Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial

Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method

BACKGROUND : Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. METHODS : An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. RESULTS : The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. CONCLUSIONS : This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.

GIE Editorial Board top 10 topics: advances in GI endoscopy in 2020

The American Society for Gastrointestinal Endoscopy's Gastrointestinal Endoscopy Editorial Board reviewed a systematic literature search of original endoscopy-related articles published during 2020 in Gastrointestinal Endoscopy and 10 other high-impact medical and gastroenterology journals. Votes from each individual board member were tallied to identify a consensus list of the 10 most significant topic areas in GI endoscopy over the calendar year of study using 4 criteria: significance, novelty, impact on national health, and impact on global health. The 10 areas identified were as follows: artificial intelligence in endoscopy, coronavirus disease 2019 and GI practice, third-space endoscopy, lumen-apposing metal stents, single-use duodenoscopes and other disposable equipment, endosonographic needle technology and techniques, endoscopic closure devices, advances in GI bleeding management, improvements in polypectomy techniques, and bariatric endoscopy. Each board member contributed a summary of important articles relevant to 1 to 2 topic areas, leading to a collective summary that is presented in this document of the "top 10" endoscopic advances of 2020.

Updates in artificial intelligence in gastroenterology endoscopy in 2020

PURPOSE OF REVIEW

Artificial intelligence is becoming rapidly integrated into modern technology including medicine. Artificial intelligence has a wide range of potential in gastroenterology, particularly with endoscopy, given the required analysis of large datasets of images. The aim of this review is to summarize the advances of artificial intelligence in gastroenterology (GI) endoscopy over the past year.

RECENT FINDINGS

Computer-aided detection (CADe) systems during real-time colonoscopy have resulted in increased adenoma detection rate with no significant increase in procedure times. Deep learning techniques have been utilized to accurately assess bowel preparation quality, which would impact surveillance colonoscopy recommendations. For the upper GI tract, CADe systems have been developed to aid in improving the diagnosis of Barrett's neoplasia during real-time endoscopy. Artificial intelligence-assisted real-time endoscopy has been shown to reduce blind spots during EGD.

SUMMARY

The application of artificial intelligence in gastroenterology endoscopy remains promising. Advances over the past year include improved detection of GI neoplasia during endoscopy and characterization of lesions. Further research including randomized, multicenter trials are needed to further evaluate the use of artificial intelligence for real-time 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.

Early gastrointestinal cancer: The application of artificial intelligence

Early gastrointestinal (GI) cancer has been the core of clinical endoscopic work. Its early detection and treatment are tightly associated with patients’ prognoses. As a novel technology, artificial intelligence has been improved and applied in the field of endoscopy. Studies on detection, diagnosis, risk, and prognosis evaluation of diseases in the GI tract have been in development, including precancerous lesions, adenoma, early GI cancers, and advanced GI cancers. In this review, research on esophagus, stomach, and colon was concluded, and associated with the process from precancerous lesions to early GI cancer, such as from Barrett’s esophagus to early esophageal cancer, from dysplasia to early gastric cancer, and from adenoma to early colonic cancer. A status quo of research on early GI cancers and artificial intelligence was provided.

Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning

BACKGROUND

Artificial intelligence in colonoscopy is an emerging field, and its application may help colonoscopists improve inspection quality and reduce the rate of missed polyps and adenomas. Several deep learning-based computer-assisted detection (CADe) techniques were established from small single-center datasets, and unrepresentative learning materials might confine their application and generalization in wide practice. Although CADes have been reported to identify polyps in colonoscopic images and videos in real time, their diagnostic performance deserves to be further validated in clinical practice.

AIM

To train and test a CADe based on multicenter high-quality images of polyps and preliminarily validate it in clinical colonoscopies.

METHODS

With high-quality screening and labeling from 55 qualified colonoscopists, a dataset consisting of over 71000 images from 20 centers was used to train and test a deep learning-based CADe. In addition, the real-time diagnostic performance of CADe was tested frame by frame in 47 unaltered full-ranged videos that contained 86 histologically confirmed polyps. Finally, we conducted a self-controlled observational study to validate the diagnostic performance of CADe in real-world colonoscopy with the main outcome measure of polyps per colonoscopy in Changhai Hospital.

RESULTS

The CADe was able to identify polyps in the test dataset with 95.0% sensitivity and 99.1% specificity. For colonoscopy videos, all 86 polyps were detected with 92.2% sensitivity and 93.6% specificity in frame-by-frame analysis. In the prospective validation, the sensitivity of CAD in identifying polyps was 98.4% (185/188). Folds, reflections of light and fecal fluid were the main causes of false positives in both the test dataset and clinical colonoscopies. Colonoscopists can detect more polyps (0.90 vs 0.82, P < 0.001) and adenomas (0.32 vs 0.30, P = 0.045) with the aid of CADe, particularly polyps < 5 mm and flat polyps (0.65 vs 0.57, P < 0.001; 0.74 vs 0.67, P = 0.001, respectively). However, high efficacy is not realized in colonoscopies with inadequate bowel preparation and withdrawal time (P = 0.32; P = 0.16, respectively).

