Vol.:(0123456789) 1 3 https://doi.org/10.1007/s12553-020-00486-7 REVIEW PAPER Gut microbiota and artiﬁcial intelligence approaches: A scoping review Ernesto Iadanza 1 · Rachele Fabbri 1 · Džana Bašić‑ČiČak 2 · Amedeo Amedei 3 · Jasminka Hasic Telalovic 2 Received: 25 September 2020 / Accepted: 1 October 2020 © The Author(s) 2020 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. Keywords Microbiota · Microbiome · Artiﬁcial intelligence · Machine learning · Deep learning · Clinical decision support systems 1 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 apply- ing computer-based algorithms for assessing medical prob- lems 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 liv- ing in the same place, while microbiota habitat, the larg- est eukaryotic organism where the microbiota is located, is termed the host [1]. In animals, the site in which the largest amount of microorganisms resides is the gastro- digestive tract (mainly large intestine) [2]. A complex ecosystem consisting of bacteria, viruses, fungi and protozoans, is a human microbiota. It contains more than 100 times the human genome and gives us the functional properties we do not possess. It is composed by a number of genes (the microbiome). According to a recent estimation, the amount of bacteria contained in it could be higher than the amount of eukaryotic cells in the human body [3]: some 30 to 400 trillion microorganisms live in the gastrointestinal tract [4, 5]. Any surface exposed to the exter- nal environment, such as skin and mucosa (gastrointestinal, respiratory, and urogenital), is populated with the commen- sal microbiota, with the colon containing over 70% of all the bacteria in our body. An ecological connotation is thus assumed by the entire organism, which can now be redeﬁned as a network of interactions and connections between vari- ous organisms (both eukaryotes and prokaryotes) [6]. * Ernesto Iadanza ernesto.iadanza@uniﬁ.it 1 Department of Information Engineering, University of Florence, Via S. Marta 3, 50139 Florence, Italy 2 School of Science and Technology, University of Sarajevo, Sarajevo, Bosnia and Herzegovina 3 Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy Health and Technology (2020) 10:13 –1358 43 / Published online: 26 October 2020