Effectiveness of a Deep-learning Polyp Detection System in Prospectively Collected Colonoscopy Videos With Variable Bowel Preparation Quality

Assessment of the role of false-positive alerts in computer-aided polyp detection for assistance capabilities

BACKGROUND AND AIM

False positives (FPs) pose a significant challenge in the application of artificial intelligence (AI) for polyp detection during colonoscopy. The study aimed to quantitatively evaluate the impact of computer-aided polyp detection (CADe) systems' FPs on endoscopists.

METHODS

The model's FPs were categorized into four gradients: 0-5, 5-10, 10-15, and 15-20 FPs per minute (FPPM). Fifty-six colonoscopy videos were collected for a crossover study involving 10 endoscopists. Polyp missed rate (PMR) was set as primary outcome. Subsequently, to further verify the impact of FPPM on the assistance capability of AI in clinical environments, a secondary analysis was conducted on a prospective randomized controlled trial (RCT) from Renmin Hospital of Wuhan University in China from July 1 to October 15, 2020, with the adenoma detection rate (ADR) as primary outcome.

RESULTS

Compared with routine group, CADe reduced PMR when FPPM was less than 5. However, with the continuous increase of FPPM, the beneficial effect of CADe gradually weakens. For secondary analysis of RCT, a total of 956 patients were enrolled. In AI-assisted group, ADR is higher when FPPM ≤ 5 compared with FPPM > 5 (CADe group: 27.78% vs 11.90%; P = 0.014; odds ratio [OR], 0.351; 95% confidence interval [CI], 0.152-0.812; COMBO group: 38.40% vs 23.46%, P = 0.029; OR, 0.427; 95% CI, 0.199-0.916). After AI intervention, ADR increased when FPPM ≤ 5 (27.78% vs 14.76%; P = 0.001; OR, 0.399; 95% CI, 0.231-0.690), but no statistically significant difference was found when FPPM > 5 (11.90% vs 14.76%, P = 0.788; OR, 1.111; 95% CI, 0.514-2.403).

CONCLUSION

The level of FPs of CADe does affect its effectiveness as an aid to endoscopists, with its best effect when FPPM is less than 5.

Evaluating false-positive detection in a computer-aided detection system for colonoscopy

BACKGROUND AND AIM

Computer-aided detection (CADe) systems can efficiently detect polyps during colonoscopy. However, false-positive (FP) activation is a major limitation of CADe. We aimed to compare the rate and causes of FP using CADe before and after an update designed to reduce FP.

METHODS

We analyzed CADe-assisted colonoscopy videos recorded between July 2022 and October 2022. The number and causes of FPs and excessive time spent by the endoscopist on FP (ET) were compared pre- and post-update using 1:1 propensity score matching.

RESULTS

During the study period, 191 colonoscopy videos (94 and 97 in the pre- and post-update groups, respectively) were recorded. Propensity score matching resulted in 146 videos (73 in each group). The mean number of FPs and median ET per colonoscopy were significantly lower in the post-update group than those in the pre-update group (4.2 ± 3.7 vs 18.1 ± 11.1; P < 0.001 and 0 vs 16 s; P < 0.001, respectively). Mucosal tags, bubbles, and folds had the strongest association with decreased FP post-update (pre-update vs post-update: 4.3 ± 3.6 vs 0.4 ± 0.8, 0.32 ± 0.70 vs 0.04 ± 0.20, and 8.6 ± 6.7 vs 1.6 ± 1.7, respectively). There was no significant decrease in the true positive rate (post-update vs pre-update: 95.0% vs 99.2%; P = 0.09) or the adenoma detection rate (post-update vs pre-update: 52.1% vs 49.3%; P = 0.87).

CONCLUSIONS

The updated CADe can reduce FP without impairing polyp detection. A reduction in FP may help relieve the burden on endoscopists.

Recent advances in devices and technologies that might prove revolutionary for colonoscopy procedures

INTRODUCTION

Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR.

AREAS COVERED

The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies.

