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

Spatiotemporal analysis of small bowel capsule endoscopy videos for outcomes prediction in Crohn's disease

Background

Deep learning techniques can accurately detect and grade inflammatory findings on images from capsule endoscopy (CE) in Crohn's disease (CD). However, the predictive utility of deep learning of CE in CD for disease outcomes has not been examined.

Objectives

We aimed to develop a deep learning model that can predict the need for biological therapy based on complete CE videos of newly-diagnosed CD patients.

Design

This was a retrospective cohort study. The study cohort included treatment-naïve CD patients that have performed CE (SB3, Medtronic) within 6 months of diagnosis. Complete small bowel videos were extracted using the RAPID Reader software.

Methods

CE videos were scored using the Lewis score (LS). Clinical, endoscopic, and laboratory data were extracted from electronic medical records. Machine learning analysis was performed using the TimeSformer computer vision algorithm developed to capture spatiotemporal characteristics for video analysis.

Results

The patient cohort included 101 patients. The median duration of follow-up was 902 (354-1626) days. Biological therapy was initiated by 37 (36.6%) out of 101 patients. TimeSformer algorithm achieved training and testing accuracy of 82% and 81%, respectively, with an Area under the ROC Curve (AUC) of 0.86 to predict the need for biological therapy. In comparison, the AUC for LS was 0.70 and for fecal calprotectin 0.74.

Conclusion

Spatiotemporal analysis of complete CE videos of newly-diagnosed CD patients achieved accurate prediction of the need for biological therapy. The accuracy was superior to that of the human reader index or fecal calprotectin. Following future validation studies, this approach will allow for fast and accurate personalization of treatment decisions in CD.

Research trends on artificial intelligence and endoscopy in digestive diseases: A bibliometric analysis from 1990 to 2022

BACKGROUND

Recently, artificial intelligence (AI) has been widely used in gastrointestinal endoscopy examinations.

AIM

To comprehensively evaluate the application of AI-assisted endoscopy in detecting different digestive diseases using bibliometric analysis.

METHODS

Relevant publications from the Web of Science published from 1990 to 2022 were extracted using a combination of the search terms “AI” and “endoscopy”. The following information was recorded from the included publications: Title, author, institution, country, endoscopy type, disease type, performance of AI, publication, citation, journal and H-index.

RESULTS

A total of 446 studies were included. The number of articles reached its peak in 2021, and the annual citation numbers increased after 2006. China, the United States and Japan were dominant countries in this field, accounting for 28.7%, 16.8%, and 15.7% of publications, respectively. The Tada Tomohiro Institute of Gastroenterology and Proctology was the most influential institution. “Cancer” and “polyps” were the hotspots in this field. Colorectal polyps were the most concerning and researched disease, followed by gastric cancer and gastrointestinal bleeding. Conventional endoscopy was the most common type of examination. The accuracy of AI in detecting Barrett’s esophagus, colorectal polyps and gastric cancer from 2018 to 2022 is 87.6%, 93.7% and 88.3%, respectively. The detection rates of adenoma and gastrointestinal bleeding from 2018 to 2022 are 31.3% and 96.2%, respectively.

CONCLUSION

AI could improve the detection rate of digestive tract diseases and a convolutional neural network-based diagnosis program for endoscopic images shows promising results.

Preclinical Evaluation of a Microwave-Based Accessory Device for Colonoscopy in an In Vivo Porcine Model with Colorectal Polyps

BACKGROUND AND AIMS

Colonoscopy is currently the most effective way of detecting colorectal cancer and removing polyps, but it has some drawbacks and can miss up to 22% of polyps. Microwave imaging has the potential to provide a 360° view of the colon and addresses some of the limitations of conventional colonoscopy. This study evaluates the feasibility of a microwave-based colonoscopy in an in vivo porcine model.

METHODS

A prototype device with microwave antennas attached to a conventional endoscope was tested on four healthy pigs and three gene-targeted pigs with mutations in the adenomatous polyposis coli gene. The first four animals were used to evaluate safety and maneuverability and compatibility with endoscopic tools. The ability to detect polyps was tested in a series of three gene-targeted pigs.

RESULTS

the microwave-based device did not affect endoscopic vision or cause any adverse events such as deep mural injuries. The microwave system was stable during the procedures, and the detection algorithm showed a maximum detection signal for adenomas compared with healthy mucosa.

CONCLUSIONS

Microwave-based colonoscopy is feasible and safe in a preclinical model, and it has the potential to improve polyp detection. Further investigations are required to assess the device's efficacy in humans.

Cost-effectiveness of Artificial Intelligence-Aided Colonoscopy for Adenoma Detection in Colon Cancer Screening

Background and Aims

Artificial intelligence-aided colonoscopy significantly improves adenoma detection. We assessed the cost-effectiveness of the GI Genius technology, an artificial intelligence-aided computer diagnosis for polyp detection (CADe), in improving colorectal cancer outcomes, adopting a Canadian health care perspective.

Methods

A Markov model with 1-year cycles and a lifetime horizon was used to estimate incremental cost-effectiveness ratio comparing CADe to conventional colonoscopy polyp detection amongst patients with a positive faecal immunochemical test. Outcomes were life years (LYs) and quality-adjusted life years (QALY) gained. The analysis applied costs associated with health care resource utilization, including procedures and follow-ups, from a provincial payer's perspective using 2022 Canadian dollars. Effectiveness and cost data were sourced from the literature and publicly available databases. Extensive probabilistic and deterministic sensitivity analyses were performed, assessing model robustness.

Results

Life years and QALY gains for the CADe and conventional colonoscopy groups were 19.144 versus 19.125 and 17.137 versus 17.113, respectively. CADe and conventional colonoscopies' overall per-case costs were $2990.74 and $3004.59, respectively. With a willingness-to-pay pre-set at $50,000/QALY, the incremental cost-effectiveness ratio was dominant for both outcomes, showing that CADe colonoscopy is cost-effective. Deterministic sensitivity analysis confirmed that the model was sensitive to the incidence risk ratio of adenoma per colonoscopy for large adenomas. Probabilistic sensitivity analysis showed that the CADe strategy was cost-effective in up to 73.4% of scenarios.

Conclusion

The addition of CADe solution to colonoscopy is a dominant, cost-effective strategy when used in faecal immunochemical test-positive patients in a Canadian health care setting.

Artificial Intelligence in Colonoscopy

PURPOSE OF REVIEW

Artificial intelligence (AI) is a rapidly growing field in gastrointestinal endoscopy, and its potential applications are virtually endless, with studies demonstrating use of AI for early gastric cancer, inflammatory bowel disease, Barrett's esophagus, capsule endoscopy, as well as other areas in gastroenterology. Much of the early studies and applications of AI in gastroenterology have revolved around colonoscopy, particularly with regards to real-time polyp detection and characterization. This review will cover much of the existing data on computer-aided detection (CADe), computer-aided diagnosis (CADx), and briefly discuss some other interesting applications of AI for colonoscopy, while also considering some of the challenges and limitations that exist around the use of AI for colonoscopy.

RECENT FINDINGS

Multiple randomized controlled trials have now been published which show a statistically significant improvement when using AI to improve adenoma detection and reduce adenoma miss rates during colonoscopy. There is also a growing pool of literature showing that AI can be helpful for characterizing/diagnosing colorectal polyps in real time. AI has also shown promise in other areas of colonoscopy, including polyp sizing and automated measurement and monitoring of quality metrics during colonoscopy. AI is a promising tool that has the ability to shape the future of gastrointestinal endoscopy, with much of the early data showing significant benefits to use of AI during colonoscopy. However, there remain several challenges that may delay or hamper the widespread use of AI in the field.

