Image-based Automated Measurement Model to Predict Pelvic Organ Prolapse
Automatically extracts pelvic floor measurement from MRIFaster and more consistent when compared to the manual process
Diagnosis of POP
USF inventors have developed a novel system for the automated localization, extraction, and analysis of MRI-based features with clinical information to improve the diagnosis of pelvic organ prolapse (POP). The system can automatically identify reference points for pelvic floor on MRI faster and with more consistency compared with the manuallyidentified point process by experts. It provides a prediction model that analyzes the correlation between current and new MRI-based feature with clinical information to differentiate patients with and without POP. The system can also accuratelyclassify different types of POP cases, particularly for posterior prolapses where the predication accuracy increases from 54% to 84% using the presented system. The developed system can also applied to the automated localization and extraction of MRIfeature for the diagnosis of other diseases where clinical examination is not adequate.
美國
