Generating Synthetic Computed Tomography (CT) Images to Improve the Performance of Machine Learning Model for Pediatric Abdominal Anomaly Detection
Abdominal pain is one of the most common symptoms for a wide range of conditions in children, under the age of 16 years. Due to the limited ability of X-ray to distinguish between structures in soft tissue, physicians often rely on Computed Tomography (CT) scan to diagnose the underlying cause of abdominal pain. A CT scan exposes the patient to 70-150 times the radiation used for an X-ray. Moreover, CT scanning equipment is often not available in low-resource countries, leading to improper diagnosis and treatment. Children are more susceptible to the harmful effects of radiation than adults and might have limited language skills, based on age, and hence limited ability to describe their symptoms to the physician. In this work, we show that it is possible to use a Machine Learning (ML) model, capable of generating synthetic CT scans, from orthogonal X-ray scans, to improve the automatic prediction of abdominal anomalies. In particular, we focus on the detection of structural anomalies such as malformed organs, cysts, and appendicitis. On average, we are able to improve the performance of the prediction model by 9.75%, with respect to the model trained on only X-ray and 4.55%, with respect to the model trained on only generated CT scan, by training it on both the generated CT scan and X-ray.