Edge learning applications in the prediction and classification of combined hepatocellular-cholangiocarcinoma: A comprehensive narrative review.

Abstract

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare heterogeneous primary malignant liver tumor containing both hepatocellular and cholangiocarcinoma features. The complex presentation of cHCC-CCA tends to be poorly investigated, and the information derived from traditional diagnostic techniques (histopathology and radiological imaging) is often not optimal. Since cHCC-CCA is usually difficult to diagnose due to complex histopathological features (edge learning) as excessive photos, hence, achieves treatment delays and poor prognosis, the incorporation of advanced artificial intelligence like edge learning is able to improve the patient's outcome. Using artificial intelligence, particularly deep learning, has recently opened new doorways for the improvement of diagnostic accuracy. If artificial intelligence models are deployed on local devices, edge learning exercises this type of learning, which provides real time processing, improved data privacy and reduced bandwidth usage. This narrative review investigates the conceptual formulation of edge learning together with its opportunities for clinical applications in the prediction and classification of cHCC-CCA, the technical solution strategies, the clinical benefits it offers, and associated challenges and future directions.