A deep learning model for predicting chemical composition of gallstones with big data in medical Internet of Things

In this paper, we present a deep learning model for potentially diagnosing gallbladder stone with big data from medical Internet of Things. With the increasing trend of population aging and the change of people's living habits, gallbladder stone is increasingly common around the world. Specially, gallstones can be classified into four types, i.e., cholesterol stones, bile pigment stones, mixed stones and other rare stones, based on the chemical composition of gallstones. Furthermore, the chemical composition directly determines the treatment options. Currently, medical Internet of Things enables the collection of big medical data from massive ultrasonic images, computed tomography and magnetic resonance imaging of gallstones. However, it is a challenging issue to determine the exact chemical composition of gallstones from the collected big medical data. To tackle this issue, this paper presents a convolutional neural network to potentially learn the features of the collected data. Furthermore, we describe an effective learning approach for training the developed convolutional neural network. Finally, we analyze the characteristics of different types of gallstones, which can help improve our presented model to potentially determine the chemical composition of gallstones. The presented model can potentially obtain smart medical data from medical Internet of Things for assisted diagnose and treatment recommendation of gallbladder stones, aiming to build smart Internet of Things, especially smart health. (C) 2018 Elsevier B.V. All rights reserved.