Cancer remains a significant global cause of mortality, irrespective of a country's level of development. Among all cancer types, gastrointestinal cancers claim the highest number of lives annually. This disease predominantly affects individuals in their 6th to 8th decades of life. Unfortunately, diagnoses often occur during advanced stages of the illness, rendering chemotherapy less effective and offering a reserved prognosis. Conventional endoscopy, however, struggles to differentiate lesions based on their histological composition. Consequently, the management of patients largely depends on histopathological examinations, which can be time-consuming. Biopsy results are sometimes delayed, with precious weeks passing, particularly critical for patients with malignant lesions. Moreover, biopsies may yield inconclusive results if not precisely targeted, leading to potential mismanagement, unnecessary resections and burdensome pathology services. This series of cases underscores, as previous studies have, the value of modern endoscopic techniques in determining the appropriate therapeutic approach for each patient, an approach that ensures the highest quality of life.

The application of artificial intelligence in improving colonoscopic adenoma detection rate: Where are we and where are we going

Colorectal cancer (CRC) is one of the common malignant tumors in the world. Colonoscopy is the crucial examination technique in CRC screening programs for the early detection of precursor lesions, and treatment of early colorectal cancer, which can reduce the morbidity and mortality of CRC significantly. However, pooled polyp miss rates during colonoscopic examination are as high as 22%. Artificial intelligence (AI) provides a promising way to improve the colonoscopic adenoma detection rate (ADR). It might assist endoscopists in avoiding missing polyps and offer an accurate optical diagnosis of suspected lesions. Herein, we described some of the milestone studies in using AI for colonoscopy, and the future application directions of AI in improving colonoscopic ADR.

Artificial Intelligence-The Rising Star in the Field of Gastroenterology and Hepatology

Artificial intelligence (AI) is a term that covers a multitude of techniques that are used in a manner that tries to reproduce human intelligence. AI is helpful in various medical specialties that use imaging for diagnostic purposes, and gastroenterology is no exception. In this field, AI has several applications, such as detecting and classifying polyps, detecting the malignancy in polyps, diagnosing Helicobacter pylori infection, gastritis, inflammatory bowel disease, gastric cancer, esophageal neoplasia, and pancreatic and hepatic lesions. The aim of this mini-review is to analyze the currently available studies regarding AI in the field of gastroenterology and hepatology and to discuss its main applications as well as its main limitations.

Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions

Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.

Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps

OBJECTIVE

The main aim of this study was to analyze the performance of different artificial intelligence (AI) models in endoscopic colonic polyp detection and classification and compare them with doctors with different experience.

METHODS

We searched the studies on Colonoscopy, Colonic Polyps, Artificial Intelligence, Machine Learning, and Deep Learning published before May 2020 in PubMed, EMBASE, Cochrane, and the citation index of the conference proceedings. The quality of studies was assessed using the QUADAS-2 table of diagnostic test quality evaluation criteria. The random-effects model was calculated using Meta-DISC 1.4 and RevMan 5.3.

RESULTS

A total of 16 studies were included for meta-analysis. Only one study (1/16) presented externally validated results. The area under the curve (AUC) of AI group, expert group and non-expert group for detection and classification of colonic polyps were 0.940, 0.918, and 0.871, respectively. AI group had slightly lower pooled specificity than the expert group (79% vs. 86%, P < 0.05), but the pooled sensitivity was higher than the expert group (88% vs. 80%, P < 0.05). While the non-experts had less pooled specificity in polyp recognition than the experts (81% vs. 86%, P < 0.05), and higher pooled sensitivity than the experts (85% vs. 80%, P < 0.05).

CONCLUSION

The performance of AI in polyp detection and classification is similar to that of human experts, with high sensitivity and moderate specificity. Different tasks may have an impact on the performance of deep learning models and human experts, especially in terms of sensitivity and specificity.

Artificial intelligence and machine learning in colorectal cancer

Colorectal cancer (CRC) is a heterogeneous illness characterized by various epigenetic and microenvironmental changes and is the third-highest cause of cancer-related death in the US. Artificial intelligence (AI) with its ability to allow automatic learning and improvement from experiences using statistical methods and Deep learning has made a distinctive contribution to the diagnosis and treatment of several cancer types. This review discusses the uses and application of AI in CRC screening using automated polyp detection assistance technologies to the development of computer-assisted diagnostic algorithms capable of accurately detecting polyps during colonoscopy and classifying them. Furthermore, we summarize the current research initiatives geared towards building computer-assisted diagnostic algorithms that aim at improving the diagnostic accuracy of benign from premalignant lesions. Considering the evolving transition to more personalized and tailored treatment strategies for CRC, the review also discusses the development of machine learning algorithms to understand responses to therapies and mechanisms of resistance as well as the future roles that AI applications may play in assisting in the treatment of CRC with the aim to improve disease outcomes. We also discuss the constraints and limitations of the use of AI systems. While the medical profession remains enthusiastic about the future of AI and machine learning, large-scale randomized clinical trials are needed to analyze AI algorithms before they can be used.

