Ram Krishna Panthi, Bishnu Khanal, Mukunda Prakash Kshetree, Bed Raj Acharya, Shashidhar Belbase
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threedimensional 3d facial symmetry analysis based 3d symmetry reference plane srp artificial intelligence widely used dental oral sciences study developed novel deep learning model called facial planar reflective symmetry net fprsnet automatically construct srp established method defining 3d pointcloud region interest roi highdimensional feature computations suitable network model overall 240 patients enrolled deep learning model trained predicted using 200 samples clinical suitability evaluated 40 samples four fprsnet models prepared using supervised unsupervised learning approaches based full facial roi data fprsnets fprsnetsr fprsnetu fprsnetur models trained 160 3d facial datasets validated 20 cases tested another 20 cases model predictions evaluated using additional 40 clinical 3d facial datasets comparing mean square error srp parameters predicted four fprsnet models truth plane clinical suitability fprsnet models evaluated measuring angle error predicted groundtruth planes experts evaluated predicted srp four fprsnet models using visual analogue scales vas method fprsnetsr fprsnetu models achieved average angle error 084 099 predicting 3d facial srp respectively vas value 8 using four fprsnet models create srp 40 cases 3d facial data required 4 study demonstrated new solution automatically constructing oral clinical 3d facial srps study proposes innovative deep learning algorithm fprsnet construct symmetry reference plane reduce workload shorten time required digital design reduce dependence expert experience improve therapeutic efficiency effectiveness dental clinics