Artificial intelligence in intestinal polyp and colorectal cancer prediction

Artificial intelligence (AI) algorithms and their application to disease detection and decision support for healthcare professions have greatly evolved in the recent decade. AI has been widely applied and explored in gastroenterology for endoscopic analysis to diagnose intestinal cancers, premalignant polyps, gastrointestinal inflammatory lesions, and bleeding. Patients' responses to treatments and prognoses have both been predicted using AI by combining multiple algorithms. In this review, we explored the recent applications of AI algorithms in the identification and characterization of intestinal polyps and colorectal cancer predictions. AI-based prediction models have the potential to help medical practitioners diagnose, establish prognoses, and find accurate conclusions for the treatment of patients. With the understanding that rigorous validation of AI approaches using randomized controlled studies is solicited before widespread clinical use by health authorities, the article also discusses the limitations and challenges associated with deploying AI systems to diagnose intestinal malignancies and premalignant lesions.

Efficacy of image-enhanced endoscopy for colorectal adenoma detection: A multicenter, randomized trial

BACKGROUND

Improved adenoma detection at colonoscopy has decreased the risk of developing colorectal cancer. However, whether image-enhanced endoscopy (IEE) further improves the adenoma detection rate (ADR) is controversial.

AIM

To compare IEE with white-light imaging (WLI) endoscopy for the detection and identification of colorectal adenoma.

METHODS

This was a multicenter, randomized, controlled trial. Participants were enrolled between September 2019 to April 2021 from 4 hospital in China. Patients were randomly assigned to an IEE group with WLI on entry and IEE on withdrawal (n = 2113) or a WLI group with WLI on both entry and withdrawal (n = 2098). The primary outcome was the ADR. The secondary endpoints were the polyp detection rate (PDR), adenomas per colonoscopy, adenomas per positive colonoscopy, and factors related to adenoma detection.

RESULTS

A total of 4211 patients (966 adenomas) were included in the analysis (mean age, 56.7 years, 47.1% male). There were 2113 patients (508 adenomas) in the IEE group and 2098 patients (458 adenomas) in the WLI group. The ADR in two group were not significantly different [24.0% vs 21.8%, 1.10, 95% confidence interval (CI): 0.99-1.23, P = 0.09]. The PDR was higher with IEE group (41.7%) than with WLI group (36.1%, 1.16, 95%CI: 1.07-1.25, P = 0.01). Differences in mean withdrawal time (7.90 ± 3.42 min vs 7.85 ± 3.47 min, P = 0.30) and adenomas per colonoscopy (0.33 ± 0.68 vs 0.28 ± 0.62, P = 0.06) were not significant. Subgroup analysis found that with narrow-band imaging (NBI), between-group differences in the ADR, were not significant (23.7% vs 21.8%, 1.09, 95%CI: 0.97-1.22, P = 0.15), but were greater with linked color imaging (30.9% vs 21.8%, 1.42, 95%CI: 1.04-1.93, P = 0.04). the second-generation NBI (2G-NBI) had an advantage of ADR than both WLI and the first-generation NBI (27.0% vs 21.8%, P = 0.01; 27.0% vs 21.2.0%, P = 0.01).

CONCLUSION

This prospective study confirmed that, among Chinese, IEE didn’t increase the ADR compared with WLI, but 2G-NBI increase the ADR.

Post-colonoscopy cancer rate at a tertiary referral hospital in Australia: A data linkage analysis

BACKGROUND AND AIM

Colorectal cancer (CRC) diagnosed following a cancer-negative colonoscopy is termed as post-colonoscopy CRC (PCCRC). The World Endoscopy Organization has recently standardized the definition of PCCRC-3Y (CRC developing within 3 years of a cancer-negative colonoscopy). In the present study, we sought to assess PCCRC-3Y rate, perform root-cause analyses, and identify factors associated with development of PCCRC at a tertiary referral hospital in Australia.

METHODS

All patients undergoing colonoscopy from 2011 to 2018 were matched to a population-based cancer register. PCCRC-3Y rate was assessed for years 2011-2015. All PCCRC cases that developed within 6-48 months after a cancer-negative colonoscopy underwent root-cause analyses. Descriptive statistics were used to summarize data.

RESULTS

Among 17 828 patients undergoing colonoscopy, 367 CRC cases were diagnosed during the study period. This included nine PCCRC cases, which developed at a median of 14 months (range 7-34 months) after cancer-negative colonoscopy. The PCCRC-3Y rate for years 2011-2015 was 2.16% (95% CI 0.91-5.15). All nine PCCRC cases were moderately or poorly differentiated adenocarcinomas; seven of nine were early-stage CRC (stages I and II) and six of nine probably represented missed lesions at index colonoscopy despite an apparently adequate examination. History of inflammatory bowel disease (IBD) (odds ratio [OR] 21.9, 95% confidence interval [CI] 4.6-103.7, P < 0.001) and diverticulosis (OR 5.4, 95% CI 1.4-20.5, P = 0.01) were significantly associated with development of missed CRC.

CONCLUSIONS

In our tertiary referral colonoscopy cohort, PCCRC-3Y rate was 2.16% (95% CI 0.91-5.15). IBD and diverticulosis were significantly associated with risk of PCCRC. The majority of PCCRC lesions were likely missed at index colonoscopy, despite an apparently adequate examination.

Increasing the accuracy of colorectal cancer screening

INTRODUCTION

Colorectal cancer (CRC) is a major health issue, being responsible for nearly 10% of all cancer-related deaths. Since CRC is often an asymptomatic or paucisymptomatic disease until it reaches advanced stages, screening is crucial for the diagnosis of preneoplastic lesions or early CRC.

AREAS COVERED

The aim of this review is to summarize the literature evidence on currently available CRC screening tools, with their pros and cons, focusing on the level of accuracy reached by each test over time. We also provide an overview of novel technologies and scientific advances that are currently being investigated and that in the future may represent real game-changers in the field of CRC screening.

EXPERT OPINION

We suggest that best screening modalities are annual or biennial FIT and colonoscopy every 10 years. We believe that the introduction of artificial intelligence (AI)-tools in the CRC screening field could lead to a significant improvement of the screening efficacy in reducing CRC incidence and mortality in the future. More resources should be put into implementing CRC programmes and support research project to further increase accuracy of CRC screening tests and strategies.

Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?

Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.

Impact of Artificial Intelligence on Colonoscopy Surveillance After Polyp Removal: A Pooled Analysis of Randomized Trials

BACKGROUND AND AIMS

Artificial intelligence (AI) tools aimed at improving polyp detection have been shown to increase the adenoma detection rate during colonoscopy. However, it is unknown how increased polyp detection rates by AI affect the burden of patient surveillance after polyp removal.

METHODS

We conducted a pooled analysis of 9 randomized controlled trials (5 in China, 2 in Italy, 1 in Japan, and 1 in the United States) comparing colonoscopy with or without AI detection aids. The primary outcome was the proportion of patients recommended to undergo intensive surveillance (ie, 3-year interval). We analyzed intervals for AI and non-AI colonoscopies for the U.S. and European recommendations separately. We estimated proportions by calculating relative risks using the Mantel-Haenszel method.

RESULTS

A total of 5796 patients (51% male, mean 53 years of age) were included; 2894 underwent AI-assisted colonoscopy and 2902 non-AI colonoscopy. When following U.S. guidelines, the proportion of patients recommended intensive surveillance increased from 8.4% (95% CI, 7.4%-9.5%) in the non-AI group to 11.3% (95% CI, 10.2%-12.6%) in the AI group (absolute difference, 2.9% [95% CI, 1.4%-4.4%]; risk ratio, 1.35 [95% CI, 1.16-1.57]). When following European guidelines, it increased from 6.1% (95% CI, 5.3%-7.0%) to 7.4% (95% CI, 6.5%-8.4%) (absolute difference, 1.3% [95% CI, 0.01%-2.6%]; risk ratio, 1.22 [95% CI, 1.01-1.47]).