Current Status and Future Perspectives of Artificial Intelligence in Colonoscopy

The early endoscopic identification, resection, and treatment of precancerous adenoma and early-stage cancer has been shown to reduce not only the prevalence of colorectal cancer but also its mortality rate. Recent advances in endoscopic devices and imaging technology have dramatically improved our ability to detect colorectal lesions and predict their pathological diagnosis. In addition to this, rapid advances in artificial intelligence (AI) technology mean that AI-related research and development is now progressing in the diagnostic imaging field, particularly colonoscopy, and AIs (i.e., devices that mimic cognitive abilities, such as learning and problem-solving) already approved as medical devices are now being introduced into everyday clinical practice. Today, there is an increasing expectation that sophisticated AIs will be able to provide high-level diagnostic performance irrespective of the level of skill of the endoscopist. In this paper, we review colonoscopy-related AI research and the AIs that have already been approved and discuss the future prospects of this technology.

Artificial Intelligence in Endoscopy

Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.

The development and clinical application of microscopic endoscopy for in vivo optical biopsies: Endocytoscopy and confocal laser endomicroscopy

Endoscopies are crucial for detecting and diagnosing diseases in gastroenterology, pulmonology, urology, and other fields. To accurately diagnose diseases, sample biopsies are indispensable and are currently considered the gold standard. However, random 4-quadrant biopsies have sampling errors and time delays. To provide intraoperative real-time microscopic images of suspicious lesions, microscopic endoscopy for in vivo optical biopsy has been developed, including endocytoscopy and confocal laser endomicroscopy. This article reviews recent advances in technology and clinical applications, as well as their shortcomings and future directions.

Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification

Colorectal cancer (CRC) is the second most frequently diagnosed type of cancer and a major worldwide public health concern. Despite the global efforts in the development of modern therapeutic strategies, CRC prognosis is strongly correlated with the stage of the disease at diagnosis. Early detection of CRC has a huge impact in decreasing mortality while pre-lesion detection significantly reduces the incidence of the pathology. Even though the management of CRC patients is based on robust diagnostic methods such as serum tumor markers analysis, colonoscopy, histopathological analysis of tumor tissue, and imaging methods (computer tomography or magnetic resonance), these strategies still have many limitations and do not fully satisfy clinical needs due to their lack of sensitivity and/or specificity. Therefore, improvements of the current practice would substantially impact the management of CRC patients. In this view, liquid biopsy is a promising approach that could help clinicians screen for disease, stratify patients to the best treatment, and monitor treatment response and resistance mechanisms in the tumor in a regular and minimally invasive manner. Liquid biopsies allow the detection and analysis of different tumor-derived circulating markers such as cell-free nucleic acids (cfNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in the bloodstream. The major advantage of this approach is its ability to trace and monitor the molecular profile of the patient's tumor and to predict personalized treatment in real-time. On the other hand, the prospective use of artificial intelligence (AI) in medicine holds great promise in oncology, for the diagnosis, treatment, and prognosis prediction of disease. AI has two main branches in the medical field: (i) a virtual branch that includes medical imaging, clinical assisted diagnosis, and treatment, as well as drug research, and (ii) a physical branch that includes surgical robots. This review summarizes findings relevant to liquid biopsy and AI in CRC for better management and stratification of CRC patients.

Artificial Intelligence-Based Assessment of Colorectal Polyp Histology by Elastic-Scattering Spectroscopy

BACKGROUND

Colonoscopic screening and surveillance for colorectal cancer could be made safer and more efficient if endoscopists could predict histology without the need to biopsy and perform histopathology on every polyp. Elastic-scattering spectroscopy (ESS), using fiberoptic probes integrated into standard biopsy tools, can assess, both in vivo and in real time, the scattering and absorption properties of tissue related to its underlying pathology.

AIMS

The objective of this study was to evaluate prospectively the potential of ESS to predict polyp pathology accurately.

METHODS

We obtained ESS measurements from patients undergoing screening/surveillance colonoscopy using an ESS fiberoptic probe integrated into biopsy forceps. The integrated forceps were used for tissue acquisition, following current standards of care, and optical measurement. All measurements were correlated to the index pathology. A machine learning model was then applied to measurements from 367 polyps in 169 patients to prospectively evaluate its predictive performance.

RESULTS

The model achieved sensitivity of 0.92, specificity of 0.87, negative predictive value (NPV) of 0.87, and high-confidence rate (HCR) of 0.84 for distinguishing 220 neoplastic polyps from 147 non-neoplastic polyps of all sizes. Among 138 neoplastic and 131 non-neoplastic polyps ≤ 5 mm, the model achieved sensitivity of 0.91, specificity of 0.88, NPV of 0.89, and HCR of 0.83.

CONCLUSIONS

Results show that ESS is a viable endoscopic platform for real-time polyp histology, particularly for polyps ≤ 5 mm. ESS is a simple, low-cost, clinically friendly, optical biopsy modality that, when interfaced with minimally obtrusive endoscopic tools, offers an attractive platform for in situ polyp assessment.