CONCLUSION

CADe is feasible in the clinical setting and might help endoscopists detect more polyps and adenomas, and further confirmation is warranted.

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.

Computer-aided detection versus advanced imaging for detection of colorectal neoplasia: a systematic review and network meta-analysis

BACKGROUND

Computer-aided detection (CADe) techniques based on artificial intelligence algorithms can assist endoscopists in detecting colorectal neoplasia. CADe has been associated with an increased adenoma detection rate, a key quality indicator, but the utility of CADe compared with existing advanced imaging techniques and distal attachment devices is unclear.

METHODS

For this systematic review and network meta-analysis, we did a comprehensive search of PubMed/Medline, Embase, and Scopus databases from inception to Nov 30, 2020, for randomised controlled trials investigating the effectiveness of the following endoscopic techniques in detecting colorectal neoplasia: CADe, high definition (HD) white-light endoscopy, chromoendoscopy, or add-on devices (ie, systems that increase mucosal visualisation, such as full spectrum endoscopy [FUSE] or G-EYE balloon endoscopy). We collected data on adenoma detection rates, sessile serrated lesion detection rates, the proportion of large adenomas detected per colonoscopy, and withdrawal times. A frequentist framework, random-effects network meta-analysis was done to compare artificial intelligence with chromoendoscopy, increased mucosal visualisation systems, and HD white-light endoscopy (the control group). We estimated odds ratios (ORs) for the adenoma detection rate, sessile serrated lesion detection rate, and proportion of large adenomas detected per colonoscopy, and calculated mean differences for withdrawal time, with 95% CIs. Risk of bias and certainty of evidence were assessed with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.

FINDINGS

50 randomised controlled trials, comprising 34 445 participants, were included in our main analysis (six trials of CADe, 18 of chromoendoscopy, and 26 of increased mucosal visualisation systems). HD white-light endoscopy was the control technique in all 50 studies. Compared with the control technique, the adenoma detection rate was 7·4% higher with CADe (OR 1·78 [95% CI 1·44-2·18]), 4·4% higher with chromoendoscopy (1·22 [1·08-1·39]), and 4·1% higher with increased mucosal visualisation systems (1·16 [1·04-1·28]). CADe ranked as the superior technique for adenoma detection (with moderate confidence in hierarchical ranking); cross-comparisons of CADe with other imaging techniques showed a significant increase in the adenoma detection rate with CADe versus increased mucosal visualisation systems (OR 1·54 [95% CI 1·22-1·94]; low certainty of evidence) and with CADe versus chromoendoscopy (1·45 [1·14-1·85]; moderate certainty of evidence). When focusing on large adenomas (≥10 mm) there was a significant increase in the detection of large adenomas only with CADe (OR 1·69 [95% CI 1·10-2·60], moderate certainty of evidence) when compared to HD white-light endoscopy; CADe ranked as the superior strategy for detection of large adenomas. CADe also seemed to be the superior strategy for detection of sessile serrated lesions (with moderate confidence in hierarchical ranking), although no significant increase in the sessile serrated lesion detection rate was shown (OR 1·37 [95% CI 0·65-2·88]). No significant difference in withdrawal time was reported for CADe compared with the other techniques.

INTERPRETATION

Based on the published literature, detection rates of colorectal neoplasia are higher with CADe than with other techniques such as chromoendoscopy or tools that increase mucosal visualisation, supporting wider incorporation of CADe strategies into community endoscopy services.

FUNDING

None.

Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial

BACKGROUND

We have developed the computer-aided detection (CADe) system using an original deep learning algorithm based on a convolutional neural network for assisting endoscopists in detecting colorectal lesions during colonoscopy. The aim of this study was to clarify whether adenoma miss rate (AMR) could be reduced with CADe assistance during screening and surveillance colonoscopy.

METHODS

This study was a multicenter randomized controlled trial. Patients aged 40 to 80 years who were referred for colorectal screening or surveillance at four sites in Japan were randomly assigned at a 1:1 ratio to either the "standard colonoscopy (SC)-first group" or the "CADe-first group" to undergo a back-to-back tandem procedure. Tandem colonoscopies were performed on the same day for each participant by the same endoscopist in a preassigned order. All polyps detected in each pass were histopathologically diagnosed after biopsy or resection.

RESULTS

A total of 358 patients were enrolled and 179 patients were assigned to the SC-first group or CADe-first group. The AMR of the CADe-first group was significantly lower than that of the SC-first group (13.8% vs. 36.7%, P < 0.0001). Similar results were observed for the polyp miss rate (14.2% vs. 40.6%, P < 0.0001) and sessile serrated lesion miss rate (13.0% vs. 38.5%, P = 0.03). The adenoma detection rate of CADe-assisted colonoscopy was 64.5%, which was significantly higher than that of standard colonoscopy (53.6%; P = 0.036).