CONCLUSIONS

The use of AI during colonoscopy increased the proportion of patients requiring intensive colonoscopy surveillance by approximately 35% in the United States and 20% in Europe (absolute increases of 2.9% and 1.3%, respectively). While this may contribute to improved cancer prevention, it significantly adds patient burden and healthcare costs.

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.

Changes in Adenoma Detection Rate From Full-Spectrum Endoscopy to Standard Forward-Viewing Endoscopy

GOALS

The aim was to investigate the adenoma detection rate (ADR) of endoscopists who have used full-spectrum endoscopy (Fuse) for 3 years and revert back to traditional forward-viewing endoscopes (R-TFV) at an ambulatory surgical center.

BACKGROUND

Traditional forward viewing (TFV) endoscopes have 1 camera and provide an angle of view of 140 to 170 degrees, whereas Fuse provides a 330 degrees view through the addition of 2 side cameras. It has previously been reported that Fuse increased the ADR by 5.4% when compared with previous rates using TFV. Fuse is no longer commercially available. The ADR of endoscopists who revert back to TFV is unknown.

STUDY

We conducted a retrospective analysis of data examining the ADR from average risk screening colonoscopies at a 5-room ambulatory surgical center where endoscopists transitioned from TFV to Fuse in April 2014 and then reverted back to TFV in 2016. The primary outcome was ADR. Secondary outcomes were ADR for advanced and right-sided adenomas.

RESULTS

A total of 6110 procedures were reviewed. The ADR was 23.70% for TFV, 29.02% for Fuse and 28.88% for R-TFV. The ADR for advanced adenomas was 3.8% for TFV, 6.0% for Fuse and 7.3% for R-TFV. The ADR for right-sided adenomas was 13.0% for TFV, 16.7% for Fuse and 16.0% for R-TFV. The results for all 3 categories showed a statistical difference between TFV and Fuse as well as between TFV and R-TFV. There were no statistical differences between the ADR of Fuse compared with R-TFV.

CONCLUSIONS

During R-TFV, endoscopists are able to maintain their increased ability to detect adenomas. This would suggest that there was a change in behavior in endoscopists using Fuse that was durable.

Artificial Intelligence in Pediatric Endoscopy: Current Status and Future Applications

The application of artificial intelligence (AI) has great promise for improving pediatric endoscopy. The majority of preclinical studies have been undertaken in adults, with the greatest progress being made in the context of colorectal cancer screening and surveillance. This development has only been possible with advances in deep learning, like the convolutional neural network model, which has enabled real-time detection of pathology. Comparatively, the majority of deep learning systems developed in inflammatory bowel disease have focused on predicting disease severity and were developed using still images rather than videos. The application of AI to pediatric endoscopy is in its infancy, thus providing an opportunity to develop clinically meaningful and fair systems that do not perpetuate societal biases. In this review, we provide an overview of AI, summarize the advances of AI in endoscopy, and describe its potential application to pediatric endoscopic practice and education.

Computer-aided automated diminutive colonic polyp detection in colonoscopy by using deep machine learning system; first indigenous algorithm developed in India

BACKGROUND

Colonic polyps can be detected and resected during a colonoscopy before cancer development. However, about 1/4th of the polyps could be missed due to their small size, location or human errors. An artificial intelligence (AI) system can improve polyp detection and reduce colorectal cancer incidence. We are developing an indigenous AI system to detect diminutive polyps in real-life scenarios that can be compatible with any high-definition colonoscopy and endoscopic video- capture software.

METHODS

We trained a masked region-based convolutional neural network model to detect and localize colonic polyps. Three independent datasets of colonoscopy videos comprising 1,039 image frames were used and divided into a training dataset of 688 frames and a testing dataset of 351 frames. Of 1,039 image frames, 231 were from real-life colonoscopy videos from our centre. The rest were from publicly available image frames already modified to be directly utilizable for developing the AI system. The image frames of the testing dataset were also augmented by rotating and zooming the images to replicate real-life distortions of images seen during colonoscopy. The AI system was trained to localize the polyp by creating a 'bounding box'. It was then applied to the testing dataset to test its accuracy in detecting polyps automatically.

RESULTS

The AI system achieved a mean average precision (equivalent to specificity) of 88.63% for automatic polyp detection. All polyps in the testing were identified by AI, i.e., no false-negative result in the testing dataset (sensitivity of 100%). The mean polyp size in the study was 5 (± 4) mm. The mean processing time per image frame was 96.4 minutes.

CONCLUSIONS

This AI system, when applied to real-life colonoscopy images, having wide variations in bowel preparation and small polyp size, can detect colonic polyps with a high degree of accuracy.

A Review of Colonoscopy in Intestinal Diseases

Since the development of the fiberoptic colonoscope in the late 1960s, colonoscopy has been a useful tool to diagnose and treat various intestinal diseases. This article reviews the clinical use of colonoscopy for various intestinal diseases based on present and future perspectives. Intestinal diseases include infectious diseases, inflammatory bowel disease (IBD), neoplasms, functional bowel disorders, and others. In cases of infectious diseases, colonoscopy is helpful in making the differential diagnosis, revealing endoscopic gross findings, and obtaining the specimens for pathology. Additionally, colonoscopy provides clues for distinguishing between infectious disease and IBD, and aids in the post-treatment monitoring of IBD. Colonoscopy is essential for the diagnosis of neoplasms that are diagnosed through only pathological confirmation. At present, malignant tumors are commonly being treated using endoscopy because of the advancement of endoscopic resection procedures. Moreover, the characteristics of tumors can be described in more detail by image-enhanced endoscopy and magnifying endoscopy. Colonoscopy can be helpful for the endoscopic decompression of colonic volvulus in large bowel obstruction, balloon dilatation as a treatment for benign stricture, and colon stenting as a treatment for malignant obstruction. In the diagnosis of functional bowel disorder, colonoscopy is used to investigate other organic causes of the symptom.

A Review of the Technology, Training, and Assessment Methods for the First Real-Time AI-Enhanced Medical Device for Endoscopy

Artificial intelligence (AI) has the potential to assist in endoscopy and improve decision making, particularly in situations where humans may make inconsistent judgments. The performance assessment of the medical devices operating in this context is a complex combination of bench tests, randomized controlled trials, and studies on the interaction between physicians and AI. We review the scientific evidence published about GI Genius, the first AI-powered medical device for colonoscopy to enter the market, and the device that is most widely tested by the scientific community. We provide an overview of its technical architecture, AI training and testing strategies, and regulatory path. In addition, we discuss the strengths and limitations of the current platform and its potential impact on clinical practice. The details of the algorithm architecture and the data that were used to train the AI device have been disclosed to the scientific community in the pursuit of a transparent AI. Overall, the first AI-enabled medical device for real-time video analysis represents a significant advancement in the use of AI for endoscopies and has the potential to improve the accuracy and efficiency of colonoscopy procedures.

One-year review of real-time artificial intelligence (AI)-aided endoscopy performance

BACKGROUND

Colonoscopies have long been the gold standard for detection of pre-malignant neoplastic lesions of the colon. Our previous study tried real-time artificial intelligence (AI)-aided colonoscopy over a three-month period and found significant improvements in collective and individual endoscopist's adenoma detection rates compared to baseline. As an expansion, this study evaluates the 1-year performance of AI-aided colonoscopy in the same institution.

METHODS

A prospective cohort study was conducted in a single institution in Singapore. The AI software used was GI Genius™ Intelligent Endoscopy Module, US-DG-2000309 © 2021 Medtronic. Between July 2021 and June 2022, polypectomy rates in non-AI-aided colonoscopies and AI-aided colonoscopies were calculated and compared. Some of the AI-aided colonoscopies were recorded and video reviewed. A "hit" was defined as a sustained detection of an area by the AI. If a polypectomy was performed for a "hit," its histology was reviewed. Additional calculations for polyp detection rate (PDR), adenoma detection rate (ADR), and adenoma detection per colonoscopy (ADPC) were performed. Cost analysis was performed to determine cost effectiveness of subscription to the AI program.