EXPERT OPINION

Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy.

Deep Learning Empowers Endoscopic Detection and Polyps Classification: A Multiple-Hospital Study

The present study aimed to develop an AI-based system for the detection and classification of polyps using colonoscopy images. A total of about 256,220 colonoscopy images from 5000 colorectal cancer patients were collected and processed. We used the CNN model for polyp detection and the EfficientNet-b0 model for polyp classification. Data were partitioned into training, validation and testing sets, with a 70%, 15% and 15% ratio, respectively. After the model was trained/validated/tested, to evaluate its performance rigorously, we conducted a further external validation using both prospective (n = 150) and retrospective (n = 385) approaches for data collection from 3 hospitals. The deep learning model performance with the testing set reached a state-of-the-art sensitivity and specificity of 0.9709 (95% CI: 0.9646-0.9757) and 0.9701 (95% CI: 0.9663-0.9749), respectively, for polyp detection. The polyp classification model attained an AUC of 0.9989 (95% CI: 0.9954-1.00). The external validation from 3 hospital results achieved 0.9516 (95% CI: 0.9295-0.9670) with the lesion-based sensitivity and a frame-based specificity of 0.9720 (95% CI: 0.9713-0.9726) for polyp detection. The model achieved an AUC of 0.9521 (95% CI: 0.9308-0.9734) for polyp classification. The high-performance, deep-learning-based system could be used in clinical practice to facilitate rapid, efficient and reliable decisions by physicians and endoscopists.

Effectiveness of artificial intelligence-assisted colonoscopy in early diagnosis of colorectal cancer: a systematic review

INTRODUCTION

As artificial intelligence (AI)-assisted diagnosis gained immense popularity, it is imperative to consider its utility and efficiency in the early diagnosis of colorectal cancer (CRC), responsible for over 1.8 million cases and 881 000 deaths globally, as reported in 2018. Improved adenoma detection rate, as well as better characterizations of polyps, are significant advantages of AI-assisted colonoscopy (AIC). This systematic review (SR) investigates the effectiveness of AIC in the early diagnosis of CRC as compared to conventional colonoscopy.

MATERIALS AND METHODS

Electronic databases such as PubMed/Medline, SCOPUS, and Web of Science were reviewed for original studies (randomized controlled trials, observational studies), SRs, and meta-analysis between 2017 and 2022 utilizing Medical Subject Headings terminology in a broad search strategy. All searches were performed and analyzed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology and were conducted from November 2022. A data extraction form based on the Cochrane Consumers and Communication Review group's extraction template for quality assessment and evidence synthesis was used for data extraction. All included studies considered for bias and ethical criteria and provided valuable evidence to answer the research question.

RESULTS

The database search identified 218 studies, including 87 from PubMed, 60 from SCOPUS, and 71 from Web of Science databases. The retrieved studies from the databases were imported to Rayyan software and a duplicate article check was performed, all duplicate articles were removed after careful evaluation of the data. The abstract and full-text screening was performed in accordance with the following eligibility criteria: Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) for observational studies; Preferred Reporting Items for Systematic Reviews and Meta-Analysis for review articles, ENTREQ for narrative studies; and modified JADAD for randomized controlled trials. This yielded 15 studies that met the requirements for this SR and were finally included in the review.

CONCLUSION

AIC is a safe, highly effective screening tool that can increase the detection rate of adenomas, and polyps resulting in an early diagnosis of CRC in adults when compared to conventional colonoscopy. The results of this SR prompt further large-scale research to investigate the effectiveness in accordance with sex, race, and socioeconomic status, as well as its influence on prognosis and survival rate.

Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore

BACKGROUND

Colonoscopy is a mainstay to detect premalignant neoplastic lesions in the colon. Real-time Artificial Intelligence (AI)-aided colonoscopy purportedly improves the polyp detection rate, especially for small flat lesions. The aim of this study is to evaluate the performance of real-time AI-aided colonoscopy in the detection of colonic polyps.