Molecular Imaging of Ulex Europaeus Agglutinin in Colorectal Cancer Using Confocal Laser Endomicroscopy (With Video)

Background and Study Aims

Previous studies have identified that colorectal cancer has different fucosylation levels compared to the normal colon. Ulex europaeus agglutinin-I (UEA-I), which specifically combines with α1-2 fucose glycan, is usually used to detect fucosylation levels. Therefore, we used confocal laser endomicroscopy (CLE) to investigate fluorescently labeled UEA-Fluorescein isothiocyanate (FITC) for detecting colonic cancer.

Patients and Methods

We stained frozen mouse colon tissue sections of normal, adenoma, and adenocarcinoma species with UEA-FITC to detect fucosylation levels in different groups. White light endoscopy and endocytoscopy were first used to detect the lesions. The UEA-FITC was then stained in the mice and human colon tissues in vitro. The CLE was used to detect the UEA-FITC levels of the corresponding lesions, and videos were recorded for quantitation analysis. The diagnostic accuracy of UEA-FITC using CLE was evaluated in terms of sensitivity and specificity.

Results

The UEA expression level in colorectal cancer was lower than that in normal intestinal epithelium. The fluorescence intensity ratio of UEA-FITC in colorectal cancer was significantly lower than that in normal tissue detected by CLE in both mice and humans. The combination of UEA-FITC and CLE presented a good diagnostic accuracy with a sensitivity of 95.6% and a specificity of 97.7% for detecting colorectal cancer. The positive and negative predictive values were 91.6% and 95.6%, respectively. Overall, 95.6% of the sites were correctly classified by CLE.

Conclusions

We developed a new imaging strategy to improve the diagnostic efficacy of CLE by using UEA-FITC.

Artificial intelligence in colonoscopy

Colorectal cancer remains a leading cause of morbidity and mortality in the United States. Advances in artificial intelligence (AI), specifically computer aided detection and computer-aided diagnosis offer promising methods of increasing adenoma detection rates with the goal of removing more pre-cancerous polyps. Conversely, these methods also may allow for smaller non-cancerous lesions to be diagnosed in vivo and left in place, decreasing the risks that come with unnecessary polypectomies. This review will provide an overview of current advances in the use of AI in colonoscopy to aid in polyp detection and characterization as well as areas of developing research.

Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm

Endoscpists always have tried to pursue a perfect colonoscopy, and application of artificial intelligence (AI) using deep-learning algorithms is one of the promising supportive options for detection and characterization of colorectal polyps during colonoscopy. Many retrospective studies conducted with real-time application of AI using convolutional neural networks have shown improved colorectal polyp detection. Moreover, a recent randomized clinical trial reported additional polyp detection with shorter analysis time. Studies conducted regarding polyp characterization provided additional promising results. Application of AI with narrow band imaging in real-time prediction of the pathology of diminutive polyps resulted in high diagnostic accuracy. In addition, application of AI with endocytoscopy or confocal laser endomicroscopy was investigated for real-time cellular diagnosis, and the diagnostic accuracy of some studies was comparable to that of pathologists. With AI technology, we can expect a higher polyp detection rate with reduced time and cost by avoiding unnecessary procedures, resulting in enhanced colonoscopy efficiency. However, for AI application in actual daily clinical practice, more prospective studies with minimized selection bias, consensus on standardized utilization, and regulatory approval are needed. (Gut Liver 2021;15:-353)

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.

Automated software-assisted diagnosis of esophageal squamous cell neoplasia using high-resolution microendoscopy

BACKGROUND AND AIMS

High-resolution microendoscopy (HRME) is an optical biopsy technology that provides subcellular imaging of esophageal mucosa but requires expert interpretation of these histopathology-like images. We compared endoscopists with an automated software algorithm for detection of esophageal squamous cell neoplasia (ESCN) and evaluated the endoscopists' accuracy with and without input from the software algorithm.

METHODS

Thirteen endoscopists (6 experts, 7 novices) were trained and tested on 218 post-hoc HRME images from 130 consecutive patients undergoing ESCN screening/surveillance. The automated software algorithm interpreted all images as neoplastic (high-grade dysplasia, ESCN) or non-neoplastic. All endoscopists provided their interpretation (neoplastic or non-neoplastic) and confidence level (high or low) without and with knowledge of the software overlay highlighting abnormal nuclei and software interpretation. The criterion standard was histopathology consensus diagnosis by 2 pathologists.

RESULTS

The endoscopists had a higher mean sensitivity (84.3%, standard deviation [SD] 8.0% vs 76.3%, P = .004), lower specificity (75.0%, SD 5.2% vs 85.3%, P < .001) but no significant difference in accuracy (81.1%, SD 5.2% vs 79.4%, P = .26) of ESCN detection compared with the automated software algorithm. With knowledge of the software algorithm, the specificity of the endoscopists increased significantly (75.0% to 80.1%, P = .002) but not the sensitivity (84.3% to 84.8%, P = .75) or accuracy (81.1% to 83.1%, P = .13). The increase in specificity was among novices (P = .008) but not experts (P = .11).