CONCLUSION

Our study results first showed a reduction in the AMR when assisting with CADe based on deep learning in a multicenter randomized controlled trial.

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.

Risk of Covert Submucosal Cancer in Patients With Granular Mixed Laterally Spreading Tumors

BACKGROUND AND AIMS

Granular mixed laterally spreading tumors (GM-LSTs) have an intermediate level of risk for submucosal invasive cancer (SMICs) without clear signs of invasion (covert); the optimal resection method is uncertain. We aimed to determine the risk of covert SMIC in GM-LSTs based on clinical and endoscopic factors.

METHODS

We collected data from 693 patients (50.6% male; median age, 69 years) with colorectal GM-LSTs, without signs of invasion, who underwent endoscopic resection (74.2%) or endoscopic submucosal dissection (25.2%) at 7 centers in Italy from 2016 through 2019. We performed multivariate and univariate analyses to identify demographic and endoscopic factors associated with risk of SMIC. We developed a multivariate model to calculate the number needed to treat (NNT) to detect 1 SMIC.

RESULTS

Based on pathology analysis, 66 patients (9.5%) had covert SMIC. In multivariate analyses, increased risk of covert SMIC were independently associated with increasing lesion size (odds ratio per mm increase, 1.02, 95% CI, 1.01-1.03; P = .003) and rectal location (odds ratio, 3.08; 95% CI, 1.62-5.83; P = .004). A logistic regression model based on lesion size (with a cutoff of 40 mm) and rectal location identified patients with covert SMIC with 47.0% sensitivity, 82.6% specificity, and an area under the curve of 0.69. The NNT to identify 1 patient with a nonrectal SMIC smaller than 4 cm was 20; the NNT to identify 1 patient with a rectal SMIC of 4 cm or more was 5.

CONCLUSIONS

In an analysis of data from 693 patients, we found the risk of covert SMIC in patients with GM-LSTs to be approximately 10%. GM-LSTs of 4 cm or more and a rectal location are high risk and should be treated by en-bloc resection. ClinicalTrials.gov, Number: NCT03836131.

Artificial intelligence for identification and characterization of colonic polyps

Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the “resect-and-discard” and “diagnose-and-leave” strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence–assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists’ optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of “resect-and-discard” and “diagnose-and-leave” strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.

Utility of artificial intelligence in colonoscopy

Colorectal cancer is one of the major causes of death worldwide. Colonoscopy is the most important tool that can identify neoplastic lesion in early stages and resect it in a timely manner which helps in reducing mortality related to colorectal cancer. However, the quality of colonoscopy findings depends on the expertise of the endoscopist and thus the rate of missed adenoma or polyp cannot be controlled. It is desirable to standardize the quality of colonoscopy by reducing the number of missed adenoma/polyps. Introduction of artificial intelligence (AI) in the field of medicine has become popular among physicians nowadays. The application of AI in colonoscopy can help in reducing miss rate and increasing colorectal cancer detection rate as per recent studies. Moreover, AI assistance during colonoscopy has also been utilized in patients with inflammatory bowel disease to improve diagnostic accuracy, assessing disease severity and predicting clinical outcomes. We conducted a literature review on the available evidence on use of AI in colonoscopy. In this review article, we discuss about the principles, application, limitations, and future aspects of AI in colonoscopy.

Biophysics inspired artificial intelligence for colorectal cancer characterization

Over the last ten years artificial intelligence (AI) methods have begun to pervade even the most common everyday tasks such as email filtering and mobile banking. While the necessary quality and safety standards may have understandably slowed the introduction of AI to healthcare when compared with other industries, we are now beginning to see AI methods becoming more available to the clinician in select settings. In this paper we discuss current AI methods as they pertain to gastrointestinal procedures including both gastroenterology and gastrointestinal surgery. The current state of the art for polyp detection in gastroenterology is explored with a particular focus on deep leaning, its strengths, as well as some of the factors that may limit its application to the field of surgery. The use of biophysics (utilizing physics to study and explain biological phenomena) in combination with more traditional machine learning is also discussed and proposed as an alternative approach that may solve some of the challenges associated with deep learning. Past and present uses of biophysics inspired AI methods, such as the use of fluorescence guided surgery to aid in the characterization of colorectal lesions, are used to illustrate the role biophysics-inspired AI can play in the exciting future of the gastrointestinal proceduralist.

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.

Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy

Colorectal cancer is common and can be devastating, with long-term survival rates vastly improved by early diagnosis. Colon capsule endoscopy (CCE) is increasingly recognised as a reliable option for colonic surveillance, but widespread adoption has been slow for several reasons, including the time-consuming reading process of the CCE recording. Automated image recognition and artificial intelligence (AI) are appealing solutions in CCE. Through a review of the currently available and developmental technologies, we discuss how AI is poised to deliver at the forefront of CCE in the coming years. Current practice for CCE reporting often involves a two-step approach, with a ‘pre-reader’ and ‘validator’. This requires skilled and experienced readers with a significant time commitment. Therefore, CCE is well-positioned to reap the benefits of the ongoing digital innovation. This is likely to initially involve an automated AI check of finished CCE evaluations as a quality control measure. Once felt reliable, AI could be used in conjunction with a ‘pre-reader’, before adopting more of this role by sending provisional results and abnormal frames to the validator. With time, AI would be able to evaluate the findings more thoroughly and reduce the input required from human readers and ultimately autogenerate a highly accurate report and recommendation of therapy, if required, for any pathology identified. As with many medical fields reliant on image recognition, AI will be a welcome aid in CCE. Initially, this will be as an adjunct to ‘double-check’ that nothing has been missed, but with time will hopefully lead to a faster, more convenient diagnostic service for the screening population.

Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers

Artificial intelligence (AI) can extract complex information from visual data. Histopathology images of gastrointestinal (GI) and liver cancer contain a very high amount of information which human observers can only partially make sense of. Complementing human observers, AI allows an in-depth analysis of digitised histological slides of GI and liver cancer and offers a wide range of clinically relevant applications. First, AI can automatically detect tumour tissue, easing the exponentially increasing workload on pathologists. In addition, and possibly exceeding pathologist's capacities, AI can capture prognostically relevant tissue features and thus predict clinical outcome across GI and liver cancer types. Finally, AI has demonstrated its capacity to infer molecular and genetic alterations of cancer tissues from histological digital slides. These are likely only the first of many AI applications that will have important clinical implications. Thus, pathologists and clinicians alike should be aware of the principles of AI-based pathology and its ability to solve clinically relevant problems, along with its limitations and biases.

Colorectal cancer incidence, mortality, and stage distribution in European countries in the colorectal cancer screening era: an international population-based study

BACKGROUND

Colorectal cancer screening programmes and uptake vary substantially across Europe. We aimed to compare changes over time in colorectal cancer incidence, mortality, and stage distribution in relation to colorectal cancer screening implementation in European countries.

METHODS

Data from nearly 3·1 million patients with colorectal cancer diagnosed from 2000 onwards (up to 2016 for most countries) were obtained from 21 European countries, and were used to analyse changes over time in age-standardised colorectal cancer incidence and stage distribution. The WHO mortality database was used to analyse changes over time in age-standardised colorectal cancer mortality over the same period for the 16 countries with nationwide data. Incidence rates were calculated for all sites of the colon and rectum combined, as well as the subsites proximal colon, distal colon, and rectum. Average annual percentage changes (AAPCs) in incidence and mortality were estimated and relevant patterns were descriptively analysed.

FINDINGS

In countries with long-standing programmes of screening colonoscopy and faecal tests (ie, Austria, the Czech Republic, and Germany), colorectal cancer incidence decreased substantially over time, with AAPCs ranging from -2·5% (95% CI -2·8 to -2·2) to -1·6% (-2·0 to -1·2) in men and from -2·4% (-2·7 to -2·1) to -1·3% (-1·7 to -0·9) in women. In countries where screening programmes were implemented during the study period, age-standardised colorectal cancer incidence either remained stable or increased up to the year screening was implemented. AAPCs for these countries ranged from -0·2% (95% CI -1·4 to 1·0) to 1·5% (1·1 to 1·8) in men and from -0·5% (-1·7 to 0·6) to 1·2% (0·8 to 1·5) in women. Where high screening coverage and uptake were rapidly achieved (ie, Denmark, the Netherlands, and Slovenia), age-standardised incidence rates initially increased but then subsequently decreased. Conversely, colorectal cancer incidence increased in most countries where no large-scale screening programmes were available (eg, Bulgaria, Estonia, Norway, and Ukraine), with AAPCs ranging from 0·3% (95% CI 0·1 to 0·5) to 1·9% (1·2 to 2·6) in men and from 0·6% (0·4 to 0·8) to 1·1% (0·8 to 1·4) in women. The largest decreases in colorectal cancer mortality were seen in countries with long-standing screening programmes.

INTERPRETATION

We observed divergent trends in colorectal cancer incidence, mortality, and stage distribution across European countries, which appear to be largely explained by different levels of colorectal cancer screening implementation.

FUNDING

German Cancer Aid (Deutsche Krebshilfe) and the German Federal Ministry of Education and Research.

Colonoscopy and artificial intelligence: Bridging the gap or a gap needing to be bridged?

Research in artificial intelligence (AI) in gastroenterology has increased over the last decade. Colonoscopy represents the most widely published field with regards to its use in gastroenterology. Most studies to date center on polyp detection and characterization, as well as real-time evaluation of adequacy of mucosal exposure for inspection. This review article discusses how advances in AI has bridged certain gaps in colonoscopy. In addition, the gaps formed with the development of AI that currently prevent its routine use in colonoscopy will be explored.

