The NLP was developed to automatically calculate a radiologist's performance for a specific diagnosis using unstructured reports in a RIS. For this initial test, the researchers selected the diagnoses of osteoporosis and osteopenia. The NLP was trained using 300 dual-energy x-ray absorptiometry (DEXA) and pelvic x-ray reports so that it would identify reports assessing normal and abnormal bony mineralization.
Session presenter Dr. Bao Do, a clinical instructor and radiologist working in the musculoskeletal section of the department of radiology, will present findings from an analysis of 1,910 pelvic x-ray reports and 1,496 DEXA reports. The NLP correctly classified all but one of the reports.
"The NLP was able to review several hundred thousand reports in less than a day's time, with a more than 99.9% accuracy rate," Do told AuntMinnie.com. "In an analysis we conducted over a five-year period, we determined that a pelvic x-ray exam has a high positive predictive value [PPV] of over 80% in identifying osteoporosis. Imagine the scenario of a patient who may not have health insurance or the funds to have a DEXA test who presents to an emergency department for hip pain after a fall. If an x-ray has a high PPV for identifying osteoporosis, the treating physician might be able to refer this patient to appropriate follow-up."
"So, our question is, without increasing the cost of healthcare, can an existing study such as a routine, inexpensive x-ray exam be useful as a complimentary screening tool to help identify more patients at risk for osteoporotic complications?" he said.