CONCLUSIONS

The software algorithm had lower sensitivity but higher specificity for ESCN detection than endoscopists. Using computer-assisted diagnosis, the endoscopists maintained high sensitivity while increasing their specificity and accuracy compared with their initial diagnosis. Automated HRME interpretation would facilitate widespread usage in resource-poor areas where this portable, low-cost technology is needed.

Artificial intelligence in gastrointestinal endoscopy

Background and Aims

Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis.

Methods

The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board.

Results

Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett’s esophagus, and detection of various abnormalities in wireless capsule endoscopy images.

Conclusions

The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.

Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer

Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.

Value of endoscopy with narrow-band imaging and probe-based confocal laser endomicroscopy in the diagnosis of preneoplastic lesions of gastrointestinal tract

INTRODUCTION

Amongst all malignant tumors, cancers of the digestive tract rank first in terms of yearly deaths. Patients above 60 years of age are the most affected, as the diagnosis is frequently made in advanced stages of the disease when therapy is less effective. Our study aimed to evaluate the efficiency of narrow-band imaging (NBI) endoscopy and probe-based confocal laser endomicroscopy (pCLE) in the correct diagnosis of preneoplastic lesions in the upper and lower digestive tract.

PATIENTS, MATERIALS AND METHODS

We included 46 patients with digestive preneoplastic lesions, who underwent either upper or lower digestive endoscopy, followed by NBI and pCLE. We recorded 5-10 frames per each lesion, from different angles and distances during white-light endoscopy and selected frames from full recordings of NBI and pCLE. Usual preparation was used for the endoscopic procedures; pCLE required in vivo administration of 10% Sodium Fluorescein as a contrast agent. Pathology was performed in case of solid tumors. Three medical professionals with different levels of training, blinded to the results, interpreted the data.

RESULTS

The experienced physician correlated very well the NBI findings with pathology (0.93, p=0.05), while the resident physician and the experienced nurse obtain lower, albeit still statistically significant, values (0.73 and 0.62, respectively). For pCLE, the experienced physician obtained near-perfect correlation with pathology (0.96), followed closely by the resident physician (0.93). The nurse obtained a modest correlation (0.42). All examiners obtained approximately equal performances in discerning between malignant and benign lesions.

CONCLUSIONS

Digestive endoscopy in NBI mode proved its effectiveness. Even less experienced endoscopists can achieve good results, while an experienced nurse can positively influence the diagnosis. In the case of pCLE, when available, it can greatly reduce diagnostic times, while requiring higher expertise and specialty training.

Accuracy of artificial intelligence on histology prediction and detection of colorectal polyps: a systematic review and meta-analysis

BACKGROUND AND AIMS

We performed a meta-analysis of all published studies to determine the diagnostic accuracy of artificial intelligence (AI) on histology prediction and detection of colorectal polyps.

METHOD

We searched Embase, PubMed, Medline, Web of Science, and Cochrane library databases to identify studies using AI for colorectal polyp histology prediction and detection. The quality of included studies was measured by the Quality Assessment of Diagnostic Accuracy Studies tool. We used a bivariate meta-analysis following a random-effects model to summarize the data and plotted hierarchical summary receiver operating characteristic curves. The area under the hierarchical summary receiver operating characteristic curve (AUC) served as an indicator of the diagnostic accuracy and during head-to-head comparisons.

RESULTS

A total of 7680 images of colorectal polyps from 18 studies were included in the analysis of histology prediction. The accuracy of the AI (AUC) was .96 (95% confidence interval [CI], .95-.98), with a corresponding pooled sensitivity of 92.3% (95% CI, 88.8%-94.9%) and specificity of 89.8% (95% CI, 85.3%-93.0%). The AUC of AI using narrow-band imaging (NBI) was significantly higher than the AUC using non-NBI (.98 vs .84, P < .01). The performance of AI was superior to nonexpert endoscopists (.97 vs .90, P < .01). For characterization of diminutive polyps using a deep learning model with nonmagnifying NBI, the pooled negative predictive value was 95.1% (95% CI, 87.7%-98.1%). For polyp detection, the pooled AUC was .90 (95% CI, .67-1.00) with a sensitivity of 95.0% (95% CI, 91.0%-97.0%) and a specificity of 88.0% (95% CI, 58.0%-99.0%).

CONCLUSIONS

AI was accurate in histology prediction and detection of colorectal polyps, including diminutive polyps. The performance of AI was better under NBI and was superior to nonexpert endoscopists. Despite the difference in AI models and study designs, AI performances are rather consistent, which could serve as a reference for future AI studies.

SAGES TAVAC safety and efficacy analysis confocal laser endomicroscopy

BACKGROUND

Confocal laser endomicroscopy (CLE) is a novel endoscopic adjunct that allows real-time in vivo histological examination of mucosal surfaces. By using intravenous or topical fluorescent agents, CLE highlights certain mucosal elements that facilitate an optical biopsy in real time. CLE technology has been used in different organ systems including the gastrointestinal tract. There has been numerous studies evaluating this technology in gastrointestinal endoscopy, our aim was to evaluate the safety, value, and efficacy of this technology in the gastrointestinal tract.

