The invention discloses a complete denture
intelligent design method based on multi-task collaborative
deep learning, and is applied to the technical field of oral
rehabilitation. Comprising the following steps: generating an edentulous jaw model after
tissue surface buffering through an edentulous jaw buffering area
mask self-encoding network; generating a model after
undercut filling by adopting an in-place lane direction
undercut filling
algorithm; generating a complete denture arrangement position based on the multi-
rigid body posture prediction network; segmenting an
abutment boundary from the edentulous jaw model by using a
deep learning model; taking the base boundary and the tooth arrangement position of the complete denture as constraint conditions, constructing a composite
loss function in combination with the curvature of the generated surface, and generating a base model by using a conditional
generative adversarial network. According to the automatic core tooth arrangement step, errors caused by human factors are reduced, the consistency and reliability of denture design quality are remarkably improved, the wearing comfort and long-term stability of final
dentures are enhanced, and popularization and application of a high-quality complete
denture repair technology in primary medical institutions are facilitated.