Gut microbiota and artificial intelligence approaches: A scoping review

Ernesto Iadanza,Jasminka Hasic Telalovic,Rachele Fabbri,Amedeo Amedei,Džana Bašić-Čičak

doi:10.1007/s12553-020-00486-7

Abstract

This article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances.

Highlights

The idea for this work came from the recent authors’ efforts in transnational scientific networks and research projects on the microbiome
The keywords used for the search are consistent with the scope of the review and are the following: artificial intelligence, machine learning, deep learning, transfer learning, neural network/s, expert system/s, automatic classifier, deep network/s, classification, clustering, regression, prediction, microbiota, microbiome, gut, colorectal, colon, Chron
The paper [18] has been excluded as it is a review. It presents a repository of classification and regression tasks from human microbiome datasets publicly available and, as for the previously mentioned review, this paper has been read and some of the studies it reviews have been considered for the assessment

Summary

Introduction

The idea for this work came from the recent authors’ efforts in transnational scientific networks and research projects on the microbiome. In the last few years, the concept of applying computer-based algorithms for assessing medical problems has become a trending topic. The availability of large amounts of data, often referred to as big data, is a crucial enabling factor for this approach. This article strives to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The term microbiota refers to all microorganisms living in the same place, while microbiota habitat, the largest eukaryotic organism where the microbiota is located, is termed the host [1]. The site in which the largest amount of microorganisms resides is the gastro- digestive tract (mainly large intestine) [2]

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