An early screening model of pulse detection technology for hepatic steatosis

Abstract

BackgroundThe increasing prevalence of hepatic steatosis presents a considerable challenge to public health. There is a critical need for the development of novel preventive and screening strategies for this condition. This study evaluated the potential applications of wrist pulse detection technology for the early detection of liver diseases. The pulse time-domain features of a medical exam population with and without hepatic steatosis were assessed to develop a screening model for this disease. MethodsParticipants were consecutively recruited from March 2021 to March 2022 in the medical examination centers of the Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and the Shanghai Municipal Hospital of Traditional Chinese Medicine. Clinical data from 255 participants, including general information (sex, age, and body mass index), and data related to glucose and blood lipids (fasting plasma glucose, triglyceride, total cholesterol, high-density lipoprotein, and low-density lipoprotein levels) were collected. Wrist pulse signals were acquired using a pulse detection device, and the pulse time-domain features, including t1, t4, t5, T, w1, w2, h2/h1, h3/h1, and h5/h1 were extracted. Participants were assigned to hepatic steatosis and non-hepatic steatosis groups according to their abdominal ultrasound examination results. Their clinical data and pulse time-domain features were compared using chi-square and parametric or non-parametric statistical methods. Three datasets were used to construct screening models for hepatic steatosis based on the random forest algorithm. The datasets for modeling were defined as Dataset 1, containing blood glucose and lipid data and general information; Dataset 2, containing time-domain features and general information; Dataset 3, containing time-domain features, blood glucose and lipid data, and general information. The evaluation metrics, accuracy, precision, recall, F1-score, and areas under the receiver operating characteristic curve (AUC) were compared for each model. ResultsThe time-domain features of the two groups differed significantly. The t1, t4, t5, T, h2/h1, h3/h1, w1, and w2 features were higher in the hepatic steatosis group than in the non-hepatic steatosis group (P < 0.05), while the h5/h1 features were lower in the hepatic steatosis group than in the non-hepatic steatosis group (P < 0.05). The screening models for hepatic steatosis based on both time-domain features and blood glucose and lipid data outperformed those based on time-domain features or blood markers alone. The accuracy, precision, recall, F1-score, and AUC of the combined model were 81.18%, 80.56%, 76.32%, 79%, and 87.79%, respectively. These proportions were 1.57%, 1.86%, 1.76%, 2%, and 3.54% higher, respectively, than those of the model based on time-domain features alone and 3.14%, 4.2%, 2.64%, 4%, and 6.47% higher, respectively, than those of the model based on blood glucose and lipid alone. ConclusionThe early screening model for hepatic steatosis using datasets that included pulse time-domain features achieved better performance. The findings suggest that pulse detection technology could be used to inform the development of a mobile medical device or remote home monitoring system to test for hepatitis steatosis.