RESUMO
Objective:To establish an interpretive reporting system for urinalysis based on artificial intelligence (AI).Methods:Urine tests were collected from the First Affiliated Hospital, College of Medicine, Zhejiang University from 2008 to 2018, including 2 899 917 patient tests and 710 971 physical check-up tests. Then we set up a large population distribution with the frequency of different results of each item and established a health index of each sample and an abnormal level of each item according to data distribution, importance and degree of abnormality. We collected data of seven diseases, such as diabetes mellitus, urinary tract infection, glomerulonephritis and nephrotic syndrome, and matched them with a same number of healthy control group by gender and age. An integrated learner based on the AdaBoost algorithm was used to establish a diagnostic model and assess its algorithm performance. JAVA was used to develop data presentation software. The accuracy of the AI model for disease judgment was assessed by manual verification using 199 abnormal urine tests.Results:Each report could be graded as four levels: normal, abnormal, ill and critical. Each item could be judged as normal, mild, moderate, severe or extreme and the population distribution was provided with big data. The training accuracy, true positive rate and area under the curve were ≥88.3%, ≥80.0%, and ≥0.954 respectively using the machine learning model based on AdaBoost. The developed JAVA software presented the above results and displayed medical records and results, historical results, personalized advice, patient education and position in large population data. By manual verification, the accuracy rate of the AI model for disease judgment was 82.41% (166/199).Conclusion:This study established an intelligent interpretive reporting system for urine test results. It can distinguish the abnormality of each report, predict the disease of patients, and make personalized clinical decisions.