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Diagnosis is typically done by bone density measurement using Dual energy x-ray absorptiometry (DEXA) examination. DEXA is currently considered to be the gold standard for the diagnosis of osteoporosis. Even with 50 million scans per year, a large portion of metastases and fractures remain unnoticed by radiologists. This can lead to increased treatment cost and morbidity downstream.
The inventors developed an AI algorithm using random forest classifiers to assist radiologists in measuring bone mineral density. The algorithm can be implemented on current scanners. Experimental data shows improvement in sensitivity (77.8%), specificity (100%), and accuracy (98%) of lumbar bony metastasis detection, and improvement in sensitivity (61%), specificity (91%), and accuracy (85%) of lumbar fracture detection, which can reduce treatment cost and offer significant benefits to patients.