METHODS

The Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) Technology and Value Assessment Committee (TAVAC) performed a PubMed/Medline database search of clinical studies involving CLE in May of 2018. The literature search used combinations of the keywords: confocal laser endomicroscopy, pCLE, Cellvizio, in vivo microscopy, optical histology, advanced endoscopic imaging, and optical diagnosis. Bibliographies of key references were searched for relevant studies not covered by the PubMed search. Case reports and small case series were excluded. The manufacturer's website was also used to identify key references. The United States Food and Drug Administration (U.S. FDA) Manufacturer And User facility and Device Experience (MAUDE) database was searched for reports regarding the device malfunction or injuries.

RESULTS

The technology offers an excellent safety profile with rare adverse events related to the use of fluorescent agents. It has been shown to increase the detection of dysplastic Barrett's esophagus, gastric intraepithelial neoplasia/early gastric cancer, and dysplasia associated with inflammatory bowel disease when compared to standard screening protocols. It also aids in the differentiation and classification of colorectal polyps, indeterminate biliary strictures, and pancreatic cystic lesions.

CONCLUSIONS

CLE has an excellent safety profile. CLE can increase the diagnostic accuracy in a number of gastrointestinal pathologies.

The future of endoscopy: Advances in endoscopic image innovations

The latest state of the art technological innovations have led to a palpable progression in endoscopic imaging and may facilitate standardisation of practice. One of the most rapidly evolving modalities is artificial intelligence with recent studies providing real-time diagnoses and encouraging results in the first randomised trials to conventional endoscopic imaging. Advances in functional hypoxia imaging offer novel opportunities to be used to detect neoplasia and the assessment of colitis. Three-dimensional volumetric imaging provides spatial information and has shown promise in the increased detection of small polyps. Studies to date of self-propelling colonoscopes demonstrate an increased caecal intubation rate and possibly offer patients a more comfortable procedure. Further development in robotic technology has introduced ex vivo automated locomotor upper gastrointestinal and small bowel capsule devices. Eye-tracking has the potential to revolutionise endoscopic training through the identification of differences in experts and non-expert endoscopist as trainable parameters. In this review, we discuss the latest innovations of all these technologies and provide perspective into the exciting future of diagnostic luminal endoscopy.

Image computing for fibre-bundle endomicroscopy: A review

Endomicroscopy is an emerging imaging modality, that facilitates the acquisition of in vivo, in situ optical biopsies, assisting diagnostic and potentially therapeutic interventions. While there is a diverse and constantly expanding range of commercial and experimental optical biopsy platforms available, fibre-bundle endomicroscopy is currently the most widely used platform and is approved for clinical use in a range of clinical indications. Miniaturised, flexible fibre-bundles, guided through the working channel of endoscopes, needles and catheters, enable high-resolution imaging across a variety of organ systems. Yet, the nature of image acquisition though a fibre-bundle gives rise to several inherent characteristics and limitations necessitating novel and effective image pre- and post-processing algorithms, ranging from image formation, enhancement and mosaicing to pathology detection and quantification. This paper introduces the underlying technology and most prevalent clinical applications of fibre-bundle endomicroscopy, and provides a comprehensive, up-to-date, review of relevant image reconstruction, analysis and understanding/inference methodologies. Furthermore, current limitations as well as future challenges and opportunities in fibre-bundle endomicroscopy computing are identified and discussed.

Review of computational methods for the detection and classification of polyps in colonoscopy imaging

Computer-aided diagnosis (CAD) is a tool with great potential to help endoscopists in the tasks of detecting and histologically classifying colorectal polyps. In recent years, different technologies have been described and their potential utility has been increasingly evidenced, which has generated great expectations among scientific societies. However, most of these works are retrospective and use images of different quality and characteristics which are analysed off line. This review aims to familiarise gastroenterologists with computational methods and the particularities of endoscopic imaging, which have an impact on image processing analysis. Finally, the publicly available image databases, needed to compare and confirm the results obtained with different methods, are presented.

Advanced Endoscopic Imaging in Colonic Neoplasia

Background

Endoscopic imaging is a rapidly evolving field with a constant influx of new concepts and technologies. Since the introduction of video endoscopy and subsequently high-definition imaging as the first revolutions in gastrointestinal endoscopy, several technologies of virtual chromoendoscopy have been developed and brought to the market in the past decade, which have shaped and revolutionized for a second time our approach to endoscopic imaging. In parallel to these developments, microscopic imaging technologies, such as endomicroscopy and endocytoscopy, allow us to examine single cells within the mucosa in real time, thereby enabling histological diagnoses during ongoing endoscopy.

Summary

In this review, we provide an overview on the technical background of different technologies of advanced endoscopic imaging, and then review and discuss their role and applications for the diagnosis and management of colorectal neoplasms as well as limitations and challenges that exist despite all technological improvements.

