Artificial intelligence in pancreatic cancer: applications in early detection, tumor staging, and survival prediction-a comprehensive review.

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Abstract

In the rapidly developing world, artificial intelligence (AI) is one of the emerging applications in the medical domain. Early detection of cancer is one of the most difficult processes, especially when it comes to the differentiation of cancer's structure, length, and size. AI methodology offers innovations and decision-making applicable to specific processes like data collecting, management, results, and conclusions. Due to the lack of particular indicators, the difficult location of the pancreas, and the absence of early symptoms, pancreatic cancer (PC) is difficult to identify with low and late analysis. However, the imaging approach is slightly improving analysis, but there is still potential for enhancement in systematizing guidelines. This comprehensive review will mainly focus on various applications of AI in pancreatic cancer diagnosis. Furthermore, this review presents various architectures based on machine learning (ML) and deep learning (DL) methods for applications such as early detection, classification, tumor staging, and pancreatic cancer survival prediction. In order to better comprehend challenging cases, clinical practitioners can benefit from the supplementary information and useful recommendations provided by various techniques. Finally, this review potentially analyzes the advantages and drawbacks present in pancreatic cancer. This review provides an overview of research based on AI methods and algorithms that provide superior performance from a variety of pancreatic cancer patients while also providing viable future perspectives with significant advancements to overcome drawbacks in previous research and provide enhanced performance, stating their effectiveness and robustness analysis.

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