RESULTS

2433 AI-aided colonoscopies were performed between July 2021 and June 2022 and compared against 1770 non-AI-aided colonoscopies. AI-aided colonoscopies yielded significantly higher rates of polypectomies (33.6%) as compared with non-AI-aided colonoscopies (28.4%) (p < 0.001). Among the AI-aided colonoscopies, 1050 were reviewed and a final 843 were included for additional analysis. The polypectomy to "hit" ratio was 57.4%, PDR = 45.6%, ADR = 32.4%, and ADPC = 2.08. Histological review showed that 25 polyps (3.13%) were sessile-serrated adenomas. Cost analysis found that the increased polypectomy rates in AI-aided colonoscopes led to an increase in revenue, which covered the subscription cost with an excess of USD 20,000.

CONCLUSION

AI-aided colonoscopy is a cost effective means of improving colonoscopy quality and may help advance colorectal cancer screening in Singapore.

Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening

Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.

Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.

From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment

Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML.

Negative Samples for Improving Object Detection-A Case Study in AI-Assisted Colonoscopy for Polyp Detection

Deep learning object-detection models are being successfully applied to develop computer-aided diagnosis systems for aiding polyp detection during colonoscopies. Here, we evidence the need to include negative samples for both (i) reducing false positives during the polyp-finding phase, by including images with artifacts that may confuse the detection models (e.g., medical instruments, water jets, feces, blood, excessive proximity of the camera to the colon wall, blurred images, etc.) that are usually not included in model development datasets, and (ii) correctly estimating a more realistic performance of the models. By retraining our previously developed YOLOv3-based detection model with a dataset that includes 15% of additional not-polyp images with a variety of artifacts, we were able to generally improve its F1 performance in our internal test datasets (from an average F1 of 0.869 to 0.893), which now include such type of images, as well as in four public datasets that include not-polyp images (from an average F1 of 0.695 to 0.722).

Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis

BACKGROUND

A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification.

OBJECTIVE

We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification.

METHODS

PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality.

RESULTS

In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups.

CONCLUSIONS

The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required.

TRIAL REGISTRATION

PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372.

Computer-aided detection, mucosal exposure device, their combination, and standard colonoscopy for adenoma detection: a randomized controlled trial

BACKGROUND AND AIMS

Computer-aided detection (CADe) and a mucosal exposure device can improve adenoma detection rate (ADR). Potential benefits of combining the 2 modalities have never been studied. This study aimed to compare ADR differences among CADe alone, endocuff-assisted colonoscopy (EAC) alone, and the combination of CADe and EAC (CADe+EAC) with standard colonoscopy.

METHODS

This prospective randomized controlled study included 1245 participants who underwent screening colonoscopy. Participants were randomized to CADe, EAC, CADe+EAC, and standard colonoscopy as a control. The primary outcome was ADR. Secondary outcomes were proximal ADR (pADR), advanced ADR (AADR), and the number of adenomas per colonoscopy (APCs).

RESULTS

ADRs from the control, CADe, EAC, and CADe+EAC groups were 41.9%, 52.2%, 54.0%, and 58.8%, respectively; pADRs were 25.2%, 33.3%, 34.9%, and 37.0%, respectively; AADRs were 7.7%, 8.3%, 8.3%, and 13.6%, respectively; and APCs were .76, 1.11, 1.18, and 1.31, respectively. Significant increases in ADR and pADR were observed between the intervention and control groups (P < .05 in all comparisons). The AADR was significantly higher only in the CADe+EAC group than in the control group (P = .02). The adjusted incidence rate ratios of APCs were significantly higher in the intervention groups versus the control group (P < .01 in all comparisons).

CONCLUSIONS

CADe+EAC significantly improve ADR and AADR over standard colonoscopy. However, although CADe or EAC alone can substantially increase the detection of adenomas, they do not lead to increased detection of advanced adenomas unless used in combination. (Clinical trial registration number: TCTR20200929003.).

Performance evaluation methods for improvements at post-market of artificial intelligence/machine learning-based computer-aided detection/diagnosis/triage in the United States

Computer-aided detection (CADe), computer-aided diagnosis (CADx), and computer-aided simple triage (CAST), which incorporate artificial intelligence (AI) and machine learning (ML), are continually undergoing post-market improvement. Therefore, understanding the evaluation and approval process of improved products is important. This study intended to conduct a comprehensive survey of AI/ML-based CAD products approved by the U.S. Food and Drug Administration (FDA) that had been improved post-market to gain insights into the efficacy and safety required for market approval. A survey of the product code database published by the FDA identified eight products that were improved post-market. The methods used to evaluate the performance of improvements were analysed, and post-market improvements were approved with retrospective data. Reader study testing (RT) or software standalone testing (SA) procedures were conducted retrospectively. Six RT procedures were conducted because of modifications to the intended use. An average of 17.3 readers (minimum 14, maximum 24) participated, and the area under the curve (AUC) was considered the primary endpoint. The addition of study learning data that did not change the intended use and changes in the analysis algorithm were evaluated by SA. The average sensitivity, specificity, and AUC were 93% (minimum 91.1, maximum 97), 89.6% (minimum 85.9, maximum 96), and 0.96 (minimum 0.96, maximum 0.97), respectively. The average interval between applications was 348 days (minimum -18, maximum 975), which showed that the improvements were implemented within approximately one year. This is the first comprehensive study on AI/ML-based CAD products that have been improved post-market to elucidate evaluation points for post-market improvements. The findings will be informative for the industry and academia in developing and improving AI/ML-based CAD.

Deep Learning-Based Model for Identifying Tumors in Endoscopic Images From Patients With Locally Advanced Rectal Cancer Treated With Total Neoadjuvant Therapy

BACKGROUND

A barrier to the widespread adoption of watch-and-wait management for locally advanced rectal cancer is the inaccuracy and variability of identifying tumor response endoscopically in patients who have completed total neoadjuvant therapy (chemoradiotherapy and systemic chemotherapy).

OBJECTIVE

This study aimed to develop a novel method of identifying the presence or absence of a tumor in endoscopic images using deep convolutional neural network-based automatic classification and to assess the accuracy of the method.

DESIGN

In this prospective pilot study, endoscopic images obtained before, during, and after total neoadjuvant therapy were grouped on the basis of tumor presence. A convolutional neural network was modified for probabilistic classification of tumor versus no tumor and trained with an endoscopic image set. After training, a testing endoscopic imaging set was applied to the network.

SETTINGS

The study was conducted at a comprehensive cancer center.

PATIENTS

Images were analyzed from 109 patients who were diagnosed with locally advanced rectal cancer between December 2012 and July 2017 and who underwent total neoadjuvant therapy.

MAIN OUTCOME MEASURES

The main outcomes were accuracy of identifying tumor presence or absence in endoscopic images measured as area under the receiver operating characteristic for the training and testing image sets.

RESULTS

A total of 1392 images were included; 1099 images (468 of no tumor and 631 of tumor) were for training and 293 images (151 of no tumor and 142 of tumor) for testing. The area under the receiver operating characteristic for training and testing was 0.83.

LIMITATIONS

The study had a limited number of images in each set and was conducted at a single institution.

CONCLUSIONS

The convolutional neural network method is moderately accurate in distinguishing tumor from no tumor. Further research should focus on validating the convolutional neural network on a large image set. See Video Abstract at http://links.lww.com/DCR/B959 .