Key Messages

Technologies of advanced endoscopic imaging have profound impact not only on our imaging capabilities, they are also about to fundamentally change our approach to managing lesions in the gastrointestinal tract: not every lesion found during colonoscopy has to be excised or sent for histopathologic evaluation. However, before this becomes widespread reality, major obstacles such as patient acceptance, adoption by less trained endoscopists, and also legal aspects need to carefully addressed. The development of computer-aided diagnosis and artificial intelligence algorithms hold the potential to overcome the obstacles associated with the concept of optical biopsy and will most likely fundamentally facilitate, shape, and change decision making in the management of colorectal lesions.

Artificial intelligence in luminal endoscopy

Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett's, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence-augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence-augmented diagnostic luminal endoscopy into our routine clinical practice.

Computer-assisted assessment of colonic polyp histopathology using probe-based confocal laser endomicroscopy

INTRODUCTION

Probe-based confocal laser endomicroscopy (pCLE) is a promising modality for classifying polyp histology in vivo, but decision making in real-time is hampered by high-magnification targeting and by the learning curve for image interpretation. The aim of this study is to test the feasibility of a system combining the use of a low-magnification, wider field-of-view pCLE probe and a computer-assisted diagnosis (CAD) algorithm that automatically classifies colonic polyps.

METHODS

This feasibility study utilized images of polyps from 26 patients who underwent colonoscopy with pCLE. The pCLE images were reviewed offline by two expert and five junior endoscopists blinded to index histopathology. A subset of images was used to train classification software based on the consensus of two GI histopathologists. Images were processed to extract image features as inputs to a linear support vector machine classifier. We compared the CAD algorithm's prediction accuracy against the classification accuracy of the endoscopists.

RESULTS

We utilized 96 neoplastic and 93 non-neoplastic confocal images from 27 neoplastic and 20 non-neoplastic polyps. The CAD algorithm had sensitivity of 95%, specificity of 94%, and accuracy of 94%. The expert endoscopists had sensitivities of 98% and 95%, specificities of 98% and 96%, and accuracies of 98% and 96%, while the junior endoscopists had, on average, a sensitivity of 60%, specificity of 85%, and accuracy of 73%.

CONCLUSION

The CAD algorithm showed comparable performance to offline review by expert endoscopists and improved performance when compared to junior endoscopists and may be useful for assisting clinical decision making in real time.

Quality assurance of computer-aided detection and diagnosis in colonoscopy

Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field "deep learning," have direct implications for computer-aided detection and diagnosis (CADe and/or CADx) for colonoscopy. AI is expected to have at least 2 major roles in colonoscopy practice-polyp detection (CADe) and polyp characterization (CADx). CADe has the potential to decrease the polyp miss rate, contributing to improving adenoma detection, whereas CADx can improve the accuracy of colorectal polyp optical diagnosis, leading to reduction of unnecessary polypectomy of non-neoplastic lesions, potential implementation of a resect-and-discard paradigm, and proper application of advanced resection techniques. A growing number of medical-engineering researchers are developing both CADe and CADx systems, some of which allow real-time recognition of polyps or in vivo identification of adenomas, with over 90% accuracy. However, the quality of the developed AI systems as well as that of the study designs vary significantly, hence raising some concerns regarding the generalization of the proposed AI systems. Initial studies were conducted in an exploratory or retrospective fashion by using stored images and likely overestimating the results. These drawbacks potentially hinder smooth implementation of this novel technology into colonoscopy practice. The aim of this article is to review both contributions and limitations in recent machine-learning-based CADe and/or CADx colonoscopy studies and propose some principles that should underlie system development and clinical testing.

Artificial intelligence and colonoscopy: Current status and future perspectives

BACKGROUND AND AIM

Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer-aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is still in the preclinical phase. Because colonoscopy is carried out by humans, it is inherently an imperfect procedure. CAD assistance is expected to improve its quality regarding automated polyp detection and characterization (i.e. predicting the polyp's pathology). It could help prevent endoscopists from missing polyps as well as provide a precise optical diagnosis for those detected. Ultimately, these functions that CAD provides could produce a higher adenoma detection rate and reduce the cost of polypectomy for hyperplastic polyps.

METHODS AND RESULTS

Currently, research on automated polyp detection has been limited to experimental assessments using an algorithm based on ex vivo videos or static images. Performance for clinical use was reported to have >90% sensitivity with acceptable specificity. In contrast, research on automated polyp characterization seems to surpass that for polyp detection. Prospective studies of in vivo use of artificial intelligence technologies have been reported by several groups, some of which showed a >90% negative predictive value for differentiating diminutive (≤5 mm) rectosigmoid adenomas, which exceeded the threshold for optical biopsy.

CONCLUSION

We introduce the potential of using CAD for colonoscopy and describe the most recent conditions for regulatory approval for artificial intelligence-assisted medical devices.

