Trial protocol for COLO-DETECT: A randomized controlled trial of lesion detection comparing colonoscopy assisted by the GI Genius™ artificial intelligence endoscopy module with standard colonoscopy

Polyp detection with colonoscopy assisted by the GI Genius artificial intelligence endoscopy module compared with standard colonoscopy in routine colonoscopy practice (COLO-DETECT): a multicentre, open-label, parallel-arm, pragmatic randomised controlled trial

BACKGROUND

Increased polyp detection during colonoscopy is associated with decreased post-colonoscopy colorectal cancer incidence and mortality. The COLO-DETECT trial aimed to assess the clinical effectiveness of the GI Genius intelligent endoscopy module for polyp detection, comparing colonoscopy assisted by GI Genius (computer-aided detection [CADe]-assisted colonoscopy) with standard colonoscopy in routine practice.

METHODS

We did a multicentre, open-label, parallel-arm, pragmatic randomised controlled trial in 12 National Health Service (NHS) hospitals (ten NHS Trusts) in England, among adults (aged ≥18 years) undergoing planned colonoscopy for gastrointestinal symptoms or for surveillance due to personal or family history (ie, symptomatic indications), or colorectal cancer screening. Randomisation (1:1) to CADe-assisted colonoscopy or standard colonoscopy was done with a web-based dynamic adaptive algorithm, immediately before colonoscopy, with stratification by age group, sex, colonoscopy indication (screening or symptomatic), and NHS Trust. Recruiting staff, participants, and colonoscopists were unmasked to trial allocation; histopathologists, co-chief investigators, and trial statisticians were masked. CADe-assisted colonoscopy consisted of standard colonoscopy plus the GI Genius module active for at least the entire inspection phase of colonoscope withdrawal. The primary outcome was mean adenomas per procedure (total number of adenomas detected divided by total number of procedures); the key secondary outcome was adenoma detection rate (proportion of colonoscopies with at least one adenoma). Analysis was by intention to treat (ITT), with outcomes compared between groups by mixed-effects regression modelling, in which effect estimates were adjusted for randomisation stratification variables. Data were imputed for outcome measures with more than 5% missing values. All participants who underwent colonoscopy were assessed for safety. The trial is registered on ISRCTN (ISRCTN10451355) and ClinicalTrials.gov (NCT04723758), and is complete.

FINDINGS

Between March 29, 2021, and April 6, 2023, 2032 participants (1132 [55·7%] male, 900 [44·3%] female; mean age 62·4 years [SD 10·8]) were recruited and randomly assigned: 1015 to CADe-assisted colonoscopy and 1017 to standard colonoscopy. 1231 (60·6%) participants were undergoing screening and 801 (39·4%) had symptomatic indications. Mean adenomas per procedure was 1·56 (SD 2·82; n=1001 participants with available data) in the CADe-assisted colonoscopy group versus 1·21 (1·91; n=1009) in the standard colonoscopy group, representing an adjusted mean difference of 0·36 (95% CI 0·14-0·57; adjusted incidence rate ratio 1·30 [95% CI 1·15-1·47], p<0·0001). Adenomas were detected in 555 (56·6%) of 980 participants in the CADe-assisted colonoscopy group versus 477 (48·4%) of 986 in the standard colonoscopy group, representing a proportion difference of 8·3% (95% CI 3·9-12·7; adjusted odds ratio 1·47 [95% CI 1·21-1·78], p<0·0001). Numbers of adverse events were similar between the CADe-assisted colonoscopy and standard colonoscopy groups (adverse events: 25 vs 19; serious adverse events: four vs six), and no adverse events in the CADe-assisted colonoscopy group were deemed to be related to GI Genius use on independent review.

INTERPRETATION

Results of the COLO-DETECT trial support the use of GI Genius to increase detection of premalignant colorectal polyps in routine colonoscopy practice.

FUNDING

Medtronic.

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

Application of artificial intelligence in colorectal cancer screening by colonoscopy: Future prospects (Review)

Colorectal cancer (CRC) has become a severe global health concern, with the third‑high incidence and second‑high mortality rate of all cancers. The burden of CRC is expected to surge to 60% by 2030. Fortunately, effective early evidence‑based screening could significantly reduce the incidence and mortality of CRC. Colonoscopy is the core screening method for CRC with high popularity and accuracy. Yet, the accuracy of colonoscopy in CRC screening is related to the experience and state of operating physicians. It is challenging to maintain the high CRC diagnostic rate of colonoscopy. Artificial intelligence (AI)‑assisted colonoscopy will compensate for the above shortcomings and improve the accuracy, efficiency, and quality of colonoscopy screening. The unique advantages of AI, such as the continuous advancement of high‑performance computing capabilities and innovative deep‑learning architectures, which hugely impact the control of colorectal cancer morbidity and mortality expectancy, highlight its role in colonoscopy screening.

Effectiveness and application of artificial intelligence for endoscopic screening of colorectal cancer: the future is now

INTRODUCTION

Artificial intelligence (AI) in gastrointestinal endoscopy includes systems designed to interpret medical images and increase sensitivity during examination. This may be a promising solution to human biases and may provide support during diagnostic endoscopy.

AREAS COVERED

This review aims to summarize and evaluate data supporting AI technologies in lower endoscopy, addressing their effectiveness, limitations, and future perspectives.

EXPERT OPINION

Computer-aided detection (CADe) systems have been studied with promising results, allowing for an increase in adenoma detection rate (ADR), adenoma per colonoscopy (APC), and a reduction in adenoma miss rate (AMR). This may lead to an increase in the sensitivity of endoscopic examinations and a reduction in the risk of interval-colorectal cancer. In addition, computer-aided characterization (CADx) has also been implemented, aiming to distinguish adenomatous and non-adenomatous lesions through real-time assessment using advanced endoscopic imaging techniques. Moreover, computer-aided quality (CADq) systems have been developed with the aim of standardizing quality measures in colonoscopy (e.g. withdrawal time and adequacy of bowel cleansing) both to improve the quality of examinations and set a reference standard for randomized controlled trials.

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.

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.