Artificial intelligence for colonoscopy: the new Silk Road

Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis

BACKGROUNDS AND AIMS

Mucosal healing (MH) in inflammatory bowel diseases (IBD) is an important landmark for clinical decision making. Artificial intelligence systems (AI) that automatically deliver the grade of endoscopic inflammation may solve moderate interobserver agreement and the need of central reading in clinical trials.

METHODS

We performed a systematic review of EMBASE and MEDLINE databases up to 01/12/2022 following PRISMA and the Joanna Briggs Institute methodologies to answer the following question: "Can AI replace endoscopists when assessing MH in IBD?". The research was restricted to ulcerative colitis (UC), and a diagnostic odds ratio (DOR) meta-analysis was performed. Risk of bias was evaluated with QUADAS-2 tool.

RESULTS

A total of 21 / 739 records were selected for full text evaluation, and 12 were included in the meta-analysis. Deep learning algorithms based on convolutional neural networks architecture achieved a satisfactory performance in evaluating MH on UC, with sensitivity, specificity, DOR and SROC of respectively 0.91(CI95 %:0.86-0.95);0.89(CI95 %:0.84-0.93);92.42(CI95 %:54.22-157.53) and 0.957 when evaluating fixed images (n = 8) and 0.86(CI95 %:0.75-0.93);0.91(CI95 %:0.87-0.94);70.86(CI95 %:24.63-203.86) and 0.941 when evaluating videos (n = 6). Moderate-high levels of heterogeneity were noted, limiting the quality of the evidence.

CONCLUSIONS

AI systems showed high potential in detecting MH in UC with optimal diagnostic performance, although moderate-high heterogeneity of the data was noted. Standardised and shared AI training may reduce heterogeneity between systems.

Application value of an artificial intelligence-based diagnosis and recognition system in gastroscopy training for graduate students in gastroenterology: a preliminary study

OBJECTIVE

This study aimed to discuss the application value of an artificial intelligence-based diagnosis and recognition system (AIDRS) in the teaching activities for Bachelor of Medicine and Bachelor of Surgery (MBBS) in China. The learning performance of graduate students in gastroenterology during gastroscopy training with and without AIDRS was assessed.

METHODS

The study recruited 32 graduate students of the gastroenterology program at Jiangsu province hospital of Chinese medicine and Xiangyang No. 1 People's Hospital from March 2018 to March 2022 and randomly divided them into AIDRS (n = 16) and non-AIDRS (n = 16) groups. The AIDRS software was used for real-time monitoring of blind spots of gastroscopy to aid in lesion diagnosis and recognition in the AIDRS group. Only a conventional gastroscopic procedure was implemented in the non-AIDRS group. The final performance score, success rate of gastroscopy, lesion detection rate, and pain score of patients were compared between the two groups during gastroscopy. A self-prepared teaching and learning satisfaction questionnaire was administered to the two groups of students.

RESULTS

The AIDRS group had a higher final performance score (92.60 ± 2.83 vs. 89.21 ± 3.57, t = 2.98, P < 0.05), a higher success rate of gastroscopy (448/480 vs. 417/480, χ2 = 11.23, P < 0.05), and a higher detection rate of lesions (51/52 vs. 41/53, χ2 = 8.56, P < 0.05) compared with the non-AIDRS group. The pain scores of patients were lower in the AIDRS group than in the non-AIDRS group (3.40 [2.23, 3.98] vs. 4.45 [3.72, 4.75], Z = 3.04, P < 0.05). Besides, the average time for gastroscopy was lower in the AIDRS group than in the non-AIDRS group (7.15 ± 1.24 vs. 8.21 ± 1.26, t = 2.38, P = 0.02). The overall satisfaction level with the teaching program was higher in the AIDRS group (43.51 ± 2.29 vs. 40.93 ± 2.07, t = 3.33, P < 0.05).

CONCLUSION

In the context of medicine-education cooperation, AIDRS offered valuable assistance in gastroscopy training and increased the success rate of gastroscopy and teaching and learning satisfaction. AIDRS is worthy of wider-scale promotion.

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.