Real-time computer-aided diagnosis of diminutive rectosigmoid polyps using an auto-fluorescence imaging system and novel color intensity analysis software

Objectives: An endoscopic technique that provides ≥90% negative predictive value (NPV) for differentiating neoplastic polyps is needed for the management of diminutive (≤5 mm) rectosigmoid polyps. This study aimed to assess whether a newly developed software can achieve ≥90% NPV for differentiating rectosigmoid diminutive polyps based on the green-to-red (G/R) ratio, obtained by dividing the green color tone intensity by the red color tone intensity on autofluorescence imaging (AFI). Methods: From December 2017 to May 2018, consecutive patients with known polyps who were scheduled for endoscopic treatment at our institution were prospectively recruited. All colorectal diminutive polyps were differentiated by computer-aided diagnosis using autofluorescence imaging (CAD-AFI) using a novel software-based automatic color intensity analysis; subsequent diagnosis was made by endoscopists based on trimodal imaging endoscopy (TME), which combines AFI, white-light imaging (WLI) and magnifying narrow-band imaging (M-NBI) findings. Thereafter, all polyps were removed endoscopically, and the histopathological diagnosis was evaluated. Results: Ninety-five patients with 258 diminutive rectosigmoid polyps and 171 diminutive non-rectosigmoid polyps were enrolled. Regarding diminutive rectosigmoid polyps, the NPV for differentiating neoplastic polyps was 93.4% (184/197) [95% confidence interval (CI), 89.0%-96.4%] with CAD-AFI and 94.9% (185/195) (95% CI, 90.8%-97.5%) with TME. The accuracy, sensitivity, specificity, and positive predictive value for differentiating diminutive rectosigmoid neoplastic polyps by CAD-AFI were 91.5%, 80.0%, 95.3% and 85.2%, respectively. Conclusions: Real-time CAD-AFI was effective for differentiating diminutive rectosigmoid polyps. This objective technology, which does not require extensive training or endoscopic expertise, can contribute to the effective management of diminutive rectosigmoid polyps.

Quantitative analysis of in vivo high-resolution microendoscopic images for the detection of neoplastic colorectal polyps

Colonoscopy is routinely performed for colorectal cancer screening but lacks the capability to accurately characterize precursor lesions and early cancers. High-resolution microendoscopy (HRME) is a low-cost imaging tool to visualize colorectal polyps with subcellular resolution. We present a computer-aided algorithm to evaluate HRME images of colorectal polyps and classify neoplastic from benign lesions. Using histopathology as the gold standard, clinically relevant features based on luminal morphology and texture are quantified to build the classification algorithm. We demonstrate that adenomatous polyps can be identified with a sensitivity and specificity of 100% and 80% using a two-feature linear discriminant model in a pilot test set. The classification algorithm presented here offers an objective framework to detect adenomatous lesions in the colon with high accuracy and can potentially improve real-time assessment of colorectal polyps.

Validation of Probe-based Confocal Laser Endomicroscopy (pCLE) Criteria for Diagnosing Colon Polyp Histology

BACKGROUND

Validated probe-based confocal endomicroscopy (pCLE) criteria for distinguishing hyperplastic polyps (HPs) and tubular adenomas (TA) have not yet been developed.

AIM

To develop pCLE criteria for distinguishing HP from TA and evaluate its performance characteristics among experts.

METHODS

pCLE criteria for colon polyp histology were developed and tested in 2 phases prospectively. Phase I: 8 preliminary criteria were developed and tested internally. Criteria achieving an accuracy of >75% (epithelial surface: regular vs. irregular; goblet cells: increased vs. decreased; gland axis: horizontal vs. vertical; gland shape: slit/stellate vs. villiform; image scale: gray vs. dark) were evaluated in Phase II of study wherein external assessors evaluated these criteria in a separate set of pCLE videos. Accuracy and interobserver agreement (95% confidence intervals) were determined for colon histology prediction.

RESULTS

Phase I (criteria development/internal testing): 8 criteria were assessed by 4 pCLE experts using 28 videos (14 HP/14 TA). Five of 8 pCLE criteria met selection for phase II (accuracy >75%). Phase II (external validation): 36 pCLE colon polyp videos (HP 16/TA 20) were evaluated by 8 external assessors. Overall accuracy in diagnosis of colon polyp histology was 84.9% (95% confidence interval, 81.7-87.7). Of predictions made with high confidence (75%), histology was predicted with an accuracy of 91%, sensitivity 83%, specificity 100%, negative predictive value 87% and positive predictive value 98%. Interobserver agreement was substantial (κ=0.73).

CONCLUSIONS

We demonstrate the development and validation of pCLE criteria for prediction of colon polyp histology. Using these criteria, overall accuracy in differentiating TA from HP was high with substantial interobserver agreement.

Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data

Purpose

Probe-based confocal laser endomicroscopy (pCLE) enables in vivo, in situ tissue characterisation without changes in the surgical setting and simplifies the oncological surgical workflow. The potential of this technique in identifying residual cancer tissue and improving resection rates of brain tumours has been recently verified in pilot studies. The interpretation of endomicroscopic information is challenging, particularly for surgeons who do not themselves routinely review histopathology. Also, the diagnosis can be examiner-dependent, leading to considerable inter-observer variability. Therefore, automatic tissue characterisation with pCLE would support the surgeon in establishing diagnosis as well as guide robot-assisted intervention procedures.

