Evolving role of artificial intelligence in gastrointestinal endoscopy

Artificial intelligence (AI) is a combination of different technologies that enable machines to sense, comprehend, and learn with human-like levels of intelligence. AI technology will eventually enhance human capability, provide machines genuine autonomy, and reduce errors, and increase productivity and efficiency. AI seems promising, and the field is full of invention, novel applications; however, the limitation of machine learning suggests a cautious optimism as the right strategy. AI is also becoming incorporated into medicine to improve patient care by speeding up processes and achieving greater accuracy for optimal patient care. AI using deep learning technology has been used to identify, differentiate catalog images in several medical fields including gastrointestinal endoscopy. The gastrointestinal endoscopy field involves endoscopic diagnoses and prognostication of various digestive diseases using image analysis with the help of various gastrointestinal endoscopic device systems. AI-based endoscopic systems can reliably detect and provide crucial information on gastrointestinal pathology based on their training and validation. These systems can make gastroenterology practice easier, faster, more reliable, and reduce inter-observer variability in the coming years. However, the thought that these systems will replace human decision making replace gastrointestinal endoscopists does not seem plausible in the near future. In this review, we discuss AI and associated various technological terminologies, evolving role in gastrointestinal endoscopy, and future possibilities.

Mathematical Modeling to Address Challenges in Pancreatic Cancer

Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer types for its challenges associated with early diagnosis and resistance to standard chemotherapeutic agents, thereby leading to a poor five-year survival rate. The complexity of the disease calls for a multidisciplinary approach to better manage the disease and improve the status quo in PDAC diagnosis, prognosis, and treatment. To this end, the application of quantitative tools can help improve the understanding of disease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategies to improve therapeutic outcomes. However, such approaches have only been minimally applied towards the investigation of PDAC, and we review the current status of mathematical modeling works in this field.

MicroRNA in Pancreatic Cancer: From Biology to Therapeutic Potential

Pancreatic cancer is one of the most aggressive malignancies, accounting for more than 45,750 deaths annually in the U.S. alone. The aggressive nature and late diagnosis of pancreatic cancer, coupled with the limitations of existing chemotherapy, present the pressing need for the development of novel therapeutic strategies. Recent reports have demonstrated a critical role of microRNAs (miRNAs) in the initiation, progression, and metastasis of cancer. Furthermore, aberrant expressions of miRNAs have often been associated with the cause and consequence of pancreatic cancer, emphasizing the possible use of miRNAs in the effective management of pancreatic cancer patients. In this review, we provide a brief overview of miRNA biogenesis and its role in fundamental cellular process and miRNA studies in pancreatic cancer patients and animal models. Subsequent sections narrate the role of miRNA in, (i) cell cycle and proliferation; (ii) apoptosis; (iii) invasions and metastasis; and (iv) various cellular signaling pathways. We also describe the role of miRNA's in pancreatic cancer; (i) diagnosis; (ii) prognosis and (iii) therapeutic intervention. Conclusion section describes the gist of review with future directions.