METHODS

A prospective single institution cohort study was conducted in Singapore. All real-time AI-aided colonoscopies, regardless of indication, performed by specialist-grade endoscopists were anonymously recorded from July to September 2021 and reviewed by 2 independent authors (FHK, JL). Sustained detection of an area by the program was regarded as a "hit". Histology for the polypectomies were reviewed to determine adenoma detection rate (ADR). Individual endoscopist's performance with AI were compared against their baseline performance without AI endoscopy.

RESULTS

A total of 24 (82.8%) endoscopists participated with 18 (62.1%) performing ≥ 5 AI-aided colonoscopies. Of the 18, 72.2% (n = 13) were general surgeons. During that 3-months period, 487 "hits" encountered in 298 colonoscopies. Polypectomies were performed for 51.3% and 68.4% of these polypectomies were adenomas on histology. The post-intervention median ADR was 30.4% was higher than the median baseline polypectomy rate of 24.3% (p = 0.02). Of the adenomas excised, 14 (5.6%) were sessile serrated adenomas. Of those who performed ≥ 5 AI-aided colonoscopies, 13 (72.2%) had an improvement of ADR compared to their polypectomy rate before the introduction of AI, of which 2 of them had significant improvement.

CONCLUSIONS

Real-time AI-aided colonoscopy have the potential to improved ADR even for experienced endoscopists and would therefore, improve the quality of colonoscopy.

Artificial intelligence in gastroenterology: A narrative review

Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.

Mucosal imaging in colon polyps: New advances and what the future may hold

An expanding range of advanced mucosal imaging technologies have been developed with the goal of improving the detection and characterization of lesions in the gastrointestinal tract. Many technologies have targeted colorectal neoplasia given the potential for intervention prior to the development of invasive cancer in the setting of widespread surveillance programs. Improvement in adenoma detection reduces miss rates and prevents interval cancer development. Advanced imaging technologies aim to enhance detection without significantly increasing procedural time. Accurate polyp characterisation guides resection techniques for larger polyps, as well as providing the platform for the “resect and discard” and “do not resect” strategies for small and diminutive polyps. This review aims to collate and summarise the evidence regarding these technologies to guide colonoscopic practice in both interventional and non-interventional endoscopists.

Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis

Background

Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease.

Methods

We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. We performed a meta-analysis and hierarchical summary receiver operating characteristic curves (HSROC). The risk of bias was assessed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Subgroup analyses were conducted based on the type of GI luminal disease, AI model, reference standard, and type of data used for analysis. This study is registered with PROSPERO (CRD42021288360).

Findings

We included 73 studies, of which 31 were externally validated and provided sufficient information for inclusion in the meta-analysis. The overall sensitivity of AI for detecting GI luminal pathologies was 91.9% (95% CI: 89.0–94.1) and specificity was 91.7% (95% CI: 87.4–94.7). Deep learning models (sensitivity: 89.8%, specificity: 91.9%) and ensemble methods (sensitivity: 95.4%, specificity: 90.9%) were the most commonly used models in the included studies. Majority of studies (n = 56, 76.7%) had a high risk of selection bias while 74% (n = 54) studies were low risk on reference standard and 67% (n = 49) were low risk for flow and timing bias.

Interpretation

The review suggests high sensitivity and specificity of AI models for the detection of GI luminal pathologies. There is a need for large, multi-center trials in both high income countries and low- and middle- income countries to assess the performance of these AI models in real clinical settings and its impact on diagnosis and prognosis.

Systematic review registration

[https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=288360], identifier [CRD42021288360].

Examining the effect of synthetic data augmentation in polyp detection and segmentation

PURPOSE

As with several medical image analysis tasks based on deep learning, gastrointestinal image analysis is plagued with data scarcity, privacy concerns and an insufficient number of pathology samples. This study examines the generation and utility of synthetic samples of colonoscopy images with polyps for data augmentation.

