The application discloses a multi-source
bone density data fusion method,
system and storage medium, and belongs to the field of medical
big data and intelligent diagnosis. The method aims at the
systematic deviation between DXA devices of different manufacturers and the high detection uncertainty problem under the primary medical environment. Firstly, the multi-source
bone density data and the corresponding physical calibration parameters are projected to a unified measurement space through standardized preprocessing and device-related mapping functions, and the device barriers are eliminated by using distribution alignment constraints. Then,
Gaussian uncertainty modeling is performed on the projected data to quantify the influence of specific clinical factors on
measurement reliability. Based on the uncertainty propagation mechanism, the multi-source discrete evidence is adaptively compensated and fused by weight, and multi-dimensional features including
physiology,
medical history and anatomical topology are introduced for joint modeling. Finally, end-to-end optimization is performed through a joint function of fusion uncertainty constraint and task loss, and high-robustness
osteoporosis risk intelligent evaluation is realized. The application significantly improves the consistency of cross-device T value prediction and the accuracy of
missed diagnosis / misdiagnosis review, and is suitable for multi-center
bone density mutual recognition and primary medical
bone health intelligent evaluation scene.