Advanced Imaging and Sampling in Barrett's Esophagus: Artificial Intelligence to the Rescue?

Care Delivery Interventions for Individuals with Cancer: A Literature Review and Focus on Gastrointestinal Malignancies

BACKGROUND

Gastrointestinal malignancies represent a particularly challenging condition, often requiring a multidisciplinary approach to management in order to meet the unique needs of these individuals and their caregivers.

PURPOSE

In this literature review, we sought to describe care delivery interventions that strive to improve the quality of life and care for patients with a focus on gastrointestinal malignancies.

CONCLUSION

We highlight patient-centered care delivery interventions, including patient-reported outcomes, hospital-at-home interventions, and other models of care for individuals with cancer. By demonstrating the relevance and utility of these different care models for patients with gastrointestinal malignancies, we hope to highlight the importance of developing and testing new interventions to address the unique needs of this population.

Artificial intelligence and early esophageal cancer

The development of esophageal cancer (EC) from early to advanced stage results in a high mortality rate and poor prognosis. Advanced EC not only poses a serious threat to the life and health of patients but also places a heavy economic burden on their families and society. Endoscopy is of great value for the diagnosis of EC, especially in the screening of Barrett’s esophagus and early EC. However, at present, endoscopy has a low diagnostic rate for early tumors. In recent years, artificial intelligence (AI) has made remarkable progress in the diagnosis of digestive system tumors, providing a new model for clinicians to diagnose and treat these tumors. In this review, we aim to provide a comprehensive overview of how AI can help doctors diagnose early EC and precancerous lesions and make clinical decisions based on the predicted results. We analyze and summarize the recent research on AI and early EC. We find that based on deep learning (DL) and convolutional neural network methods, the current computer-aided diagnosis system has gradually developed from in vitro image analysis to real-time detection and diagnosis. Based on powerful computing and DL capabilities, the diagnostic accuracy of AI is close to or better than that of endoscopy specialists. We also analyze the shortcomings in the current AI research and corresponding improvement strategies. We believe that the application of AI-assisted endoscopy in the diagnosis of early EC and precancerous lesions will become possible after the further advancement of AI-related research.

Barrett esophagus: What to expect from Artificial Intelligence?

The evaluation and assessment of Barrett's esophagus is challenging for both expert and nonexpert endoscopists. However, the early diagnosis of cancer in Barrett's esophagus is crucial for its prognosis, and could save costs. Pre-clinical and clinical studies on the application of Artificial Intelligence (AI) in Barrett's esophagus have shown promising results. In this review, we focus on the current challenges and future perspectives of implementing AI systems in the management of patients with Barrett's esophagus.