Artificial intelligence in colonoscopy

Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going?

Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.

Topic highlight on texture and color enhancement imaging in gastrointestinal diseases

Olympus Corporation developed texture and color enhancement imaging (TXI) as a novel image-enhancing endoscopic technique. This topic highlights a series of hot-topic articles that investigated the efficacy of TXI for gastrointestinal disease identification in the clinical setting. A randomized controlled trial demonstrated improvements in the colorectal adenoma detection rate (ADR) and the mean number of adenomas per procedure (MAP) of TXI compared with those of white-light imaging (WLI) observation (58.7% vs 42.7%, adjusted relative risk 1.35, 95%CI: 1.17-1.56; 1.36 vs 0.89, adjusted incident risk ratio 1.48, 95%CI: 1.22-1.80, respectively). A cross-over study also showed that the colorectal MAP and ADR in TXI were higher than those in WLI (1.5 vs 1.0, adjusted odds ratio 1.4, 95%CI: 1.2-1.6; 58.2% vs 46.8%, 1.5, 1.0-2.3, respectively). A randomized controlled trial demonstrated non-inferiority of TXI to narrow-band imaging in the colorectal mean number of adenomas and sessile serrated lesions per procedure (0.29 vs 0.30, difference for non-inferiority -0.01, 95%CI: -0.10 to 0.08). A cohort study found that scoring for ulcerative colitis severity using TXI could predict relapse of ulcerative colitis. A cross-sectional study found that TXI improved the gastric cancer detection rate compared to WLI (0.71% vs 0.29%). A cross-sectional study revealed that the sensitivity and accuracy for active Helicobacter pylori gastritis in TXI were higher than those of WLI (69.2% vs 52.5% and 85.3% vs 78.7%, respectively). In conclusion, TXI can improve gastrointestinal lesion detection and qualitative diagnosis. Therefore, further studies on the efficacy of TXI in clinical practice are required.

Requirement of image standardization for AI-based macroscopic diagnosis for surgical specimens of gastric cancer

PURPOSE

The pathological diagnosis of surgically resected gastric cancer involves both a macroscopic diagnosis by gross observation and a microscopic diagnosis by microscopy. Macroscopic diagnosis determines the location and stage of the disease and the involvement of other organs and surgical margin. Lesion recognition is, thus, an important diagnostic step that requires a skilled pathologist. Nonetheless, artificial intelligence (AI) technologies could allow even inexperienced doctors and laboratory technicians to examine surgically resected specimens without the need for pathologists. However, organ imaging conditions vary across hospitals, and an AI algorithm created in one setting may not work properly in another. Thus, we identified and standardized factors affecting the quality of pathological macroscopic images, which could further affect lesion identification using AI.

METHODS

We examined necessary image standardization for developing cancer detection AI for surgically resected gastric cancer by changing the following imaging conditions: focus, resolution, brightness, and contrast.

RESULTS

Regarding focus, brightness, and contrast, the farther away the test data were from the training macro-image, the less likely the inference was to be correct. Little change was observed for resolution, even with differing conditions for the training and test data. Regarding focus, brightness, and contrast, there were conditions appropriate for AI. Contrast, in particular, was far from the conditions appropriate for humans.

CONCLUSION

Standardizing focus, brightness, and contrast is important in the development of AI methodologies for lesion detection in surgically resected gastric cancer. This standardization is essential for AI to be implemented across hospitals.

Prospective study of computer-aided detection of colorectal adenomas in hospitalized patients

BACKGROUND

Adenoma detection with polypectomy during total colonoscopy reduces the incidence of colorectal cancer (CRC) and colorectal cancer-associated mortality. The adenoma detection rate (ADR) is an established quality indicator, which is associated with a decreased risk for interval cancer. An increase in ADR could be demonstrated for several artificially intelligent, real-time computer-aided detection (CADe) systems in selected patients. Most studies concentrated on outpatient colonoscopies. This sector often lacks funds for applying costly innovations like CADe. Hospitals are more likely to implement CADe and information about the impact of CADe in the distinct patient cohort of hospitalized patients is scarce.

METHODS

In this prospective, randomized-controlled study, we compared colonoscopies performed with or without computer-aided detection (CADe) system (GI Genius, Medtronic) performed at University Medical Center Schleswig-Holstein, Campus Luebeck. The primary endpoint was ADR.

RESULTS

Overall, 232 patients were randomized with n = 122 patients in the CADe arm and n = 110 patients in the control arm. Median age was 66 years (interquartile range 51-77). Indication for colonoscopy was most often workup for gastrointestinal symptoms (88.4%) followed by screening, post-polypectomy and post-CRC surveillance (each 3.9%). Withdrawal time was significantly prolonged (11 vs. 10 min, p = 0.039), without clinical relevance. Complication rate was not different between the arms (0.8% vs. 4.5%; p = 0.072). The ADR was significantly increased in the CADe arm compared to the control (33.6% vs. 18.1%, p = 0.008). ADR increase was particularly strong for the detection in elderly patients aged ≥50 years (OR 6.3, 95% CI 1.7 - 23.1, p = 0.006).

CONCLUSION

The use of CADe is safe and increases ADR in hospitalized patients.

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.

Dietary counseling based on artificial intelligence for patients with nonalcoholic fatty liver disease

BACKGROUND

About 25% of the general population in Japan are reported to have nonalcoholic fatty liver disease (NAFLD). NAFLD and nonalcoholic steatohepatitis carry a risk of progressing further to hepatocellular carcinoma. The primary treatment for NAFLD is dietary therapy. Dietary counseling plays an essential role in dietary therapy. Although artificial intelligence (AI)-based nutrition management software applications have been developed and put into practical use in recent years, the majority focus on weight loss or muscle strengthening, and no software has been developed for patient use in clinical practice.

AIM

To examine whether effective dietary counseling is possible using AI-based nutrition management software.

METHODS

NAFLD patients who had been assessed using an AI-based nutrition management software application (Calomeal) that automatically analyzed images of meals photographed by patients and agreed to receive dietary counseling were given dietary counseling. Blood biochemistry tests were performed before (baseline) and 6 mo after (6M follow-up) dietary counseling. After the dietary counseling, the patients were asked to complete a questionnaire survey.

RESULTS

A total of 29 patients diagnosed with NAFLD between August 2020 and March 2022 were included. There were significant decreases in liver enzyme and triglyceride levels at the 6M follow-up compared to baseline. The food analysis capability of the AI used by Calomeal in this study was 75.1%. Patient satisfaction with the AI-based dietary counselling was high.

CONCLUSION

AI-based nutrition management appeared to raise awareness of dietary habits among NAFLD patients. However, it did not directly alleviate the burden of registered dietitians, and improvements are much anticipated.

Application Status and Prospects of Artificial Intelligence in Peptic Ulcers

Peptic ulcer (PU) is a common and frequently occurring disease. Although PU seriously threatens the lives and health of global residents, the applications of artificial intelligence (AI) have strongly promoted diversification and modernization in the diagnosis and treatment of PU. This minireview elaborates on the research progress of AI in the field of PU, from PU's pathogenic factor Helicobacter pylori (Hp) infection, diagnosis and differential diagnosis, to its management and complications (bleeding, obstruction, perforation and canceration). Finally, the challenges and prospects of AI application in PU are prospected and expounded. With the in-depth understanding of modern medical technology, AI remains a promising option in the management of PU patients and plays a more indispensable role. How to realize the robustness, versatility and diversity of multifunctional AI systems in PU and conduct multicenter prospective clinical research as soon as possible are the top priorities in the future.