METHODS

We modify and train a pix2pix model to generate synthetic colonoscopy samples with polyps to augment the original dataset. Subsequently, we create a variety of datasets by varying the quantity of synthetic samples and traditional augmentation samples, to train a U-Net network and Faster R-CNN model for segmentation and detection of polyps, respectively. We compare the performance of the models when trained with the resulting datasets in terms of F₁ score, intersection over union, precision and recall. Further, we compare the performances of the models with unseen polyp datasets to assess their generalization ability.

RESULTS

The average F₁ coefficient and intersection over union are improved with increasing number of synthetic samples in U-Net over all test datasets. The performance of the Faster R-CNN model is also improved in terms of polyp detection, while decreasing the false-negative rate. Further, the experimental results for polyp detection outperform similar studies in the literature on the ETIS-PolypLaribDB dataset.

CONCLUSION

By varying the quantity of synthetic and traditional augmentation, there is the potential to control the sensitivity of deep learning models in polyp segmentation and detection. Further, GAN-based augmentation is a viable option for improving the performance of models for polyp segmentation and detection.

Artificial intelligence in endoscopy: More than what meets the eye in screening colonoscopy and endosonographic evaluation of pancreatic lesions

Artificial intelligence (AI)-based tools have ushered in a new era of innovation in the field of gastrointestinal (GI) endoscopy. Despite vast improvements in endoscopic techniques and equipment, diagnostic endoscopy remains heavily operator-dependent, in particular, colonoscopy and endoscopic ultrasound (EUS). Recent reports have shown that as much as 25% of colonic adenomas may be missed at colonoscopy. This can result in an increased incidence of interval colon cancer. Similarly, EUS has been shown to have high inter-observer variability, overlap in diagnoses with a relatively low specificity for pancreatic lesions. Our understanding of Machine-learning (ML) techniques in AI have evolved over the last decade and its application in AI–based tools for endoscopic detection and diagnosis is being actively investigated at several centers. ML is an aspect of AI that is based on neural networks, and is widely used for image classification, object detection, and semantic segmentation which are key functional aspects of AI-related computer aided diagnostic systems. In this review, current status and limitations of ML, specifically for adenoma detection and endosonographic diagnosis of pancreatic lesions, will be summarized from existing literature. This will help to better understand its role as viewed through the prism of real world application in the field of GI endoscopy.

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.

Artificial intelligence and colonoscopy − enhancements and improvements

Artificial intelligence is a technology that processes and analyzes information with reproducibility and accuracy. Its application in medicine, especially in the field of gastroenterology, has great potential to facilitate in diagnosis of various disease states. Currently, the role of artificial intelligence as it pertains to colonoscopy revolves around enhanced polyp detection and characterization. The aim of this article is to review the current and potential future applications of artificial intelligence for enhanced quality of detection for colorectal neoplasia.

Diagnostic Accuracy of Artificial Intelligence and Computer-Aided Diagnosis for the Detection and Characterization of Colorectal Polyps: Systematic Review and Meta-analysis

BACKGROUND

Colonoscopy reduces the incidence of colorectal cancer (CRC) by allowing detection and resection of neoplastic polyps. Evidence shows that many small polyps are missed on a single colonoscopy. There has been a successful adoption of artificial intelligence (AI) technologies to tackle the issues around missed polyps and as tools to increase the adenoma detection rate (ADR).

OBJECTIVE

The aim of this review was to examine the diagnostic accuracy of AI-based technologies in assessing colorectal polyps.

METHODS

A comprehensive literature search was undertaken using the databases of Embase, MEDLINE, and the Cochrane Library. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. Studies reporting the use of computer-aided diagnosis for polyp detection or characterization during colonoscopy were included. Independent proportions and their differences were calculated and pooled through DerSimonian and Laird random-effects modeling.

RESULTS

A total of 48 studies were included. The meta-analysis showed a significant increase in pooled polyp detection rate in patients with the use of AI for polyp detection during colonoscopy compared with patients who had standard colonoscopy (odds ratio [OR] 1.75, 95% CI 1.56-1.96; P<.001). When comparing patients undergoing colonoscopy with the use of AI to those without, there was also a significant increase in ADR (OR 1.53, 95% CI 1.32-1.77; P<.001).