Artificial intelligence and colorectal cancer: How far can you go?

Artificial intelligence is an emerging technology whose application is rapidly increasing in several medical fields. The numerous applications of artificial intelligence in gastroenterology have shown promising results, especially in the setting of gastrointestinal oncology. Therefore, we would like to highlight and summarize the research progress and clinical application value of artificial intelligence in the diagnosis, treatment, and prognosis of colorectal cancer to provide evidence for its use as a promising diagnostic and therapeutic tool in this setting.

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.

Detection of flat colorectal neoplasia by artificial intelligence: A systematic review

OBJECTIVES

This study review focuses on a deep learning method for the detection of colorectal lesions in colonoscopy and AI support for detecting colorectal neoplasia, especially in flat lesions.

DATA SOURCES

We performed a systematic electric search with PubMed by using "colonoscopy", "artificial intelligence", and "detection". Finally, nine articles about development and validation study and eight clinical trials met the review criteria.

RESULTS

Development and validation studies showed that trained AI models had high accuracy-approximately 90% or more for detecting lesions. Performance was better in elevated lesions than in superficial lesions in the two studies. Among the eight clinical trials, all but one trial showed a significantly high adenoma detection rate in the CADe group than in the control group. Interestingly, the CADe group detected significantly high flat lesions than the control group in the seven studies.

CONCLUSION

Flat colorectal neoplasia can be detected by endoscopists who use AI.

Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence

Artificial intelligence (AI) will likely affect various fields of medicine. This article aims to explain the fundamental principles of clinical validation, device approval, and insurance coverage decisions of AI algorithms for medical diagnosis and prediction. Discrimination accuracy of AI algorithms is often evaluated with the Dice similarity coefficient, sensitivity, specificity, and traditional or free-response receiver operating characteristic curves. Calibration accuracy should also be assessed, especially for algorithms that provide probabilities to users. As current AI algorithms have limited generalizability to real-world practice, clinical validation of AI should put it to proper external testing and assisting roles. External testing could adopt diagnostic case-control or diagnostic cohort designs. A diagnostic case-control study evaluates the technical validity/accuracy of AI while the latter tests the clinical validity/accuracy of AI in samples representing target patients in real-world clinical scenarios. Ultimate clinical validation of AI requires evaluations of its impact on patient outcomes, referred to as clinical utility, and for which randomized clinical trials are ideal. Device approval of AI is typically granted with proof of technical validity/accuracy and thus does not intend to directly indicate if AI is beneficial for patient care or if it improves patient outcomes. Neither can it categorically address the issue of limited generalizability of AI. After achieving device approval, it is up to medical professionals to determine if the approved AI algorithms are beneficial for real-world patient care. Insurance coverage decisions generally require a demonstration of clinical utility that the use of AI has improved patient outcomes.

Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis

Background and study aims  With the advent of deep neural networks (DNN) learning, the field of artificial intelligence (AI) is rapidly evolving. Recent randomized controlled trials (RCT) have investigated the influence of integrating AI in colonoscopy and its impact on adenoma detection rates (ADRs) and polyp detection rates (PDRs). We performed a systematic review and meta-analysis to reliably assess if the impact is statistically significant enough to warrant the adoption of AI -assisted colonoscopy (AIAC) in clinical practice. Methods  We conducted a comprehensive search of multiple electronic databases and conference proceedings to identify RCTs that compared outcomes between AIAC and conventional colonoscopy (CC). The primary outcome was ADR. The secondary outcomes were PDR and total withdrawal time (WT). Results  Six RCTs (comparing AIAC vs CC) with 5058 individuals undergoing average-risk screening colonoscopy were included in the meta-analysis. ADR was significantly higher with AIAC compared to CC (33.7 % versus 22.9 %; odds ratio (OR) 1.76, 95 % confidence interval (CI) 1.55-2.00; I ²  = 28 %). Similarly, PDR was significantly higher with AIAC (45.6 % versus 30.6 %; OR 1.90, 95 %CI, 1.68-2.15, I ²  = 0 %). The overall WT was higher for AIAC compared to CC (mean difference [MD] 0.46 (0.00-0.92) minutes, I ²  = 94 %). Conclusions  There is an increase in adenoma and polyp detection with the utilization of AIAC.

Barrett esophagus: What to expect from Artificial Intelligence?

The evaluation and assessment of Barrett's esophagus is challenging for both expert and nonexpert endoscopists. However, the early diagnosis of cancer in Barrett's esophagus is crucial for its prognosis, and could save costs. Pre-clinical and clinical studies on the application of Artificial Intelligence (AI) in Barrett's esophagus have shown promising results. In this review, we focus on the current challenges and future perspectives of implementing AI systems in the management of patients with Barrett's esophagus.

