Is artificial intelligence an intelligent choice for gastroenterologists?

Introduction to artificial neural networks

The coupling of computer science and theoretical bases such as nonlinear dynamics and chaos theory allows the creation of 'intelligent' agents, such as artificial neural networks (ANNs), able to adapt themselves dynamically to problems of high complexity. ANNs are able to reproduce the dynamic interaction of multiple factors simultaneously, allowing the study of complexity; they can also draw conclusions on individual basis and not as average trends. These tools can offer specific advantages with respect to classical statistical techniques. This article is designed to acquaint gastroenterologists with concepts and paradigms related to ANNs. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data and from complex, dynamic, and multidimensional phenomena, which are often poorly predictable in the traditional 'cause and effect' philosophy.

International experience on the use of artificial neural networks in gastroenterology

In this paper, we reconsider the scientific background for the use of artificial intelligence tools in medicine. A review of some recent significant papers shows that artificial neural networks, the more advanced and effective artificial intelligence technique, can improve the classification accuracy and survival prediction of a number of gastrointestinal diseases. We discuss the 'added value' the use of artificial neural networks-based tools can bring in the field of gastroenterology, both at research and clinical application level, when compared with traditional statistical or clinical-pathological methods.

Artificial neural networks are able to recognize gastro-oesophageal reflux disease patients solely on the basis of clinical data

BACKGROUND

Artificial neural networks (ANN) are modelling mechanisms that are highly flexible and adaptive to solve the non-linearity inherent in the relationship between symptoms and underlying pathology.

OBJECTIVES

To assess the efficacy of ANN in achieving a diagnosis of gastro-oesophageal reflux disease (GORD) using oesophagoscopy or pH-metry as a diagnostic gold standard and discriminant analysis as a statistical comparator technique in a group of patients with typical GORD symptoms and with or without GORD objective findings (e.g. a positive oesophagoscopy or a pathological oesophageal pH-metry).

METHODS

The sample of 159 cases (88 men, 71 women) presenting with typical symptoms of GORD, were subdivided on the basis of endoscopy and pH-metry results into two groups: GORD patients with or without oesophagitis, group 1 (N=103), and pH and endoscopy-negative patients in whom both examinations were negative, group 2 (N=56). A total of 101 different independent variables were collected: demographic information, medical history, generic health state and lifestyle, intensity and frequency of typical and atypical symptoms based on the Italian version of the Gastroesophageal Reflux Questionnaire (Mayo Clinic). The diagnosis was used as a dependent variable. Different ANN models were assessed.

RESULTS

Specific evolutionary algorithms selected 45 independent variables, concerning clinical and demographic features, as predictors of the diagnosis. The highest predictive performance was achieved by a 'back propagation' ANN, which was consistently 100% accurate in identifying the correct diagnosis compared with 78% obtained by traditional discriminant analysis.

CONCLUSION

On the basis of this preliminary work, the use of ANN seems to be a promising approach for predicting diagnosis without the need for invasive diagnostic methods in patients suffering from GORD symptoms.