Mastication Evaluation With Unsupervised Learning: Using an Inertial Sensor-Based System

A 3D printed electronic wearable device to generate vertical, horizontal and phono-articulatory jaw movement parameters: A concept implementation

PURPOSE

The current research aimed to develop a concept open-source 3D printable, electronic wearable head gear to record jaw movement parameters.

MATERIALS & METHODS

A 3D printed wearable device was designed and manufactured then fitted with open-source sensors to record vertical, horizontal and phono-articulatory jaw motions. Mean deviation and relative error were measured invitro. The device was implemented on two volunteers for the parameters of maximum anterior protrusion (MAP), maximum lateral excursion (MLE), normal (NMO), and maximum (MMO) mouth opening and fricative phono-articulation. Raw data was normalized using z-score and root mean squared error (RMSE) values were used to evaluate relative differences in readings across the two participants.

RESULTS

RMSE differences across the left and right piezoresistive sensors demonstrated near similar bilateral movements during normal (0.12) and maximal mouth (0.09) opening for participant 1, while varying greatly for participant 2 (0.25 and 0.14, respectively). There were larger differences in RMSE during accelerometric motion in different axes for MAP, MLE and Fricatives.

CONCLUSION

The current implementation demonstrated that a 3D printed electronic wearable device with open-source sensor technology can record horizontal, vertical, and phono-articulatory maxillomandibular movements in two participants. However, future efforts must be made to overcome the limitations documented within the current experiment.

Clinical machine learning in parafunctional and altered functional occlusion: A systematic review

STATEMENT OF PROBLEM

The advent of machine learning in the complex subject of occlusal rehabilitation warrants a thorough investigation into the techniques applied for successful clinical translation of computer automation. A systematic evaluation on the topic with subsequent discussion of the clinical variables involved is lacking.

PURPOSE

The purpose of this study was to systematically critique the digital methods and techniques used to deploy automated diagnostic tools in the clinical evaluation of altered functional and parafunctional occlusion.

MATERIAL AND METHODS

Articles were screened by 2 reviewers in mid-2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible articles were critically appraised by using the Joanna Briggs Institute's Diagnostic Test Accuracy (JBI-DTA) protocol and Minimum Information for Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist.

RESULTS

Sixteen articles were extracted. Variations in mandibular anatomic landmarks obtained via radiographs and photographs produced notable errors in prediction accuracy. While half of the studies adhered to robust methods of computer science, the lack of blinding to a reference standard and convenient exclusion of data in favor of accurate machine learning suggested that conventional diagnostic test methods were ineffective in regulating machine learning research in clinical occlusion. As preestablished baselines or criterion standards were lacking for model evaluation, a heavy reliance was placed on the validation provided by clinicians, often dental specialists, which was prone to subjective biases and largely governed by professional experience.

CONCLUSIONS

Based on the findings and because of the numerous clinical variables and inconsistencies, the current literature on dental machine learning presented nondefinitive but promising results in diagnosing functional and parafunctional occlusal parameters.

Variables influencing the device-dependent approaches in digitally analysing jaw movement-a systematic review

BACKGROUND

To explore the digitisation of jaw movement trajectories through devices and discuss the physiological factors and device-dependent variables with their subsequent effects on the jaw movement analyses.

METHODS

Based on predefined eligibility criteria, the search was conducted following PRISMA-P 2015 guidelines on MEDLINE, EBSCO Host, Scopus, PubMed, and Web of Science databases in 2022 by 2 reviewers. Articles then underwent Cochrane GRADE approach and JBI critical appraisal for certainty of evidence and bias evaluation.

RESULTS

Thirty articles were included following eligibility screening. Both in vitro experiments (20%) and in vivo (80%) devices ranging from electronic axiography, electromyography, optoelectronic and ultrasonic, oral or extra-oral tracking, photogrammetry, sirognathography, digital pressure sensors, electrognathography, and computerised medical-image tracing were documented. 53.53% of the studies were rated below "moderate" certainty of evidence. Critical appraisal showed 80% case-control investigations failed to address confounding variables while 90% of the included non-randomised experimental studies failed to establish control reference.