Artificial Intelligence-Aided Colonoscopy for Polyp Detection: A Systematic Review and Meta-Analysis of Randomized Clinical Trials

Background: This study aimed to compare artificial intelligence (AI)-aided colonoscopy with conventional colonoscopy for polyp detection. Methods: A systematic literature search was performed in PubMed and Ovid for randomized clinical trials (RCTs) comparing AI-aided colonoscopy with conventional colonoscopy for polyp detection. The last search was performed on July 22, 2020. The primary outcome was polyp detection rate (PDR) and adenoma detection rate (ADR). Results: Seven RCTs published between 2019 and 2020 with a total of 5427 individuals were included. When compared with conventional colonoscopy, AI-aided colonoscopy significantly improved PDR (P < .001, odds ratio [OR] = 1.95, 95% confidence interval [CI]: 1.75 to 2.19, I² = 0%) and ADR (P < .001, OR = 1.72, 95% CI: 1.52 to 1.95, I² = 33%). Besides, polyps in the AI-aided group were significantly smaller in size than those in conventional group (P = .004, weighted mean difference = -0.48, 95% CI: -0.81 to -0.15, I² = 0%). In addition, AI-aided group detected significantly less proportion of advanced adenoma (P = .03, OR = 0.70, 95% CI: 0.50 to 0.97, I² = 46%), pedicle polyps (P < .001, OR = 0.64, 95% CI: 0.49 to 0.83, I² = 0%), and pedicle adenomas (P < .001, OR = 0.60, 95% CI: 0.44 to 0.80, I² = 0%). Conclusion: AI-aided colonoscopy could significantly increase the PDR and ADR, especially for those with small size. Besides, the shape and pathology recognition of the AI technique should be further improved in the future.

Artificial intelligence in endoscopy: Present and future perspectives

Artificial intelligence (AI) has been attracting considerable attention as an important scientific topic in the field of medicine. Deep-leaning (DL) technologies have been applied more dominantly than other traditional machine-learning methods. They have demonstrated excellent capability to retract visual features of objectives, even unnoticeable ones for humans, and analyze huge amounts of information within short periods. The amount of research applying DL-based models to real-time computer-aided diagnosis (CAD) systems has been increasing steadily in the GI endoscopy field. An array of published data has already demonstrated the advantages of DL-based CAD models in the detection and characterization of various neoplastic lesions, regardless of the level of the GI tract. Although the diagnostic performances and study designs vary widely, owing to a lack of academic standards to assess the capability of AI for GI endoscopic diagnosis fairly, the superiority of CAD models has been demonstrated for almost all applications studied so far. Most of the challenges associated with AI in the endoscopy field are general problems for AI models used in the real world outside of medical fields. Solutions have been explored seriously and some solutions have been tested in the endoscopy field. Given that AI has become the basic technology to make machines react to the environment, AI would be a major technological paradigm shift, for not only diagnosis but also treatment. In the near future, autonomous endoscopic diagnosis might no longer be just a dream, as we are witnessing with the advent of autonomously driven electric vehicles.

Artificial Intelligence in Colorectal Polyp Detection and Characterization

BACKGROUND

Over the past 20 years, the advancement of artificial intelligence (AI) and deep learning (DL) has allowed for fast sorting and analysis of large sets of data. In the field of gastroenterology, colorectal screening procedures produces an abundance of data through video and imaging. With AI and DL, this information can be used to create systems where automatic polyp detection and characterization is possible. Convoluted Neural Networks (CNNs) have proven to be an effective way to increase polyp detection and ultimately adenoma detection rates. Different methods of polyp characterization of being hyperplastic vs. adenomatous or non-neoplastic vs. neoplastic has also been investigated showing promising results.

FINDINGS

The rate of missed polyps on colonoscopy can be as high as 25%. At the beginning of the 2000s, hand-crafted machine learning (ML) algorithms were created and trained retrospectively on colonoscopy images and videos, achieving high sensitivity, specificity, and accuracy of over 90% in many of the studies. Over time, the advancement of DL and CNNs has allowed algorithms to be trained on non-medical images and applied retrospectively to colonoscopy videos and images with similar results. Within the past few years, these algorithms have been applied in real-time colonoscopies and has shown mixed results, one showing no difference while others showing increased polyp detection.Various methods of polyp characterization have also been investigated. Through AI, DL, and CNNs polyps can be identified has hyperplastic/adenomatous or non-neoplastic/neoplastic with high sensitivity, specificity, and accuracy. One of the research areas in polyp characterization is how to capture the polyp image. This paper looks at different modalities of characterizing polyps such as magnifying narrow band imaging (NBI), endocytoscopy, laser-induced florescent spectroscopy, auto-florescent endoscopy, and white-light endoscopy.

CONCLUSIONS

Overall, much progress has been made in automatic detection and characterization of polyps in real time. Barring ethical or mass adoption setbacks, it is inevitable that AI will be involved in the field of GI, especially in colorectal polyp detection and identification.

Current status and future perspective on artificial intelligence for lower endoscopy

The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for non-experts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.

Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy

Artificial intelligence (AI) and its application in medicine has grown large interest. Within gastrointestinal (GI) endoscopy, the field of colonoscopy and polyp detection is the most investigated, however, upper GI follows the lead. Since endoscopy is performed by humans, it is inherently an imperfect procedure. Computer-aided diagnosis may improve its quality by helping prevent missing lesions and supporting optical diagnosis for those detected. An entire evolution in AI systems has been established in the last decades, resulting in optimization of the diagnostic performance with lower variability and matching or even outperformance of expert endoscopists. This shows a great potential for future quality improvement of endoscopy, given the outstanding diagnostic features of AI. With this narrative review, we highlight the potential benefit of AI to improve overall quality in daily endoscopy and describe the most recent developments for characterization and diagnosis as well as the recent conditions for regulatory approval.

Artificial intelligence in colonoscopy - Now on the market. What's next?

Adoption of artificial intelligence (AI) in clinical medicine is revolutionizing daily practice. In the field of colonoscopy, major endoscopy manufacturers have already launched their own AI products on the market with regulatory approval in Europe and Asia. This commercialization is strongly supported by positive evidence that has been recently established through rigorously designed prospective trials and randomized controlled trials. According to some of the trials, AI tools possibly increase the adenoma detection rate by roughly 50% and contribute to a 7-20% reduction of colonoscopy-related costs. Given that reliable evidence is emerging, together with active commercialization, this seems to be a good time for us to review and discuss the current status of AI in colonoscopy from a clinical perspective. In this review, we introduce the advantages and possible drawbacks of AI tools and explore their future potential including the possibility of obtaining reimbursement.

Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis

BACKGROUND AND AIMS

One-fourth of colorectal neoplasia are missed at screening colonoscopy, representing the main cause of interval colorectal cancer. Deep learning systems with real-time computer-aided polyp detection (CADe) showed high accuracy in artificial settings, and preliminary randomized controlled trials (RCTs) reported favorable outcomes in the clinical setting. The aim of this meta-analysis was to summarize available RCTs on the performance of CADe systems in colorectal neoplasia detection.

METHODS

We searched MEDLINE, EMBASE, and Cochrane Central databases until March 2020 for RCTs reporting diagnostic accuracy of CADe systems in the detection of colorectal neoplasia. The primary outcome was pooled adenoma detection rate (ADR), and secondary outcomes were adenoma per colonoscopy (APC) according to size, morphology, and location; advanced APC; polyp detection rate; polyps per colonoscopy; and sessile serrated lesions per colonoscopy. We calculated risk ratios (RRs), performed subgroup and sensitivity analyses, and assessed heterogeneity and publication bias.

RESULTS

Overall, 5 randomized controlled trials (4354 patients) were included in the final analysis. Pooled ADR was significantly higher in the CADe group than in the control group (791/2163 [36.6%] vs 558/2191 [25.2%]; RR, 1.44; 95% confidence interval [CI], 1.27-1.62; P < .01; I² = 42%). APC was also higher in the CADe group compared with control (1249/2163 [.58] vs 779/2191 [.36]; RR, 1.70; 95% CI, 1.53-1.89; P < .01; I² = 33%). APC was higher for ≤5-mm (RR, 1.69; 95% CI, 1.48-1.84), 6- to 9-mm (RR, 1.44; 95% CI, 1.19-1.75), and ≥10-mm adenomas (RR, 1.46; 95% CI, 1.04-2.06) and for proximal (RR, 1.59; 95% CI, 1.34-1.88), distal (RR, 1.68; 95% CI, 1.50-1.88), flat (RR, 1.78; 95% CI, 1.47-2.15), and polypoid morphology (RR, 1.54; 95% CI, 1.40-1.68). Regarding histology, CADe resulted in a higher sessile serrated lesion per colonoscopy (RR, 1.52; 95% CI, 1.14-2.02), whereas a nonsignificant trend for advanced ADR was found (RR, 1.35; 95% CI, .74-2.47; P = .33; I² = 69%). Level of evidence for RCTs was graded as moderate.

CONCLUSIONS

According to available evidence, the incorporation of artificial intelligence as aid for detection of colorectal neoplasia results in a significant increase in the detection of colorectal neoplasia, and such effect is independent from main adenoma characteristics.

Striving for quality improvement: can artificial intelligence help?

Artificial intelligence (AI) is of keen interest for global health development as potential support for current human shortcomings. Gastrointestinal (GI) endoscopy is an excellent substrate for AI, since it holds the genuine potential to improve quality in GI endoscopy and overall patient care by improving detection and diagnosis guiding the endoscopists in performing endoscopy to the highest quality standards. The possibility of large data acquisitioning to refine algorithms makes implementation of AI into daily practice a potential reality. With the start of a new era adopting deep learning, large amounts of data can easily be processed, resulting in better diagnostic performances. In the upper gastrointestinal tract, research currently focusses on the detection and characterization of neoplasia, including Barrett's, squamous cell and gastric carcinoma, with an increasing amount of AI studies demonstrating the potential and benefit of AI-augmented endoscopy. Deep learning applied to small bowel video capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. In the colon, multiple prospective trials including five randomized trials, showed a consistent improvement in polyp and adenoma detection rates, one of the main quality indicators in endoscopy. There are however potential additional roles for AI to assist in quality improvement of endoscopic procedures, training and therapeutic decision making. Further large-scale, multicenter validation trials are required before AI-augmented diagnostic gastrointestinal endoscopy can be integrated into our routine clinical practice.

