Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications

Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review

Artificial intelligence (AI) is revolutionizing early cancer detection by enhancing the sensitivity, efficiency, and precision of screening programs for breast, colorectal, and lung cancers. Deep learning algorithms, such as convolutional neural networks, are pivotal in improving diagnostic accuracy by identifying patterns in imaging data that may elude human radiologists. AI has shown remarkable advancements in breast cancer detection, including risk stratification and treatment planning, with models achieving high specificity and precision in identifying invasive ductal carcinoma. In colorectal cancer screening, AI-powered systems significantly enhance polyp detection rates during colonoscopies, optimizing the adenoma detection rate and improving diagnostic workflows. Similarly, low-dose CT scans integrated with AI algorithms are transforming lung cancer screening by increasing the sensitivity and specificity of early-stage cancer detection, while aiding in accurate lesion segmentation and classification. This review highlights the potential of AI to streamline cancer diagnosis and treatment by analyzing vast datasets and reducing diagnostic variability. Despite these advancements, challenges such as data standardization, model generalization, and integration into clinical workflows remain. Addressing these issues through collaborative research, enhanced dataset diversity, and improved explainability of AI models will be critical for widespread adoption. The findings underscore AI's potential to significantly impact patient outcomes and reduce cancer-related mortality, emphasizing the need for further validation and optimization in diverse healthcare settings.

Use of artificial intelligence in the management of T1 colorectal cancer: a new tool in the arsenal or is deep learning out of its depth?

The field of artificial intelligence is rapidly evolving, and there has been an interest in its use to predict the risk of lymph node metastasis in T1 colorectal cancer. Accurately predicting lymph node invasion may result in fewer patients undergoing unnecessary surgeries; conversely, inadequate assessments will result in suboptimal oncological outcomes. This narrative review aims to summarize the current literature on deep learning for predicting the probability of lymph node metastasis in T1 colorectal cancer, highlighting areas of potential application and barriers that may limit its generalizability and clinical utility.

Precision endoscopy in the era of climate change and sustainability

Global warming caused by increased greenhouse gas (GHG) emissions has a direct impact on human health. Gastrointestinal (GI) endoscopy contributes significantly to GHG emissions due to energy consumption, reprocessing of endoscopes and accessories, production of equipment, safe disposal of biohazardous waste, and travel by patients. Moreover, GHGs are also generated in histopathology through tissue processing and the production of biopsy specimen bottles. The reduction in unnecessary surveillance endoscopies and biopsies is a practical approach to decrease GHG emissions without affecting disease outcomes. This narrative review explores the role of precision medicine in GI endoscopy, such as image-enhanced endoscopy and artificial intelligence, with a focus on decreasing unnecessary endoscopic procedures and biopsies in the surveillance and diagnosis of premalignant lesions in the esophagus, stomach, and colon. This review offers strategies to minimize unnecessary endoscopic procedures and biopsies, decrease GHG emissions, and maintain high-quality patient care, thereby contributing to sustainable healthcare practices.

Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review

OBJECTIVE

This study aims to summarize the research literature evaluating machine learning (ML)-based clinical decision support (CDS) systems in healthcare settings.

MATERIALS AND METHODS

We conducted a review in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta Analyses extension for Scoping Review). Four databases, including PubMed, Medline, Embase, and Scopus were searched for studies published from January 2016 to April 2021 evaluating the use of ML-based CDS in clinical settings. We extracted the study design, care setting, clinical task, CDS task, and ML method. The level of CDS autonomy was examined using a previously published 3-level classification based on the division of clinical tasks between the clinician and CDS; effects on decision-making, care delivery, and patient outcomes were summarized.

RESULTS

Thirty-two studies evaluating the use of ML-based CDS in clinical settings were identified. All were undertaken in developed countries and largely in secondary and tertiary care settings. The most common clinical tasks supported by ML-based CDS were image recognition and interpretation (n = 12) and risk assessment (n = 9). The majority of studies examined assistive CDS (n = 23) which required clinicians to confirm or approve CDS recommendations for risk assessment in sepsis and for interpreting cancerous lesions in colonoscopy. Effects on decision-making, care delivery, and patient outcomes were mixed.

CONCLUSION

ML-based CDS are being evaluated in many clinical areas. There remain many opportunities to apply and evaluate effects of ML-based CDS on decision-making, care delivery, and patient outcomes, particularly in resource-constrained settings.

The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists' perceptions thereof

The development of deep learning systems in artificial intelligence (AI) has enabled advances in endoscopy, and AI-aided colonoscopy has recently been ushered into clinical practice as a clinical decision-support tool. This has enabled real-time AI-aided detection of polyps with a higher sensitivity than the average endoscopist, and evidence to support its use has been promising thus far. This review article provides a summary of currently published data relating to AI-aided colonoscopy, discusses current clinical applications, and introduces ongoing research directions. We also explore endoscopists' perceptions and attitudes toward the use of this technology, and discuss factors influencing its uptake in clinical practice.

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.

Special Issue "Advances in Gastrointestinal and Liver Disease: From Physiological Mechanisms to Clinical Practice"

It is an exciting time for gastroenterology and hepatology [...].