Methods

The aim of this work is to propose a deep learning-based framework for brain tissue characterisation for context aware diagnosis support in neurosurgical oncology. An efficient representation of the context information of pCLE data is presented by exploring state-of-the-art CNN models with different tuning configurations. A novel video classification framework based on the combination of convolutional layers with long-range temporal recursion has been proposed to estimate the probability of each tumour class. The video classification accuracy is compared for different network architectures and data representation and video segmentation methods.

Results

We demonstrate the application of the proposed deep learning framework to classify Glioblastoma and Meningioma brain tumours based on endomicroscopic data. Results show significant improvement of our proposed image classification framework over state-of-the-art feature-based methods. The use of video data further improves the classification performance, achieving accuracy equal to 99.49%.

Conclusions

This work demonstrates that deep learning can provide an efficient representation of pCLE data and accurately classify Glioblastoma and Meningioma tumours. The performance evaluation analysis shows the potential clinical value of the technique.

Implementation of real-time probe-based confocal laser endomicroscopy (pCLE) for differentiation of colorectal polyps during routine colonoscopy

BACKGROUND AND AIMS

 Probe-based confocal laser endomicroscopy (pCLE) is used to differentiate between neoplastic and non-neoplastic colorectal polyps during colonoscopy. We aimed to assess the accuracy of two endoscopists starting to use real-time pCLE for differentiation of colorectal polyps and to determine the negative predictive value (NPV) for neoplasia in polyps ≤ 5 mm.

METHODS

 Patients undergoing colonoscopy in a tertiary hospital were included in this prospective trial. After a training session, two colonoscopists assessed 50 polyps between August 2012 and April 2014. They sequentially used narrow-band imaging (NBI) and real-time pCLE to differentiate non-adenomatous, adenomatous, and carcinomatous polyps during colonoscopy. Histologic diagnosis by a gastrointestinal pathologist was the gold standard. Results were compared to post-hoc pCLE by a panel of gastroenterologists and pathologists.

RESULTS

 The accuracy of real-time pCLE was 76 %, compared to 73 % for NBI, and was not significantly different between the first 50 cases (74 %) and the last 50 cases (78 %, P  = 0.64). The accuracy in polyps > 5 mm was 87 % versus 59 % in polyps ≤ 5 mm ( P  = 0.04) and increased from 45 % (13/29) in poor quality images to 86 % (44/51) in fair quality images and 95 % (19/20) in good quality images ( P  < 0.01). The post-hoc pCLE accuracy was 62 %. The NPV for polyps ≤ 5 mm was 58 % for real-time pCLE and 54 % for post-hoc pCLE.

CONCLUSION

 Although a fair accuracy of real-time pCLE for differentiation of colorectal polyps can be achieved within 50 cases, low NPV and difficulty in obtaining high-quality pCLE images hamper implementation in routine clinical practice.

Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning

Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%).

Confocal Laser Endomicroscopy in Gastrointestinal and Pancreatobiliary Diseases: A Systematic Review and Meta-Analysis

Confocal laser endomicroscopy (CLE) is an endoscopic-assisted technique developed to obtain histopathological diagnoses of gastrointestinal and pancreatobiliary diseases in real time. The objective of this systematic review is to analyze the current literature on CLE and to evaluate the applicability and diagnostic yield of CLE in patients with gastrointestinal and pancreatobiliary diseases. A literature search was performed on MEDLINE, EMBASE, Scopus, and Cochrane Oral Health Group Specialized Register, using pertinent keywords without time limitations. Both prospective and retrospective clinical studies that evaluated the sensitivity, specificity, or accuracy of CLE were eligible for inclusion. Of 662 articles identified, 102 studies were included in the systematic review. The studies were conducted between 2004 and 2015 in 16 different countries. CLE demonstrated high sensitivity and specificity in the detection of dysplasia in Barrett's esophagus, gastric neoplasms and polyps, colorectal cancers in inflammatory bowel disease, malignant pancreatobiliary strictures, and pancreatic cysts. Although CLE has several promising applications, its use has been limited by its low availability, high cost, and need of specific operator training. Further clinical trials with a particular focus on cost-effectiveness and medicoeconomic analyses, as well as standardized institutional training, are advocated to implement CLE in routine clinical practice.

Automated analysis of confocal laser endomicroscopy images to detect head and neck cancer

BACKGROUND

The purpose of this study was to develop an automated image analysis algorithm to discriminate between head and neck cancer and nonneoplastic epithelium in confocal laser endomicroscopy (CLE) images.

METHODS

CLE was applied to image head and neck cancer epithelium in vivo. Histopathologic diagnosis from biopsies was used to classify the CLE images offline as cancer or noncancer tissue. The classified images were used to train automated software based on distance map histograms. The performance of the final algorithm was confirmed by "leave 2 patients out" cross-validation and area under the curve (AUC)/receiver operating characteristic (ROC) analysis.

RESULTS

Ninety-two CLE videos and 92 biopsies were analyzed from 12 patients. One hundred two frames of classified neoplastic tissue and 52 frames of nonneoplastic tissue were used for cross-validation of the developed algorithm. AUC varied from 0.52 to 0.92.

CONCLUSION

The proposed software allows an objective classification of CLE images of head and neck cancer and adjacent nonneoplastic epithelium. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1419-E1426, 2016.