MODELO BASADO EN APRENDIZAJE PROFUNDO PARA IDENTIFICAR TUMORES EN IMGENES ENDOSCPICAS DE PACIENTES CON CNCER DE RECTO LOCALMENTE AVANZADO TRATADOS CON TERAPIA NEOADYUVANTE TOTAL

ANTECEDENTES:Una barrera para la aceptación generalizada del tratamiento de Observar y Esperar para el cáncer de recto localmente avanzado, es la imprecisión y la variabilidad en la identificación de la respuesta tumoral endoscópica, en pacientes que completaron la terapia neoadyuvante total (quimiorradioterapia y quimioterapia sistémica).OBJETIVO:Desarrollar un método novedoso para identificar la presencia o ausencia de un tumor en imágenes endoscópicas utilizando una clasificación automática basada en redes neuronales convolucionales profundas y evaluar la precisión del método.DISEÑO:Las imágenes endoscópicas obtenidas antes, durante y después de la terapia neoadyuvante total se agruparon en base de la presencia del tumor. Se modificó una red neuronal convolucional para la clasificación probabilística de tumor versus no tumor y se entrenó con un conjunto de imágenes endoscópicas. Después del entrenamiento, se aplicó a la red un conjunto de imágenes endoscópicas de prueba.ENTORNO CLINICO:El estudio se realizó en un centro oncológico integral.PACIENTES:Analizamos imágenes de 109 pacientes que fueron diagnosticados de cáncer de recto localmente avanzado entre diciembre de 2012 y julio de 2017 y que se sometieron a terapia neoadyuvante total.PRINCIPALES MEDIDAS DE VALORACION:La precisión en la identificación de la presencia o ausencia de tumores en imágenes endoscópicas medidas como el área bajo la curva de funcionamiento del receptor para los conjuntos de imágenes de entrenamiento y prueba.RESULTADOS:Se incluyeron mil trescientas noventa y dos imágenes: 1099 (468 sin tumor y 631 con tumor) para entrenamiento y 293 (151 sin tumor y 142 con tumor) para prueba. El área bajo la curva operativa del receptor para entrenamiento y prueba fue de 0,83.LIMITACIONES:El estudio tuvo un número limitado de imágenes en cada conjunto y se realizó en una sola institución.CONCLUSIÓN:El método de la red neuronal convolucional es moderadamente preciso para distinguir el tumor de ningún tumor. La investigación adicional debería centrarse en validar la red neuronal convolucional en un conjunto de imágenes mayor. Consulte Video Resumen en http://links.lww.com/DCR/B959 . (Traducción -Dr. Fidel Ruiz Healy ).

Usefulness of a novel computer-aided detection system for colorectal neoplasia: a randomized controlled trial

BACKGROUND AND AIMS

Artificial intelligence-based computer-aid detection (CADe) devices have been recently tested in colonoscopies, increasing the adenoma detection rate (ADR), mainly in Asian populations. However, evidence for the benefit of these devices in the occidental population is still low. We tested a new CADe device, namely, ENDO-AID (OIP-1) (Olympus, Tokyo, Japan), in clinical practice.

METHODS

This randomized controlled trial included 370 consecutive patients who were randomized 1:1 to CADe (n = 185) versus standard exploration (n = 185) from November 2021 to January 2022. The primary endpoint was the ADR. Advanced adenoma was defined as ≥10 mm, harboring high-grade dysplasia, or with a villous pattern. Otherwise, the adenoma was nonadvanced. ADR was assessed in both groups stratified by endoscopist ADR and colon cleansing.

RESULTS

In the intention-to-treat analysis, the ADR was 55.1% (102/185) in the CADe group and 43.8% (81/185) in the control group (P = .029). Nonadvanced ADRs (54.8% vs 40.8%, P = .01) and flat ADRs (39.4 vs 24.8, P = .006), polyp detection rate (67.1% vs 51%; P = .004), and number of adenomas per colonoscopy were significantly higher in the CADe group than in the control group (median [25th-75th percentile], 1 [0-2] vs 0 [0-1.5], respectively; P = .014). No significant differences were found in serrated ADR. After stratification by endoscopist and bowel cleansing, no statistically significant differences in ADR were found.

CONCLUSIONS

Colonoscopy assisted by ENDO-AID (OIP-1) increases ADR and number of adenomas per colonoscopy, suggesting it may aid in the detection of colorectal neoplastic lesions, especially because of its detection of diminutive and flat adenomas. (Clinical trial registration number: NCT04945044.).

The Impacts of Computer-Aided Detection of Colorectal Polyps on Subsequent Colonoscopy Surveillance Intervals: Simulation Study

BACKGROUND

Computer-aided detection (CADe) of colorectal polyps has been shown to increase adenoma detection rates, which would potentially shorten subsequent surveillance intervals.

OBJECTIVE

The purpose of this study is to simulate the potential changes in subsequent colonoscopy surveillance intervals after the application of CADe in a large cohort of patients.

METHODS

We simulated the projected increase in polyp and adenoma detection by universal CADe application in our patients who had undergone colonoscopy with complete endoscopic and histological findings between 2016 and 2020. The simulation was based on bootstrapping the published performance of CADe. The corresponding changes in surveillance intervals for each patient, as recommended by the US Multi-Society Task Force on Colorectal Cancer (USMSTF) or the European Society of Gastrointestinal Endoscopy (ESGE), were determined after the CADe was determined.

RESULTS

A total of 3735 patients who had undergone colonoscopy were included. Based on the simulated CADe effect, the application of CADe would result in 19.1% (n=714) and 1.9% (n=71) of patients having shorter surveillance intervals, according to the USMSTF and ESGE guidelines, respectively. In particular, all (or 2.7% (n=101) of the total) patients who were originally scheduled to have 3-5 years of surveillance would have their surveillance intervals shortened to 3 years, following the USMSTF guidelines. The changes in this group of patients were largely attributed to an increase in the number of adenomas (n=75, 74%) rather than serrated lesions being detected.

CONCLUSIONS

Widespread adoption of CADe would inevitably increase the demand for surveillance colonoscopies with the shortening of original surveillance intervals, particularly following the current USMSTF guideline.

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022)

Background

Colorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis.

Objective

This study uses bibliometrics to analyze research in AI in CRC, summarize the field's history and current status of research, and predict future research directions.

Method

We searched the SCIE database for all literature on CRC and AI. The documents span the period 2002-2022. we used bibliometrics to analyze the data of these papers, such as authors, countries, institutions, and references. Co-authorship, co-citation, and co-occurrence analysis were the main methods of analysis. Citespace, VOSviewer, and SCImago Graphica were used to visualize the results.

Result

This study selected 1,531 articles on AI in CRC. China has published a maximum number of 580 such articles in this field. The U.S. had the most quality publications, boasting an average citation per article of 46.13. Mori Y and Ding K were the two authors with the highest number of articles. Scientific Reports, Cancers, and Frontiers in Oncology are this field's most widely published journals. Institutions from China occupy the top 9 positions among the most published institutions. We found that research on AI in this field mainly focuses on colonoscopy-assisted diagnosis, imaging histology, and pathology examination.

Conclusion

AI in CRC is currently in the development stage with good prospects. AI is currently widely used in colonoscopy, imageomics, and pathology. However, the scope of AI applications is still limited, and there is a lack of inter-institutional collaboration. The pervasiveness of AI technology is the main direction of future housing development in this field.

An Evaluation of Critical Factors for the Cost-Effectiveness of Real-Time Computer-Aided Detection: Sensitivity and Threshold Analyses Using a Microsimulation Model

BACKGROUND

The use of computer-aided detection (CAD) increases the adenoma detection rates (ADRs) during colorectal cancer (CRC) screening/surveillance. This study aimed to evaluate the requirements for CAD to be cost-effective and the impact of CAD on adenoma detection by endoscopists with different ADRs.