Artificial intelligence-aided colonoscopy: Recent developments and future perspectives

Artificial intelligence (AI) systems, especially after the successful application of Convolutional Neural Networks, are revolutionizing modern medicine. Gastrointestinal Endoscopy has shown to be a fertile terrain for the development of AI systems aiming to aid endoscopists in various aspects of their daily activity. Lesion detection can be one of the two main aspects in which AI can increase diagnostic yield and abilities of endoscopists. In colonoscopy, it is well known that a substantial rate of missed neoplasia is still present, representing the major cause of interval cancer. In addition, an extremely high variability in adenoma detection rate, the main key quality indicator in colonoscopy, has been extensively reported. The other domain in which AI is believed to have a considerable impact on everyday clinical practice is lesion characterization and aid in “optical diagnosis”. By predicting in vivo histology, such pathology costs may be averted by the implementation of two separate but synergistic strategies, namely the “leave-in-situ” strategy for < 5 mm hyperplastic lesions in the rectosigmoid tract, and “resect and discard” for the other diminutive lesions. In this opinion review we present current available evidence regarding the role of AI in improving lesions’ detection and characterization during colonoscopy.

The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study

BACKGROUND

Computer-aided detection (CADe) of colon polyps has been demonstrated to improve colon polyp and adenoma detection during colonoscopy by indicating the location of a given polyp on a parallel monitor. The aim of this study was to investigate whether embedding the CADe system into the primary colonoscopy monitor may serve to increase polyp and adenoma detection, without increasing physician fatigue level.

METHODS

Consecutive patients presenting for colonoscopies were prospectively randomized to undergo routine colonoscopy with or without the assistance of a real-time polyp detection CADe system. Fatigue level was evaluated from score 0 to 10 by the performing endoscopists after each colonoscopy procedure. The main outcome was adenoma detection rate (ADR).

RESULTS

Out of 790 patients analyzed, 397 were randomized to routine colonoscopy (control group), and 393 to a colonoscopy with computer-aided diagnosis (CADe group). The ADRs were 20.91% and 29.01%, respectively (OR = 1.546, 95% CI 1.116-2.141, p = 0.009). The average number of adenomas per colonoscopy (APC) was 0.29 and 0.48, respectively (Change Folds = 1.64, 95% CI 1.299-2.063, p < 0.001). The improvement in polyp detection was mainly due to increased detection of non-advanced diminutive adenomas, serrated adenoma and hyperplastic polyps. The fatigue score for each procedure was 3.28 versus 3.40 for routine and CADe group, p = 0.357.

CONCLUSIONS

A real-time CADe system employed on the primary endoscopy monitor may lead to improvements in ADR and polyp detection rate without increasing fatigue level during colonoscopy. The integration of a low-latency and high-performance CADe systems may serve as an effective quality assurance tool during colonoscopy. www.chictr.org.cn number, ChiCTR1800018058.

Artificial intelligence: Thinking outside the box

Artificial intelligence (AI) for luminal gastrointestinal endoscopy is rapidly evolving. To date, most applications have focused on colon polyp detection and characterization. However, the potential of AI to revolutionize our current practice in endoscopy is much more broadly positioned. In this review article, the Authors provide new ideas on how AI might help endoscopists in the future to rediscover endoscopy practice.

Impact of artificial intelligence on colorectal polyp detection

Since colonoscopy and polypectomy were introduced, Colorectal Cancer (CRC) incidence and mortality decreased significantly. Although we have entered the era of quality measurement and improvement, literature shows that a considerable amount of colorectal neoplasia is still missed by colonoscopists up to 25%, leading to an high rate of interval colorectal cancer that account for nearly 10% of all diagnosed CRC. Two main reasons have been recognised: recognition failure and mucosal exposure. For this purpose, Artificial Intelligence (AI) systems have been recently developed that identify a "hot" area during the endoscopic examination. In retrospective studies, where the systems are tested with a batch of unknown images, deep learning systems have shown very good performances, with high levels of accuracy. Of course, this setting may not reflect actual clinical practice where different pitfalls can occur, like suboptimal bowel preparation or poor examination technique. For this reason, a number of randomised clinical trials have recently been published where AI was tested in real time during endoscopic examinations. We present here an overview on recent literature addressing the performance of Computer Assisted Detection (CADe) of colorectal polyps in colonoscopy.