Convolutional-neural-network-based diagnosis of appendicitis via CT scans in patients with acute abdominal pain presenting in the emergency department.

Scientific Reports
Jin Joo ParkJeongbae Rhie

Abstract

Acute appendicitis is one of the most common causes of abdominal emergencies. We investigated the feasibility of a neural-network-based diagnosis algorithm of appendicitis by using computed tomography (CT) for patients with acute abdominal pain visiting the emergency room (ER). A neural-network-based diagnostic algorithm of appendicitis was developed and validated using CT data from three institutions who visited the ER with abdominal pain and underwent abdominopelvic CT. For input data, 3D isotropic cubes including the appendix were manually extracted and labeled as appendicitis or a normal appendix. A 3D convolutional neural network (CNN) was trained to binary classification on the input. For model development and testing, 8-fold cross validation was conducted for internal validation and an ensemble model was used for external validation. Diagnostic performance was excellent in both the internal and external validation with an accuracy larger than 90%. The CNN-based diagnosis algorithm may be feasible in diagnosing acute appendicitis using the CT data of patients visiting the ER with acute abdominal pain.

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Citations

Jul 31, 2021·World Journal of Gastroenterology : WJG·Simon BourcierLee S Nguyen
Sep 28, 2021·World Journal of Emergency Surgery : WJES·Andrey LitvinFausto Catena
Nov 9, 2021·Journal of Laparoendoscopic & Advanced Surgical Techniques. Part a·Kentaro HayashiHiroshi Kawashima

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