Bioceramics, such as hydroxyapatite and β-tricalcium
phosphate,
are widely used in orthopedics and oral surgery because they are free
in shape and size and are not harvested from patients or donors. General
development of bioceramics requires a great deal of effort, a long
time, and many animal experiments. Because an animal experiment takes
several months and is currently regarded as an ethical problem, the
number of experiments should be reduced. In this study, machine learning
was applied to construct mathematical models to predict the material
properties, including the porosity, compressive strength, Ca2+ dissolution rate, and bone formation rate, from the synthesis conditions
and to design synthesis conditions of bioceramics with desired bone
formation rates. We propose two types of models: model 1 to predict
the material properties, crystallite sizes, and second selected Fourier
transform infrared wavenumbers of the resulting bioceramics from the
synthesis conditions, such as the starting powder conditions, and
model 2 to predict the bone formation rate from the material properties,
crystallite sizes, second selected Fourier transform infrared wavenumbers,
and animal experimental conditions of bioceramics. Both models were
constructed using Gaussian mixture regression, enabling direct inverse
analysis of the models. Furthermore, by visualization of the models,
the relationships among the bone formation rate, material properties,
and animal experimental conditions can be understood to establish
guidelines for designing the synthesis conditions. We succeeded in
designing artificial bone synthesis conditions with bone formation
rate exceeding existing bone formation rates.