Our work on Jaw Movement

This study investigated the potential significant associations between specific aspects of patient histories, occlusal therapy, and self-reported or observed jaw clicking in a population from the state of South Australia.

Dental Loop Signals (DLS) offers a unique approach to biomedical signal-processing, employing deep learning to convert archived images of mandibular muscle activity during dynamic functions into signal data. DLS, processed through unsupervised learning, introduces a cluster-centric signal processing method, enhancing data normalisation for broad applicability. 

To Develop a workfow to extract normalised signal parameters from images of mandibular muscle EMG and identify optimal clustering methods for quantifying signal intensity and activity durations.

This study compared the accuracy of facial landmark measurements using deep learning-based fiducial marker (FM) and arbitrary width reference (AWR) approaches. It quantitatively analysed mandibular hard and soft tissue lateral excursions and head tilting from consumer camera footage of 37 participants.

Dental Loop SnP represents a pioneering python-based software application tailored to analyse phonetic speech patterns in patients and research. By extracting audio from video recordings that capture patients speaking, the software applies an AI-driven text-to-speech engine to create accurate reference speech samples. These samples are further processed through automated audio segmentation and subjected to statistical and spectral phonetic analysis techniques, resulting in the generation of diverse graphical data. The software's modular design allows for easy expansion by incorporating new phonemes and keywords, rendering it a highly adaptable and customisable tool in the realms of dentistry, speech therapy and craniofacial research.

Dental Loop FLT was developed to address the issue by incorporating advanced methodologies such as Dlib and FAN together into a useable interface for real-time object detection and landmark analysis. This promising approach provides a feasible means to evaluate and assess the intricate facial landmark measurements associated with soft tissue dynamics, thus enhancing the scope of both retrospective and real-time clinical research endeavours.

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

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

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.

This review aimed to systematically analyse the influence of clinical variables, diagnostic parameters and the overall image acquisition process on automation and deep learning in TMJ disorders.

To investigate the influence of endogenous and exogenous neuroendocrine analogues on the range and motion of jaw movement, mandibular growth, and factors affecting condylar guidance in patients with temporomandibular joint disorders using clinical assessment and radiographic imaging

The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain