Own Experience in the Use of Artificial Intelligence Technologies in the Diagnosis of Esophageal Achalasia

Abstract

Aim: to demonstrate an artificial intelligence model that optimises the differential diagnosis of achalasia.Material and methods. The study included 75 patients: 52 % men (mean age 44.5 ± 17.8 years) and 48 % women (mean age 45.6 ± 16.6 years,) with a preliminary diagnosis of achalasia. Patients were divided into four groups: type I, II, III achalasia and a group of patients whose results did not correspond to a diagnosis of achalasia according to HRM performed based on Chicago Classification version 4.0. On the basis of a set of data from 750 swallows and therefore 6750 manometric parameters, the artificial intelligence models DecisionTreeClassifier, RandomForestClassifier and CatBoostClassifier have been trained to provide a manometric diagnosis. The comparison criteria were the training time and the f1_score metric. The technical characteristics of the model (hyperparameters) were selected using the GridSearchCV method. The model with the best results was integrated into a web application.Results. The RandomForestClassifier was chosen as the best performing model to compare. Its technical characteristics were “decision trees” and branching depth the number of which was 14 and 5 respectively. With a maximum possible value of 1.0, these hyperparameters achieved f1_score=0.91 in 27 seconds. The web application, developed on the basis of this model, is capable of analyzing manometric data and establishing one of three types of achalasia in patients. Alternatively, it can exclude the diagnosis of achalasia. The output of an image corresponding to the diagnosis is produced for each manometric type of the disease.Conclusions. For the first time in Russia, a machine learning model based on high-resolution esophageal manometry data was developed at the V. Kh. Vasilenko Clinic of Internal Disease Propedeutics, Gastroenterology, and Hepatology of Sechenov University. The model has been applied to the creation of a web application which has the ability to substantiate the manometry diagnosis of patients. The Federal Service for Intellectual Property (Rospatent) issued a certificate of state registration of the computer program No. 2024665795 dated July 5, 2024. This artificial intelligence programme can be used in clinical practice as a medical decision support tool to optimize the process of differential diagnosis of achalasia and early detection of the disease, to determine the patient's prognosis and to select the method of further treatment.

Keywords

• machine learning
• artificial intelligence
• achalasia
• high-resolution esophageal manometry
• functional diagnostics