Use of Artificial Intelligence in Pulmonary Embolism Prediction

Aim:The purpose of this study was to use artificial intelligence to predict the risk of pulmonary embolism (PE) in patients with suspected PE admitted to the emergency room based on physical examination, laboratory, and clinical probability prediction scores without computed tomography angiography. Materials and Methods:A comprehensive analysis was conducted on a total of 156 individuals who were admitted to the emergency room with PE.Seventy-eight patients were diagnosed with PE through anamnesis, physical examination, clinical likelihood prediction scores, investigations, and imaging.These patients were then included in the PE group.The data set includes gender, age, shock index, vital signs, complaints at arrival to the emergency department, comorbidities, medications used, medical history, radiological examinations, presence of deep vein thrombosis, electrocardiography, echocardiography findings, Wells score, Geneva score, PERC score, and laboratory tests performed. Results:The average age of the patients in the study was 69.4615 years.Dyspnea was the most prevalent presentation, affecting 88 patients (56.4%).The most prevalent comorbidities were hypertension in 52 patients (33.1%), cancer in 51 patients (32.7%), and coronary artery disease in 35 patients (22.4%).The Wells score, D-dimer, low partial carbon dioxide pressure, and tachycardia were discovered to be important factors in the diagnosis of PE.Statistically significant parameters were investigated using a multilayer perceptron artificial intelligence model.The diagnosis of PE was correct with 96% accuracy and 89% specificity. Conclusion:According to the findings of our study, a thorough review of the patient's anamnesis, physical examination, laboratory and imaging data, and the application of scores are all crucial in the diagnosis of PE.Furthermore, it was determined that artificial intelligence can be used to diagnose PE before using imaging modalities.