CONCLUSION

Mandibular and condylar growth, kinematic dysfunction of the neuromuscular system, shortened dental arches, previous orthodontic treatment, variations in habitual head posture, temporomandibular joint disorders, fricative phonetics, and to a limited extent parafunctional habits and unbalanced occlusal contact were identified confounding variables that shaped jaw movement trajectories but were not highly dependent on age, gender, or diet. Realistic variations in device accuracy were found between 50 and 330 µm across the digital systems with very low interrater reliability for motion tracing from photographs. Forensic and in vitro simulation devices could not accurately recreate variations in jaw motion and muscle contractions.

Adaptive predictive systems applied to gait analysis: A systematic review

BACKGROUND

Due to the high susceptivity of the walking pattern to be affected by several disorders, accurate analysis methods are necessary. Given the complexity and relevance of such assessment, the utilization of methods to facilitate it plays a significant role, provided that they do not compromise the outcomes.

RESEARCH QUESTIONS

This paper aimed at identifying the standards for the application of adaptive predictive systems to gait analysis, given the extensive research on this field. Furthermore, we also intended to check whether such methods can effectively support clinicians in determining the number of physiotherapy sessions necessary to recover gait-related dysfunctions.

METHODS

Through a screening process of scientific databases, we considered studies encompassed from 1968 to April 2019. Within these 50 years, we found 24 papers that met our inclusion criteria. They were analyzed according to their data acquisition and processing methods via ad hoc questionnaires. Additionally, we examined quantitatively the adaptive approaches.

RESULTS

Concerning data acquisition, the included papers presented a mean score of 6.1 SD 1.0, most of them applying optoelectronic systems, and the ground reaction force (GRF) was the most used parameter. The AI quality assessment showed an above-average rate of 7.8 SD 1.0, and artificial neural networks (ANN) being the paradigm most frequently utilized. Our systematic review identified only one study that addressed therapeutics including a predictive method.

SIGNIFICANCE

While much progress has been identified to predict assessment aspects, there is little effort to assist healthcare professionals in establishing the rehabilitation duration and prognostics. Therefore, future studies should focus on accomplishing the production of applications of predictive methods to therapeutics and prognosis, not lingering extremely on the analysis of gait features.

Validation of two hybrid approaches for clustering age-related groups based on gait kinematics data

Age-associated changes in walking parameters are relevant to recognize functional capacity and physical performance. However, the sensible nuances of slightly different gait patterns are hardly noticeable by inexperienced observers. Due to the complexity of this evaluation, we aimed at verifying the efficiency of applied hybrid-adaptive algorithms to cluster groups with similar gait patterns. Based on self-organizing maps (SOM), k-means clustering (KM), and fuzzy c-means (FCM), we compared the hybrid algorithms to a conventional FCM approach to cluster accordingly age-related groups. Additionally, we performed a relevance analysis to identify the principal gait characteristics. Our experiments, based on inertial-sensors data, comprised a sample of 180 healthy subjects, divided into age-related groups. The outcomes suggest that our methods outperformed the FCM algorithm, demonstrating a high accuracy (88%) and consistent sensitivity also to distinguish groups that presented a significant difference (p < .05) only in one of the six observed gait features. The applied algorithms showed a compatible performance, but the SOM + KM required less computation cost and, therefore, was more efficient. Furthermore, the results indicate the overall importance of cadence, as a measurement of physical performance, especially when clustering subjects by their age. Such output provides valuable information to healthcare professionals, concerning the subject's physical performance related to his age, supporting and guiding the physical evaluation.

Clustering of Self-Organizing Maps as a means to support gait kinematics analysis and symmetry evaluation

Gait analysis is relevant for the functional diagnostic of several musculoskeletal disorders. Walking patterns can be analyzed using techniques such as video processing and inertial measurement units (IMU). In this work, a Self-Organizing Maps (SOM) algorithm is applied to reduce the complexity of kinematic features obtained by IMU sensors of a sample of 40 individuals. Our system provides a simpler data representation (2-D graphic) than conventional methods, which often applies statistical analysis. We have tested the proposed method to analyze typical and simulated limping gait pattern under well-controlled conditions. Based on kinematic parameters and symmetry-related features, SOM algorithm was able to organize the sample in groups of subjects with three different gait patterns, normal and limping with each lower limb. Moreover, our system may be used to evaluate the recovery of a patient, offering intuitive information of his walking pattern in an assessment report. However, further research with atypical-gait subjects is necessary before applying such method as a clinical tool.