CONCLUSIONS

With the aid of machine learning, there is potential to improve ADR and, consequently, reduce the incidence of CRC. The current generation of AI-based systems demonstrate impressive accuracy for the detection and characterization of colorectal polyps. However, this is an evolving field and before its adoption into a clinical setting, AI systems must prove worthy to patients and clinicians.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42020169786; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020169786.

Computer-Aided Detection False Positives in Colonoscopy

Randomized control trials and meta-analyses comparing colonoscopies with and without computer-aided detection (CADe) assistance showed significant increases in adenoma detection rates (ADRs) with CADe. A major limitation of CADe is its false positives (FPs), ranked 3rd in importance among 59 research questions in a modified Delphi consensus review. The definition of FPs varies. One commonly used definition defines an FP as an activation of the CADe system, irrespective of the number of frames or duration of time, not due to any polypoid or nonpolypoid lesions. Although only 0.07 to 0.2 FPs were observed per colonoscopy, video analysis studies using FPs as the primary outcome showed much higher numbers of 26 to 27 per colonoscopy. Most FPs were of short duration (91% < 0.5 s). A higher number of FPs was also associated with suboptimal bowel preparation. The appearance of FPs can lead to user fatigue. The polypectomy of FPs results in increased procedure time and added use of resources. Re-training the CADe algorithms is one way to reduce FPs but is not practical in the clinical setting during colonoscopy. Water exchange (WE) is an emerging method that the colonoscopist can use to provide salvage cleaning during insertion. We discuss the potential of WE for reducing FPs as well as the augmentation of ADRs through CADe.

Medical needs related to the endoscopic technology and colonoscopy for colorectal cancer diagnosis

Background

The high incidence and mortality rate of colorectal cancer require new technologies to improve its early diagnosis. This study aims at extracting the medical needs related to the endoscopic technology and the colonoscopy procedure currently used for colorectal cancer diagnosis, essential for designing these demanded technologies.

Methods

Semi-structured interviews and an online survey were used.

Results

Six endoscopists were interviewed and 103 were surveyed, obtaining the demanded needs that can be divided into: a) clinical needs, for better polyp detection and classification (especially flat polyps), location, size, margins and penetration depth; b) computer-aided diagnosis (CAD) system needs, for additional visual information supporting polyp characterization and diagnosis; and c) operational/physical needs, related to limitations of image quality, colon lighting, flexibility of the endoscope tip, and even poor bowel preparation.

Conclusions

This study shows some undertaken initiatives to meet the detected medical needs and challenges to be solved. The great potential of advanced optical technologies suggests their use for a better polyp detection and classification since they provide additional functional and structural information than the currently used image enhancement technologies. The inspection of remaining tissue of diminutive polyps (< 5 mm) should be addressed to reduce recurrence rates. Few progresses have been made in estimating the infiltration depth. Detection and classification methods should be combined into one CAD system, providing visual aids over polyps for detection and displaying a Kudo-based diagnosis suggestion to assist the endoscopist on real-time decision making. Estimated size and location of polyps should also be provided. Endoscopes with 360° vision are still a challenge not met by the mechanical and optical systems developed to improve the colon inspection. Patients and healthcare providers should be trained to improve the patient’s bowel preparation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08190-z.

A novel machine learning-based algorithm to identify and classify lesions and anatomical landmarks in colonoscopy images

OBJECTIVES

Computer-aided diagnosis (CAD)-based artificial intelligence (AI) has been shown to be highly accurate for detecting and characterizing colon polyps. However, the application of AI to identify normal colon landmarks and differentiate multiple colon diseases has not yet been established. We aimed to develop a convolutional neural network (CNN)-based algorithm (GUTAID) to recognize different colon lesions and anatomical landmarks.