METHODS

We developed a semi-Markov microsimulation model to compare the effectiveness of traditional colonoscopy (mean ADR, 26%) to colonoscopy with CAD (mean ADR, 37%). CAD was modeled as having a $75 per-procedure cost. Extensive 1-way sensitivity and threshold analysis were performed to vary cost and ADR of CAD. Multiple scenarios evaluated the potential effect of CAD on endoscopists' ADRs. Outcome measures were reported in incremental cost-effectiveness ratios, with a willingness-to-pay threshold of $100,000/quality-adjusted life year.

RESULTS

When modeling CAD improved ADR for all endoscopists, the CAD cohort had 79 and 34 fewer lifetime CRC cases and deaths, respectively, per 10,000 persons. This scenario was dominant with a cost savings of $143 and incremental effectiveness of 0.01 quality-adjusted life years. Threshold analysis demonstrated that CAD would be cost-effective up to an additional cost of $579 per colonoscopy, or if it increases ADR from 26% to at least 30%. CAD reduced CRC incidence and mortality when limited to improving ADRs for low-ADR endoscopists (ADR <25%), with 67 fewer CRC cases and 28 CRC deaths per 10,000 persons compared with traditional colonoscopy.

CONCLUSIONS

As CAD is implemented clinically, it needs to improve mean ADR from 26% to at least 30% or cost less than $579 per colonoscopy to be cost-effective when compared with traditional colonoscopy. Further studies are needed to understand the impact of CAD on community practice.

Computer-assisted detection versus conventional colonoscopy for proximal colonic lesions: a multicenter, randomized, tandem-colonoscopy study

BACKGROUND AND AIMS

Computer-assisted detection (CADe) is a promising technologic advance that enhances adenoma detection during colonoscopy. However, the role of CADe in reducing missed colonic lesions is uncertain. The aim of this study was to determine the miss rates of proximal colonic lesions by CADe and conventional colonoscopy.

METHODS

This was a prospective, multicenter, randomized, tandem-colonoscopy study conducted in 3 Asian centers. Patients were randomized to receive CADe or conventional white-light colonoscopy during the first withdrawal of the proximal colon (cecum to splenic flexure), immediately followed by tandem examination of the proximal colon with white light in both groups. The primary outcome was adenoma/polyp miss rate, which was defined as any adenoma/polyp detected during the second examination.

RESULTS

Of 223 patients (48.6% men; median age, 63 years) enrolled, 7 patients did not have tandem examination, leaving 108 patients in each group. There was no difference in the miss rate for proximal adenomas (CADe vs conventional: 20.0% vs 14.0%, P = .07) and polyps (26.7% vs 19.6%, P = .06). The CADe group, however, had significantly higher proximal polyp (58.0% vs 46.7%, P = .03) and adenoma (44.7% vs 34.6%, P = .04) detection rates than the conventional group. The mean number of proximal polyps and adenomas detected per patient during the first examination was also significantly higher in the CADe group (polyp: 1.20 vs .86, P = .03; adenoma, .91 vs .61, P = .03). Subgroup analysis showed that CADe enhanced proximal adenoma detection in patients with fair bowel preparation, shorter withdrawal time, and endoscopists with lower adenoma detection rate.

CONCLUSIONS

This multicenter trial from Asia confirmed that CADe can further enhance proximal adenoma and polyp detection but may not be able to reduce the number of missed proximal colonic lesions. (Clinical trial registration number: NCT04294355.).

Automation: A revolutionary vision of artificial intelligence in theranostics

The last two decades have witnessed an extraordinary evolution of automation and artificial intelligence (AI), which has become an integral part of our daily lives. Lately, AI has also been assimilated in the field of medicine to upgrade overall healthcare system and encourage personalized treatment. Theranostics literally meaning combination of diagnosis and therapeutics, is a targeted pharmacotherapy, based on specific targeted diagnostic tests. Numerous theranostic agents/biomarkers are available which can identify the most beneficial treatment, correct dose or predict response to a medicine, thus, maximizing drug efficacy, minimizing toxicity and providing informed treatment choice. For instance, a statistics based Cluster-FLIM technology provides precise data on drug-receptor binding behavior in biological tissues using fluorescence real experimental imaging. Automated Idylla™ qPCR System is another approach in oncology to determine the EGFR mutations at initial stage as well as during the treatment and also assists the oncologist in designing the treatment protocol. Recent incorporation of automation and AI in theranostics has brought a drastic change in early detection and treatment protocols for various diseases such as cancer and diabetes. Also, it leads to quick analysis of number of diverse experimental datum with accuracy. The approach mainly uses computer algorithms to unveil relevant and significant information from clinical data, thereby assisting in making accurate, logical and pertinent decisions. This review highlights the emerging uses/role of automation and AI in theranostics, technical difficulties and focuses on its future prospects to facilitate a patient specific, reliable and efficient pharmacotherapy.

Variability in adenoma detection rate in control groups of randomized colonoscopy trials: a systematic review and meta-analysis

BACKGROUND AND AIMS

Adenoma detection rate (ADR) is still the main surrogate outcome parameter of screening colonoscopy, but most studies include mixed indications, and basic ADR is quite variable. We therefore looked at the control groups in randomized ADR trials using advanced imaging or mechanical methods to find out whether indications or other factors influence ADR levels.

METHODS

Patients in the control groups of randomized controlled trials (RCTs) on ADR increase using various methods were collected based on a systematic review; this control group had to use high-definition white-light endoscopy performed between 2008 and 2021. Random-effects meta-analysis was used to pool ADR in control groups and its 95% confidence interval (CI) according to clinical (indication and demographic), study setting (tandem/parallel, number of centers, sample size), and technical (type of intervention, withdrawal time) parameters. Interstudy heterogeneity was reported with the I² statistic. Multivariable mixed-effects meta-regression was performed for potentially relevant variables.

RESULTS

From 80 studies, 25,304 patients in the respective control groups were included. ADR in control arms varied between 8.2% and 68.1% with a high degree of heterogeneity (I² = 95.1%; random-effect pooled value, 37.5%; 95% CI, 34.6‒40.5). There was no difference in ADR levels between primary colonoscopy screening (12 RCTs, 15%) and mixed indications including screening/surveillance and diagnostic colonoscopy; however, fecal immunochemical testing as an indication for colonoscopy was an independent predictor of ADR (odds ratio [OR], 1.6; 95% CI, 1.1-2.4). Other well-known parameters were confirmed by our analysis such as age (OR, 1.038; 95% CI, 1.004-1.074), sex (male sex: OR, 1.02; 95% CI, 1.01-1.03), and withdrawal time (OR, 1.1; 95% CI, 1.0-1.1). The type of intervention (imaging vs mechanical) had no influence, but methodologic factors did: More recent year of publication and smaller sample size were associated with higher ADR.

CONCLUSIONS

A high level of variability was found in the level of ADR in the control groups of RCTs. With regards to indications, only fecal immunochemical test-based colonoscopy studies influenced basic ADR, and primary colonoscopy screening appeared to be similar to other indications. Standardization for variables related to clinical, methodologic, and technical parameters is required to achieve generalizability and reproducibility.

Effect of computer-aided colonoscopy on adenoma miss rates and polyp detection: A systematic review and meta-analysis

BACKGROUND AND AIM

Multiple computer-aided techniques utilizing artificial intelligence (AI) have been created to improve the detection of polyps during colonoscopy and thereby reduce the incidence of colorectal cancer. While adenoma detection rates (ADR) and polyp detection rates (PDR) are important colonoscopy quality indicators, adenoma miss rates (AMR) may better quantify missed lesions, which can ultimately lead to interval colorectal cancer. The purpose of this systematic review and meta-analysis was to determine the efficacy of computer-aided colonoscopy (CAC) with respect to AMR, ADR, and PDR in randomized controlled trials.

METHODS

A comprehensive, systematic literature search was performed across multiple databases in September of 2022 to identify randomized, controlled trials that compared CAC with traditional colonoscopy. Primary outcomes were AMR, ADR, and PDR.

