Anomaly detection of retention loss in fixed partial dentures using resonance frequency analysis and machine learning: An in vitro study.

PURPOSE: This study aimed to determine the usefulness of machine learning techniques, specifically supervised and unsupervised learning, for assessing the cementation condition between a fixed partial denture (FPD) and its abutment using a resonance frequency analysis (RFA) system. METHODS: An in vitro mandibular model was used with a single crown and three-unit bridge made of a high-gold alloy. Two cementation conditions for the single crown and its abutment were set: cemented and uncemented. Four cementation conditions were set for the bridge and abutments: both crowns were firmly cemented, only the premolar crown was cemented, only the molar crown was cemented, and both crowns were uncemented. For RFA under cementation conditions, 16 impulsive forces were directly applied to the buccal side of the tested tooth at a frequency of 4 Hz using a Periotest device. Frequency responses were measured using a 3D accelerometer mounted on the occlusal surface of the tested tooth. Both supervised and unsupervised learning methods were used to analyze the datasets. RESULTS: Using supervised learning, the fully cemented condition had the highest feature importance scores at approximately 3000 Hz; the partially cemented condition had the highest scores between 1000 and 2000 Hz; and the highest scores for the uncemented condition were observed between 0 and 500 Hz. Using unsupervised learning, the uncemented and partially cemented conditions exhibited the highest anomaly scores. CONCLUSIONS: Machine learning combined with RFA exhibits good potential to assess the cementation condition of an FPD and hence facilitate the early diagnosis of FPD retention loss.

Correspondence between phase oscillator network and classical XY model with the same random and frustrated interactions.

We study correspondence between a phase oscillator network with distributed natural frequencies and a classical XY model at finite temperatures with the same random and frustrated interactions used in the Sherrington-Kirkpatrick model. We perform numerical calculations of the spin glass order parameter q and the distributions of the local fields P(R), where R is the amplitude of the local field. As a result, we find that the parameter dependences of P(R) in both models agree fairly well in all ranges of parameters in the spin glass phase and those of q agree at least for lower values of parameters in the spin glass phase, if parameters are normalized by using the previously obtained correspondence relation between two models with the same other types of interactions. Furthermore, we numerically calculate the time evolution of quantities such as the instantaneous local field in the phase oscillator network in order to study the roles of synchronous and asynchronous oscillators. We also study the self-consistent equation of the local fields in the oscillator network and XY model derived by the mean-field approximation.

Analysis of a solvable model of a phase oscillator network on a circle with infinite-range Mexican-hat-type interaction.

We study a phase oscillator network on a circle with an infinite-range interaction. First, we treat the Mexican-hat interaction with the zeroth and first Fourier components. We give detailed derivations of the auxiliary equations for the phases and self-consistent equations for the amplitudes. We solve these equations and characterize the nontrivial solutions in terms of order parameters and the rotation number. Furthermore, we derive the boundaries of the bistable regions and study the bifurcation structures in detail. Expressions for location-dependent resultant frequencies and entrained phases are also derived. Secondly, we treat a different interaction that is composed of mth and nth Fourier components, where m

Coexistence of memory patterns and mixed states in a sparsely encoded associative memory model storing ultrametric patterns.

Analyzing the coexistence of memory patterns and mixed states gives important information for constructing a model for the face responsive neurons of the monkey inferior-temporal cortex. We analyzed whether the memory patterns coexist with mixed states when the sparse coding scheme is used for the associative memory model storing ultrametric patterns. For memory patterns and mixed states to coexist, there must be sufficient capacity for storing them and their threshold values must be the same. We determined that the storage capacities for all mixed states composed of correlated memory patterns diverge as 1/| f log f| (where f is the firing rate) even when the correlation of the memory patterns is infinitesimally small. We also determined that the memory patterns and the mixed states can become the equilibrium state of the model in the same threshold value. These results mean that they can coexist in this model. These findings should contribute to research on face responsive neurons in the monkey inferior-temporal cortex.

Mixed states on neural network with structural learning.

We investigated the properties of mixed states in a sparsely encoded associative memory model with a structural learning method. When mixed states are made of s memory patterns, s types of mixed states, which become equilibrium states of the model, can be generated. To investigate the properties of s types of the mixed states, we analyzed them using the statistical mechanical method. We found that the storage capacity of the memory pattern and the storage capacity of only a particular mixed state diverge at the sparse limit. We also found that the threshold value needed to recall the memory pattern is nearly equal to the threshold value needed to recall the particular mixed state. This means that the memory pattern and the particular mixed state can be made to easily coexist at the sparse limit. The properties of the model obtained by the analysis are also useful for constructing a transform-invariant recognition model.

Synapse efficiency diverges due to synaptic pruning following overgrowth.

In the development of the brain, it is known that synapses are pruned following overgrowth. This pruning following overgrowth seems to be a universal phenomenon that occurs in almost all areas-visual cortex, motor area, association area, and so on. It has been shown numerically that the synapse efficiency is increased by systematic deletion. We discuss the synapse efficiency to evaluate the effect of pruning following overgrowth, and analytically show that the synapse efficiency diverges as O(|ln c|) at the limit where connecting rate c is extremely small. Under a fixed synapse number criterion, the optimal connecting rate, which maximizes memory performance, exists.