METHODS

Colonoscopic images were obtained to train and validate the AI classifiers. An independent dataset was collected for verification. The architecture of GUTAID contains two major sub-models: the Normal, Polyp, Diverticulum, Cecum and CAncer (NPDCCA) and Narrow-Band Imaging for Adenomatous/Hyperplastic polyps (NBI-AH) models. The development of GUTAID was based on the 16-layer Visual Geometry Group (VGG16) architecture and implemented on Google Cloud Platform.

RESULTS

In total, 7838 colonoscopy images were used for developing and validating the AI model. An additional 1273 images were independently applied to verify the GUTAID. The accuracy for GUTAID in detecting various colon lesions/landmarks is 93.3% for polyps, 93.9% for diverticula, 91.7% for cecum, 97.5% for cancer, and 83.5% for adenomatous/hyperplastic polyps.

CONCLUSIONS

A CNN-based algorithm (GUTAID) to identify colonic abnormalities and landmarks was successfully established with high accuracy. This GUTAID system can further characterize polyps for optical diagnosis. We demonstrated that AI classification methodology is feasible to identify multiple and different colon diseases.

Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning

Background and study aims  Storage of full-length endoscopic procedures is becoming increasingly popular. To facilitate large-scale machine learning (ML) focused on clinical outcomes, these videos must be merged with the patient-level data in the electronic health record (EHR). Our aim was to present a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos. Methods  This study was conducted at a single academic medical center. Most procedure videos are automatically uploaded to the cloud server but are identified only by procedure time and procedure room. We developed and then tested an algorithm to match recorded videos with corresponding exams in the EHR based upon procedure time and room and subsequently extract frames of interest. Results  Among 28,611 total colonoscopies performed over the study period, 21,170 colonoscopy videos in 20,420 unique patients (54.2 % male, median age 58) were matched to EHR data. Of 100 randomly sampled videos, appropriate matching was manually confirmed in all. In total, these videos represented 489,721 minutes of colonoscopy performed by 50 endoscopists (median 214 colonoscopies per endoscopist). The most common procedure indications were polyp screening (47.3 %), surveillance (28.9 %) and inflammatory bowel disease (9.4 %). From these videos, we extracted procedure highlights (identified by image capture; mean 8.5 per colonoscopy) and surrounding frames. Conclusions  We report the successful merging of a large database of endoscopy videos stored with limited identifiers to rich patient-level data in a highly accurate manner. This technique facilitates the development of ML algorithms based upon relevant patient outcomes.

A review of water exchange and artificial intelligence in improving adenoma detection

Water exchange (WE) and artificial intelligence (AI) have made critical advances during the past decade. WE significantly increases adenoma detection and AI holds the potential to help endoscopists detect more polyps and adenomas. We performed an electronic literature search on PubMed using the following keywords: water-assisted and water exchange colonoscopy, adenoma and polyp detection, artificial intelligence, deep learning, neural networks, and computer-aided colonoscopy. We reviewed relevant articles published in English from 2010 to May 2020. Additional articles were searched manually from the reference lists of the publications reviewed. We discussed recent advances in both WE and AI, including their advantages and limitations. AI may mitigate operator-dependent factors that limit the potential of WE. By increasing bowel cleanliness and improving visualization, WE may provide the platform to optimize the performance of AI for colonoscopies. The strengths of WE and AI may complement each other in spite of their weaknesses to maximize adenoma detection.

Potential applications of artificial intelligence in colorectal polyps and cancer: Recent advances and prospects

Since the advent of artificial intelligence (AI) technology, it has been constantly studied and has achieved rapid development. The AI assistant system is expected to improve the quality of automatic polyp detection and classification. It could also help prevent endoscopists from missing polyps and make an accurate optical diagnosis. These functions provided by AI could result in a higher adenoma detection rate and decrease the cost of polypectomy for hyperplastic polyps. In addition, AI has good performance in the staging, diagnosis, and segmentation of colorectal cancer. This article provides an overview of recent research focusing on the application of AI in colorectal polyps and cancer and highlights the advances achieved.