Qifei Dong

Although osteoporosis is a debilitating disease that affects 9% of individuals over 50 years of age in the US and 200 million women globally, osteoporosis screening is underutilized. A complementary approach to osteoporosis screening is opportunistic screening using pre-existing images to detect spinal osteoporotic compression fractures (OCFs). Spinal OCFs are often incidental findings and under-reported. An automated opportunistic screening tool can ensure earlier diagnosis and treatment of spinal OCFs and osteoporosis. A crucial component for the automated opportunistic screening tool is an OCF classifier that detects OCF on each vertebral body. In this research, we focus on building this OCF classifier. To do this, two spine radiograph datasets were obtained, whose radiographs are in the Digital Imaging and Communications in Medicine (DICOM) format. To annotate the data, we designed DicomAnnotator, a configurable open-source software program for efficient DICOM image annotation. With the annotated radiographs, we used five deep learning algorithms to build the OCF classifier. Training a deep learning model on a large dataset is often time-consuming. During deep learning model training, it is desirable to offer a non-trivial progress indicator that can continuously project the remaining model training time and the fraction of model training work completed. This makes the deep learning model training process more user-friendly. We designed the first set of techniques to support progress indication for deep learning model training that allows early stopping. In summary, we realized the following three aims in this research:
Aim 1: Design DicomAnnotator. Usability evaluation shows that DicomAnnotator is easy to learn, is efficient to use, and allows annotators to quickly make several types of annotations on a large set of DICOM images.
Aim 2: Build the OCF classifier. Model evaluation results show that our OCF classifier has some generalizability to clinical data and a suitable performance for our future opportunistic osteoporosis screening.
Aim 3: Design progress indication methods for deep learning model training. Our experiments show that our progress indicator can offer useful information even if the run-time system load varies over time and can self-correct its initial estimation errors, if any, over time.