Unleashing the Power of AI in healthcare: Comprehensive colorectal cancer detection

ARTIFICIAL INTELLIGENCE IN GASTROENTEROLOGY

Artificial intelligence (AI) has emerged as a transformative force across industries, and its applications in gastroenterology are rapidly evolving. AI, particularly machine learning and deep learning, is gaining prominence in diagnosing dysplasia in Barrett's esophagus and detecting gastric cancers (GCs) during upper gastrointestinal endoscopy. This innovation has the potential to prevent esophageal and stomach malignancies by providing automatic and accurate cancer detection. In colonoscopy, AI can automatically detect colorectal polyps, addressing variations in detection rates among endoscopists and allowing for the assessment of the feasibility of endoscopic polypectomy. 

MACHINE LEARNING AND DEEP LEARNING IN ENDOSCOPY

AI is rooted in elements that mimic human cognitive functions, such as learning and problem-solving. In endoscopy, machine learning (ML) and deep learning (DL) are subfields of AI that support decision-making and enhance colon polyp and adenoma detection rates. ML algorithms, including artificial neural networks (ANNs) and complex neural networks (CNNs), mirror brain functions and are designed to distinguish between normal and abnormal regions in the intestinal lumen. These technologies utilize characteristics like size, shape, and mucosal patterns to detect polyps. 

AI FOR COLORECTAL CANCER DIAGNOSIS

Colorectal cancer (CRC) is a major global health concern with the third most common malignancy in men and the second in women, and the fourth most common cause of cancer death. The National Polyp Study Registered that 70%-90% CRC can be prevented with routine endoscopic monitoring and polyp removal, but 7%-9% CRC can occur regardless of measures. 

 AI aids in improving adenoma detection rates (ADR) during colonoscopy, reducing the risk of missing cancers. Computer-aided diagnostic (CAD) systems, utilizing CNNs, demonstrate high accuracy in identifying and classifying polyps, enhancing ADR. Moreover, AI assists in distinguishing between adenomas and hyperplastic polyps, providing accurate predictions of histology. 

AI IN LESION ASSESSMENT AND PREDICTING FEASIBILITY OF EMR:

AI-assisted image classifiers assess the feasibility of mucosal resection of large colonic lesions, showcasing high accuracy. The technology's ability to recognize specific mucosal patterns and classify images suggests a potential future where AI predictions outperform those of specialized endoscopists. Additionally, AI-driven endoscopic computer-aided detection (EC-CAD) systems exhibit superior diagnostic capabilities for colorectal lesions compared to non-specialists. 

AI IN PREDICTING CLINICAL OUTCOMES

Deep learning algorithms demonstrate efficacy in predicting pathological responses after adjuvant chemotherapy for advanced rectal cancer, enabling personalized treatment plans. AI holds promise as a diagnostic tool for endoscopists and gastroenterologists, improving lesion detection, and predicting clinical outcomes in gastrointestinal endoscopy. However, current limitations include the need for high-quality datasets and the reliance on preclinical studies, emphasizing the evolving nature of AI in medical applications. 

CONCLUSION

AI's potential in gastroenterology is vast, revolutionizing technology and impacting ethical considerations. While challenges persist, collaborative efforts between AI and medical practitioners can harness achievements for mutual benefit. Routine check-ups and colorectal cancer screenings are pivotal for proactive health management, enhancing the treatment procedure with early cancer detection. 

Introducing the innovative AI for healthcare built by VinBrain, DrAid™ MRI Rectal Cancer D&T, served as an AI-powered platform that aids doctors and radiologists to identify rectal cancer early and precisely. This artificial intelligence in healthcare systems is designed to assist radiologists in automatically detecting abnormal lesions through MRI, provide clinical solutions to aid in the early identification of rectal cancer, as well as assist oncologists in developing treatment plans.  

 -----------------------------------

Learn more about DrAid™ MRI Rectal Cancer D&T now to see how the future of rectal cancer diagnostic works! 

Sources and References: 

Russell S, Norvig P. Artificial Intelligence: A Modern Approach, Global Edition. 3rd edition. London: Pearson, 2016. 

Colom R, Karama S, Jung RE, Haier RJ. Human intelligence and brain networks. Dialogs Clin Neurosci. 2010;12:489-501. 

Goodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge: The MIT Press, 2016.