RESULTS

Fourteen studies totaling 10 928 patients were included in the final analysis. There was a 65% reduction in the adenoma miss rate with CAC (OR, 0.35; 95% CI, 0.25-0.49, P < 0.001, I²  = 50%). There was a 78% reduction in the sessile serrated lesion miss rate with CAC (OR, 0.22; 95% CI, 0.08-0.65, P < 0.01, I²  = 0%). There was a 52% increase in ADR in the CAC group compared with the control group (OR, 1.52; 95% CI, 1.39-1.67, P = 0.04, I²  = 47%). There was 93% increase in the number of adenomas > 10 mm detected per colonoscopy with CAC (OR 1.93; 95% CI, 1.18-3.16, P < 0.01, I²  = 0%).

CONCLUSIONS

The results of the present study demonstrate the promise of CAC in improving AMR, ADR, PDR across a spectrum of size and morphological lesion characteristics.

Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility, and usability

BACKGROUND AND AIMS

Publicly available databases containing colonoscopic imaging data are valuable resources for artificial intelligence (AI) research. Currently, little is known regarding the available number and content of these databases. This review aimed to describe the availability, accessibility, and usability of publicly available colonoscopic imaging databases, focusing on polyp detection, polyp characterization, and quality of colonoscopy.

METHODS

A systematic literature search was performed in MEDLINE and Embase to identify AI studies describing publicly available colonoscopic imaging databases published after 2010. Second, a targeted search using Google's Dataset Search, Google Search, GitHub, and Figshare was done to identify databases directly. Databases were included if they contained data about polyp detection, polyp characterization, or quality of colonoscopy. To assess accessibility of databases, the following categories were defined: open access, open access with barriers, and regulated access. To assess the potential usability of the included databases, essential details of each database were extracted using a checklist derived from the Checklist for Artificial Intelligence in Medical Imaging.

RESULTS

We identified 22 databases with open access, 3 databases with open access with barriers, and 15 databases with regulated access. The 22 open access databases contained 19,463 images and 952 videos. Nineteen of these databases focused on polyp detection, localization, and/or segmentation; 6 on polyp characterization, and 3 on quality of colonoscopy. Only half of these databases have been used by other researcher to develop, train, or benchmark their AI system. Although technical details were in general well reported, important details such as polyp and patient demographics and the annotation process were under-reported in almost all databases.

CONCLUSIONS

This review provides greater insight on public availability of colonoscopic imaging databases for AI research. Incomplete reporting of important details limits the ability of researchers to assess the usability of current databases.

Artificial intelligence empowers the second-observer strategy for colonoscopy: a randomized clinical trial

Background

In colonoscopy screening for colorectal cancer, human vision limitations may lead to higher miss rate of lesions; artificial intelligence (AI) assistance has been demonstrated to improve polyp detection. However, there still lacks direct evidence to demonstrate whether AI is superior to trainees or experienced nurses as a second observer to increase adenoma detection during colonoscopy. In this study, we aimed to compare the effectiveness of assistance from AI and human observer during colonoscopy.

Methods

A prospective multicenter randomized study was conducted from 2 September 2019 to 29 May 2020 at four endoscopy centers in China. Eligible patients were randomized to either computer-aided detection (CADe)-assisted group or observer-assisted group. The primary outcome was adenoma per colonoscopy (APC). Secondary outcomes included polyp per colonoscopy (PPC), adenoma detection rate (ADR), and polyp detection rate (PDR). We compared continuous variables and categorical variables by using R studio (version 3.4.4).

Results

A total of 1,261 (636 in the CADe-assisted group and 625 in the observer-assisted group) eligible patients were analysed. APC (0.42 vs 0.35, P = 0.034), PPC (1.13 vs 0.81, P < 0.001), PDR (47.5% vs 37.4%, P < 0.001), ADR (25.8% vs 24.0%, P = 0.464), the number of detected sessile polyps (683 vs 464, P < 0.001), and sessile adenomas (244 vs 182, P = 0.005) were significantly higher in the CADe-assisted group than in the observer-assisted group. False detections of the CADe system were lower than those of the human observer (122 vs 191, P < 0.001).

Conclusions

Compared with the human observer, the CADe system may improve the clinical outcome of colonoscopy and reduce disturbance to routine practice (Chictr.org.cn No.: ChiCTR1900025235).

Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions

Background and Objectives: Device-assisted enteroscopy (DAE) has a significant role in approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and can be associated with many nosological entities, namely Crohn's disease. Although the application of artificial intelligence (AI) is growing exponentially in various imaged-based gastroenterology procedures, there is still a lack of evidence of the AI technical feasibility and clinical applicability of DAE. This study aimed to develop and test a multi-brand convolutional neural network (CNN)-based algorithm for automatically detecting ulcers and erosions in DAE. Materials and Methods: A unicentric retrospective study was conducted for the development of a CNN, based on a total of 250 DAE exams. A total of 6772 images were used, of which 678 were considered ulcers or erosions after double-validation. Data were divided into a training and a validation set, the latter being used for the performance assessment of the model. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the curve precision-recall curve (AUC-PR). Results: Sensitivity, specificity, PPV, and NPV were respectively 88.5%, 99.7%, 96.4%, and 98.9%. The algorithm's accuracy was 98.7%. The AUC-PR was 1.00. The CNN processed 293.6 frames per second, enabling AI live application in a real-life clinical setting in DAE. Conclusion: To the best of our knowledge, this is the first study regarding the automatic multi-brand panendoscopic detection of ulcers and erosions throughout the digestive tract during DAE, overcoming a relevant interoperability challenge. Our results highlight that using a CNN to detect this type of lesion is associated with high overall accuracy. The development of binary CNN for automatically detecting clinically relevant endoscopic findings and assessing endoscopic inflammatory activity are relevant steps toward AI application in digestive endoscopy, particularly for panendoscopic evaluation.

Risk of Metachronous Advanced Colorectal Neoplasia After Removal of Diminutive Versus Small Nonadvanced Adenomas: A Multicenter Study

BACKGROUND

Current postpolypectomy guidelines treat 1-9 mm nonadvanced adenomas (NAAs) as carrying the same level of risk for metachronous advanced colorectal neoplasia (ACRN).

AIMS

To evaluate whether small (6-9 mm) NAAs are associated with a greater risk of metachronous ACRN than diminutive (1-5 mm) NAAs.

METHODS

We retrospectively evaluated 10,060 index colonoscopies performed from July 2011 to June 2019. A total of 1369 patients aged ≥ 40 years with index NAAs and having follow-up examinations were categorized into 5 groups based on size and number of index findings: Group 1, ≤ 2 diminutive NAAs (n = 655); Group 2, ≤ 2 small NAAs (n = 529); Group 3, 3-4 diminutive NAAs (n = 78); Group 4, 3-4 small NAAs (n = 65); and Group 5, 5-10 NAAs (n = 42). Size was classified based on the largest NAA. ACRN was defined as finding an advanced adenoma or colorectal cancer at follow-up.

RESULTS

The absolute risk of metachronous ACRN increased from 7.2% in patients with all diminutive NAAs to 12.2% in patients with at least 1 small NAA (P = 0.002). Patients in Group 2 (adjusted odds ratio [AOR] 1.89; 95% confidence interval [CI], 1.21-2.95), Group 3 (AOR 2.40; 95% CI 1.78-4.90), Group 4 (AOR 2.77; 95% CI 1.35-5.66), and Group 5 (AOR 3.71; 95% CI 1.65-8.37) were associated with an increased risk of metachronous ACRN compared with Group 1.

CONCLUSIONS

Patients with small NAAs have an increased risk of metachronous ACRN. Postpolypectomy guidelines should consider including risk stratification between small and diminutive adenomas.

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.

