Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis

Pancreatic cancer(PC)has a low incidence rate but a high mortality,with patients often in the advanced stage of the disease at the time of the first diagnosis.If detected,early neoplastic lesions are ideal for surgery,offering the best prognosis.Preneoplastic lesions of the pancreas include pancreatic intraepithelial neoplasia and mucinous cystic neoplasms,with intraductal papillary mucinous neoplasms being the most commonly diagnosed.Our study focused on predicting PC by identifying early signs using noninvasive techniques and artificial intelligence(AI).A systematic English literature search was conducted on the PubMed electronic database and other sources.We obtained a total of 97 studies on the subject of pancreatic neoplasms.The final number of articles included in our study was 44,34 of which focused on the use of AI algorithms in the early diagnosis and prediction of pancreatic lesions.AI algorithms can facilitate diagnosis by analyzing massive amounts of data in a short period of time.Correlations can be made through AI algorithms by expanding image and electronic medical records databases,which can later be used as part of a screening program for the general population.AI-based screening models should involve a combination of biomarkers and medical and imaging data from different sources.This requires large numbers of resources,collaboration between medical practitioners,and investment in medical infrastructures.

The role of computer-assisted systems for upper-endoscopy quality monitoring and assessment of gastric lesions

This article analyses the literature regarding the value of computer-assisted systems in esogastroduodenoscopy-quality monitoring and the assessment of gastric lesions.Current data show promising results in upper-endoscopy quality control and a satisfactory detection accuracy of gastric premalignant and malignant lesions,similar or even exceeding that of experienced endoscopists.Moreover,artificial systems enable the decision for the best treatment strategies in gastriccancer patient care,namely endoscopic vs surgical resection according to tumor depth.In so doing,unnecessary surgical interventions would be avoided whilst providing a better quality of life and prognosis for these patients.All these performance data have been revealed by numerous studies using different artificial intelligence(AI)algorithms in addition to white-light endoscopy or novel endoscopic techniques that are available in expert endoscopy centers.It is expected that ongoing clinical trials involving AI and the embedding of computer-assisted diagnosis systems into endoscopic devices will enable real-life implementation of AI endoscopic systems in the near future and at the same time will help to overcome the current limits of the computer-assisted systems leading to an improvement in performance.These benefits should lead to better diagnostic and treatment strategies for gastric-cancer patients.Furthermore,the incorporation of AI algorithms in endoscopic tools along with the development of large electronic databases containing endoscopic images might help in upper-endoscopy assistance and could be used for telemedicine purposes and second opinion for difficult cases.