Augmenting generalizability of automated methods with local fine-tuning can help providers diagnosis osteoporosis with these scans.
Identifying patients who have osteoporosis can be easier on abdominal CT scans with automated methods that have improved generalizability.
It is well known that measuring the Hounsfield unit (HU) in the L1 vertebral body (VB) is an opportunistic way to screen patients for osteoporosis. But, the HU thresholds may not generalize well between different groups based on population and imaging protocol differences.
To see whether it is possible to augment opportunistic osteoporosis screening, Steven Rothenberg, M.D., assistant professor of diagnostic radiology at the University of Maryland School of Medicine, compared two deep learning model approaches to an external institution. His team first applied previously established thresholds to automatically extracted L1 HU, and they also trained a locally fine-tuned model using automatically extracted HUs, bone mineral densities (BMD), and demographic variables.
Rothenberg presented the team’s results in a poster during the Society for Imaging Informatics in Medicine (SIIM) 2021 Virtual Annual Meeting.
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For the study, the team used a deep learning model that had been trained on images from one institution to automatically segment lumbar VBs on abdominal CTs and to extract trabecular HUs from each body in order to assess the external dataset from their local site.
They gathered 200 consecutive abdominal CTs from their hospital that had undergone DEXA examination within six months over a two-year timeframe. They, first, applied L1 VB HU cut-offs to diagnose potential osteoporosis, and, then, they used age and sex data alongside automatically-extracted L1 and L2 HU and BMD to train both decision tree and random forest models for osteoporosis diagnosis.
Based on their analysis, the locally fine-tuned random forest and decision tree models had average F1 scores of 0.84 and 0.8, respectively. These results outperformed the 0.54 and 0.46 scores achieved by L1 HU thresholds of 80 and 120, respectively. In addition, they team determined the average sensitivity for the fine-tuned models were greater than 80 percent – far outpacing the 40 percent and 57 percent achieved by the two HU thresholds.
Overall, the team said, their results indicate that local models that are fine-tuned on demographic variables and automatically-extracted VB HU and BMD data do result in improved generalization for opportunistic osteoporosis screening.
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