Endoscopic resection is indicated for early or superficial gastrointestinal neoplasms with a negligible risk of lymph node metastasis. This procedure could preserve the organ while allowing en bloc resection of tumors, irrespective of the size and location of the lesion. Histological evaluation of the resected specimen determines whether curative resection, which implies a favorable long-term outcome, was achieved. If the resected specimen reveals non-curative, additional treatment is necessary as it is strongly associated with recurrence.Current Concepts: Surgical resection is recommended after non-curative resection of gastrointestinal neoplasms. However, rather than surgical resection, additional endoscopic treatment can be recommended if non-curative resection is solely because of the positive involvement at the horizontal resection margin without any other findings compatible with the non-curative resection criteria.Discussion and Conclusion: Adopting precise indications of endoscopic resection is important to reduce the risk of non-curative resection. If curative resection is not achieved after endoscopic resection, additional treatment should be considered to prevent local recurrence as well as lymph node metastasis.

In the past, conventional machine learning was applied to analyze tabulated medical data while deep learning was applied to study afflictions such as gastrointestinal disorders. Neural networks were used to detect, classify, and delineate various images of lesions because the local feature selection and optimization of the deep learning model enabled accurate image analysis. With the accumulation of medical records, the evolution of computational power and graphics processing units, and the widespread use of open-source libraries in large-scale machine learning processes, medical artificial intelligence (AI) is overcoming its limitations. While early studies prioritized the automatic diagnosis of cancer or pre-cancerous lesions, the current expanded scope of AI includes benign lesions, quality control, and machine learning analysis of big data. However, the limited commercialization of medical AI and the need to justify its application in each field of research are restricting factors. Modeling assumes that observations follow certain statistical rules, and external validation checks whether assumption is correct or generalizable. Therefore, unused data are essential in the training or internal testing process to validate the performance of the established AI models. This article summarizes the studies on the application of AI models in upper gastrointestinal disorders. The current limitations and the perspectives on future development have also been discussed.

Obesity is associated with various comorbidities, such as type II diabetes, hypertension, dyslipidemia, and cardiovascular disease. Gastrointestinal complications are also frequent and obesity is a direct cause of nonalcoholic fatty liver disease, and are risk factors for gastroesophageal reflux disease, pancreatitis, gallstone disease, diarrhea, dyssynergic defection, and various gastrointestinal cancers. Diagnosis is usually made by measuring body mass index (BMI). Although BMI is correlated with body fat mass, it may overestimate subjects with high muscle mass and underestimate subjects with low muscle mass. Co-measurement of waist circumference as a reflection of abdominal obesity for subjects with BMIs ranging from 25 to 35 kg/m2 has been recommended; however, it is still an anthropometric diagnosis that does not clearly discriminate subjects at risk for developing comorbidities. Biomarkers reflect the underlying biological mechanisms of obesity and can be used to characterize the obesity phenotype (i.e., at high risk for disease development) as well as a target for disease-causing factors. In this article, we describe the conventional diagnosis, biomarkers of obesity, and current challenges.
Adipose Tissue ; Biomarkers ; Body Mass Index ; Cardiovascular Diseases ; Comorbidity ; Diagnosis ; Diarrhea ; Dyslipidemias ; Gallstones ; Gastroesophageal Reflux ; Gastrointestinal Diseases ; Gastrointestinal Neoplasms ; Hypertension ; Non-alcoholic Fatty Liver Disease ; Obesity ; Obesity, Abdominal ; Pancreatitis ; Phenotype ; Risk Factors ; Waist Circumference

Artificial intelligence using deep learning has been applied to gastrointestinal disorders for the detection, classification, and delineation of various lesion images. With the accumulation of enormous medical records, the evolution of computation power with graphic processing units, and the widespread use of open-source libraries in large-scale machine learning processes, medical artificial intelligence is overcoming its traditional limitations. This paper explains the basic concepts of deep learning model establishment and summarizes previous studies on upper gastrointestinal disorders. The limitations and perspectives on future development are also discussed.

Obesity is associated with various comorbidities, such as type II diabetes, hypertension, dyslipidemia, and cardiovascular disease. Gastrointestinal complications are also frequent and obesity is a direct cause of nonalcoholic fatty liver disease, and are risk factors for gastroesophageal reflux disease, pancreatitis, gallstone disease, diarrhea, dyssynergic defection, and various gastrointestinal cancers. Diagnosis is usually made by measuring body mass index (BMI). Although BMI is correlated with body fat mass, it may overestimate subjects with high muscle mass and underestimate subjects with low muscle mass. Co-measurement of waist circumference as a reflection of abdominal obesity for subjects with BMIs ranging from 25 to 35 kg/m2 has been recommended; however, it is still an anthropometric diagnosis that does not clearly discriminate subjects at risk for developing comorbidities. Biomarkers reflect the underlying biological mechanisms of obesity and can be used to characterize the obesity phenotype (i.e., at high risk for disease development) as well as a target for disease-causing factors. In this article, we describe the conventional diagnosis, biomarkers of obesity, and current challenges.