An interpretable artificial intelligence system for detecting risk factors of gastroesophageal variceal bleeding

Bleeding risk factors for gastroesophageal varices (GEV) detected by endoscopy in cirrhotic patients determine the prophylactical treatment patients will undergo in the following 2 years. We propose a methodology for measuring the risk factors. We create an artificial intelligence system (ENDOANGEL-GEV) containing six models to segment GEV and to classify the grades (grades 1-3) and red color signs (RC, RC0-RC3) of varices. It also summarizes changes in the above results with region in real time. ENDOANGEL-GEV is trained using 6034 images from 1156 cirrhotic patients across three hospitals (dataset 1) and validated on multicenter datasets with 11009 images from 141 videos (dataset 2) and in a prospective study recruiting 161 cirrhotic patients from Renmin Hospital of Wuhan University (dataset 3). In dataset 1, ENDOANGEL-GEV achieves intersection over union values of 0.8087 for segmenting esophageal varices and 0.8141 for gastric varices. In dataset 2, the system maintains fairly accuracy across images from three hospitals. In dataset 3, ENDOANGEL-GEV surpasses attended endoscopists in detecting RC of GEV and classifying grades (p < 0.001). When ranking the risk of patients combined with the Child‒Pugh score, ENDOANGEL-GEV outperforms endoscopists for esophageal varices (p < 0.001) and shows comparable performance for gastric varices (p = 0.152). Compared with endoscopists, ENDOANGEL-GEV may help 12.31% (16/130) more patients receive the right intervention. We establish an interpretable system for the endoscopic diagnosis and risk stratification of GEV. It will assist in detecting the first bleeding risk factors accurately and expanding the scope of quantitative measurement of diseases.

Evaluation of a real-time computer-aided polyp detection system during screening colonoscopy: AI-DETECT study

BACKGROUND

Polyp detection and resection during colonoscopy significantly reduce long-term colorectal cancer risk. Computer-aided detection (CADe) may increase polyp identification but has undergone limited clinical evaluation. Our aim was to assess the effectiveness of CADe at colonoscopy within a bowel cancer screening program (BCSP).

METHODS

This prospective, randomized controlled trial involved all eight screening-accredited colonoscopists at an English National Health Service (NHS) BCSP center (February 2020 to December 2021). Patients were randomized to CADe or standard colonoscopy. Patients meeting NHS criteria for bowel cancer screening were included. The primary outcome of interest was polyp detection rate (PDR).

RESULTS

658 patients were invited and 44 were excluded. A total of 614 patients were randomized to CADe (n = 308) or standard colonoscopy (n = 306); 35 cases were excluded from the per-protocol analysis due to poor bowel preparation (n = 10), an incomplete procedure (n = 24), or a data issue (n = 1). Endocuff Vision was frequently used and evenly distributed (71.7 % CADe and 69.2 % standard). On intention-to-treat (ITT) analysis, there was a borderline significant difference in PDR (85.7 % vs. 79.7 %; P = 0.05) but no significant difference in adenoma detection rate (ADR; 71.4 % vs. 65.0 %; P = 0.09) for CADe vs. standard groups, respectively. On per-protocol analysis, no significant difference was observed in these rates. There was no significant difference in procedure times.

CONCLUSIONS

In high-performing colonoscopists in a BCSP who routinely used Endocuff Vision, CADe improved PDR but not ADR. CADe appeared to have limited benefit in a BCSP setting where procedures are performed by experienced colonoscopists.

Artificial Intelligence and Advanced Melanoma: Treatment Management Implications

Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities that would be more complex otherwise. Even in the medical field, and specifically in oncology, many studies in recent years have highlighted the possible helping role that AI could play in clinical and therapeutic patient management. In specific contexts, clinical decisions are supported by "intelligent" machines and the development of specific softwares that assist the specialist in the management of the oncology patient. Melanoma, a highly heterogeneous disease influenced by several genetic and environmental factors, to date is still difficult to manage clinically in its advanced stages. Therapies often fail, due to the establishment of intrinsic or secondary resistance, making clinical decisions complex. In this sense, although much work still needs to be conducted, numerous evidence shows that AI (through the processing of large available data) could positively influence the management of the patient with advanced melanoma, helping the clinician in the most favorable therapeutic choice and avoiding unnecessary treatments that are sure to fail. In this review, the most recent applications of AI in melanoma will be described, focusing especially on the possible finding of this field in the management of drug treatments.

Artificial intelligence and colon capsule endoscopy: Automatic detection of ulcers and erosions using a convolutional neural network

BACKGROUND AND AIM

Colon capsule endoscopy (CCE) has become a minimally invasive alternative for conventional colonoscopy. Nevertheless, each CCE exam produces between 50 000 and 100 000 frames, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNNs) are a type of artificial intelligence (AI) architecture with high performance in image analysis. This study aims to develop a CNN model for the identification of colonic ulcers and erosions in CCE images.

METHODS

A CNN model was designed using a database of CCE images. A total of 124 CCE exams performed between 2010 and 2020 in two centers were reviewed. For CNN development, a total of 37 319 images were extracted, 33 749 showing normal colonic mucosa and 3570 showing colonic ulcers and erosions. Datasets for CNN training, validation, and testing were created. The performance of the algorithm was evaluated regarding its sensitivity, specificity, positive and negative predictive values, accuracy, and area under the curve.

RESULTS

The network had a sensitivity of 96.9% and a specificity of 99.9% specific for the detection of colonic ulcers and erosions. The algorithm had an overall accuracy of 99.6%. The area under the curve was 1.00. The CNN had an image processing capacity of 90 frames per second.

CONCLUSIONS

The developed algorithm is the first CNN-based model to accurately detect ulcers and erosions in CCE images, also providing a good image processing performance. The development of these AI systems may contribute to improve both the diagnostic and time efficiency of CCE exams, facilitating CCE adoption to routine clinical practice.

Efficacy of a computer-aided detection system in a fecal immunochemical test-based organized colorectal cancer screening program: a randomized controlled trial (AIFIT study)

BACKGROUND

Computer-aided detection (CADe) increases adenoma detection in primary screening colonoscopy. The potential benefit of CADe in a fecal immunochemical test (FIT)-based colorectal cancer (CRC) screening program is unknown. This study assessed whether use of CADe increases the adenoma detection rate (ADR) in a FIT-based CRC screening program.

METHODS

In a multicenter, randomized trial, FIT-positive individuals aged 50-74 years undergoing colonoscopy, were randomized (1:1) to receive high definition white-light (HDWL) colonoscopy, with or without a real-time deep-learning CADe by endoscopists with baseline ADR > 25 %. The primary outcome was ADR. Secondary outcomes were mean number of adenomas per colonoscopy (APC) and advanced adenoma detection rate (advanced-ADR). Subgroup analysis according to baseline endoscopists' ADR (≤ 40 %, 41 %-45 %, ≥ 46 %) was also performed.

RESULTS

800 individuals (median age 61.0 years [interquartile range 55-67]; 409 men) were included: 405 underwent CADe-assisted colonoscopy and 395 underwent HDWL colonoscopy alone. ADR and APC were significantly higher in the CADe group than in the HDWL arm: ADR 53.6 % (95 %CI 48.6 %-58.5 %) vs. 45.3 % (95 %CI 40.3 %-50.45 %; RR 1.18; 95 %CI 1.03-1.36); APC 1.13 (SD 1.54) vs. 0.90 (SD 1.32; P  = 0.03). No significant difference in advanced-ADR was found (18.5 % [95 %CI 14.8 %-22.6 %] vs. 15.9 % [95 %CI 12.5 %-19.9 %], respectively). An increase in ADR was observed in all endoscopist groups regardless of baseline ADR.

CONCLUSIONS

Incorporating CADe significantly increased ADR and APC in the framework of a FIT-based CRC screening program. The impact of CADe appeared to be consistent regardless of endoscopist baseline ADR.