This invention discloses a multi-dimensional
quantitative assessment method and
system combining
meibomian gland morphology and function, belonging to the fields of ophthalmic diagnosis and
artificial intelligence technology. It acquires color images of the
eyelid margin,
infrared images of the meibomian glands, and dynamic videos of gland expulsion upon pressure, inputting these into a dedicated AI analysis engine for
processing. Employing an innovative anatomically guided
backbone network and a
meibomian gland region adaptive module, it achieves parallel automatic quantitative scoring across four dimensions:
meibomian gland opening
abnormality score,
secretion characteristics
score, expulsion capacity
score, and morphological
abnormality score based on gland segmentation
loss rate. The four scores are summed to obtain a comprehensive total score, used for the graded diagnosis of
meibomian gland dysfunction. By comparing changes in the patient's scores over time, it achieves macroscopic and microscopic evaluation of
treatment efficacy. By integrating functional and morphological indicators into a unified automated quantitative
system, it solves the problems of subjectivity and fragmentation in existing
assessment methods, significantly improving the objectivity, comprehensiveness, and accuracy of diagnosis, and providing a reliable tool for
individualized treatment and long-term management of MGD.