A Novel Deep Learning Approach for the Automatic Diagnosis of Acute Appendicitis

Abstract

Background: Acute appendicitis (AA) is a major cause of acute abdominal pain requiring surgical intervention. Approximately 20% of AA cases are diagnosed neither early nor accurately, leading to an increased risk of appendiceal perforation and postoperative sequelae. AA can be identified with good accuracy using computed tomography (CT). However, some studies have found that a false-negative AA diagnosis made using CT can cause surgical therapy to be delayed. Deep learning experiments are aimed at minimizing false-negative diagnoses. However, the success rates reported in these studies are far from 100%. In addition, the methods used to divide patients into groups do not adequately reflect situations in which accurate radiological diagnosis is difficult. Therefore, in this study, we propose a novel deep-learning approach for the automatic diagnosis of AA using CT based on establishing a new strategy for classification according to the difficulties encountered in radiological diagnosis. Methods: A total of 266 patients with a pathological diagnosis of AA who underwent appendectomy were divided into two groups based on CT images and radiology reports. A deep learning analysis was performed on the CT images and clinical and laboratory parameters that contributed to the diagnosis of both the patient and age- and sex-adjusted control groups. Results: The deep learning diagnosis success rate was 96% for the group with advanced radiological findings and 83.3% for the group with radiologically suspicious findings that could be considered normal. Conclusions: Using deep learning, successful results can be achieved in cases in which the appendix diameter has not increased significantly and there is no significant edema effect.