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

Farook, Taseef Hasan; Rashid, Farah; Ahmed, Saif; Dudley, James

doi:10.1016/j.prosdent.2023.01.013

Cited by 13 publications

(11 citation statements)

References 21 publications

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“…The supramentale attachment and menton rest in the current study was designed to mitigate most of the influence that the soft tissue might pose during the jaw motions. That said, soft tissue movement has always affected jaw movement analyzing devices [ 4 ] and can serve as a source of variation that can be corrected in the future with deep learning-based interventions [ 27 , 28 ]. Additionally, standard intraoral attachments are not ideal for fricative phono-articulation as the incisors need to make contact with the lower lip and produce a seal, which is impeded by the extraoral extension from the stabilization trays.…”

Section: Discussionmentioning

confidence: 99%

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

Farook,

Ahmed,

Talukder

et al. 2023

PLoS ONE

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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.

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Section: Discussionmentioning

confidence: 99%

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

Farook,

Ahmed,

Talukder

et al. 2023

PLoS ONE

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“…Searches included specific keywords ‘condylar function and dysfunction’, ‘temporomandibular disorders’, ‘degenerative disorders’, ‘trauma’, ‘articulation and transfer records’, ‘jaw movement, cephalometric and curvature parameters’, ‘muscular and electromyographic evaluation’ and ‘myositis, tendonitis and myalgia features’ as key diagnostic terms. Owing to the large heterogeneity present in the space of dental machine learning, no articles were excluded based on the type of diagnostic tests performed 8,14 . The large heterogeneity also unwarranted a statistical meta‐analysis for the current review.…”

Section: Methodsmentioning

confidence: 99%

“…Owing to the large heterogeneity present in the space of dental machine learning, no articles were excluded based on the type of diagnostic tests performed. 8,14 The large heterogeneity also unwarranted a statistical meta-analysis for the current review.…”

Section: Definition Of Data Extracted and Handling Of Variablesmentioning

confidence: 99%

Automation and deep (machine) learning in temporomandibular joint disorder radiomics: A systematic review

Farook

Dudley

2023

J of Oral Rehabilitation

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Objective 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. Methods Articles were screened in late 2022 according to a predefined eligibility criteria adhering to the PRISMA protocol. Eligible studies were extracted from databases hosted by MEDLINE, EBSCOHost, Scopus, PubMed and Web of Science. Critical appraisals were performed on individual studies following Nature Medicine's MI‐CLAIM checklist while a combined appraisal of the image acquisition procedures was conducted using Cochrane's GRADE approach. Results Twenty articles were included for full review following eligibility screening. The average experience possessed by the clinical operators within the eligible studies was 13.7 years. Bone volume, trabecular number and separation, and bone surface‐to‐volume ratio were clinical radiographic parameters while disc shape, signal intensity, fluid collection, joint space narrowing and arthritic changes were successful parameters used in MRI‐based deep machine learning. Entropy was correlated to sclerosis in CBCT and was the most stable radiomic parameter in MRI while contrast was the least stable across thermography and MRI. Adjunct serum and salivary biomarkers, or clinical questionnaires only marginally improved diagnostic outcomes through deep learning. Substantial data was classified as unusable and subsequently discarded owing to a combination of suboptimal image acquisition and data augmentation procedures. Inadequate identification of the participant characteristics and multiple studies utilising the same dataset and data acquisition procedures accounted for serious risks of bias. Conclusion Deep‐learned models diagnosed osteoarthritis as accurately as clinicians from 2D and 3D radiographs but, in comparison, performed poorly when detecting disc disorders from MRI datasets. Complexities in clinical classification criteria; non‐standardised diagnostic parameters; errors in image acquisition; cognitive, contextual or implicit biases were influential variables that generally affected analyses of inflammatory joint changes and disc disorders.

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“…Lateral jaw movements exhibit individual variability, and using the destabilised soft tissue of the chin as a reference point for jaw movement measurement has proven unreliable. This unreliability is particularly notable when habitual head tilting introduces an additional source of variation [2].…”

Section: Introductionmentioning

confidence: 99%

Facial and mandibular landmark tracking with habitual head posture estimation using linear and fiducial markers

Saad,

Farook,

Ahmed

et al. 2024

Healthcare Tech Letters

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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. A custom deep learning system recognised facial landmarks for measuring head tilt and mandibular lateral excursions. Circular fiducial markers (FM) and inter‐zygion measurements (AWR) were validated against physical measurements using electrognathography and electronic rulers. Results showed notable differences in lower and mid‐face estimations for both FM and AWR compared to physical measurements. The study also demonstrated the comparability of both approaches in assessing lateral movement, though fiducial markers exhibited variability in mid‐face and lower face parameter assessments. Regardless of the technique applied, hard tissue movement was typically seen to be 30% less than soft tissue among the participants. Additionally, a significant number of participants consistently displayed a 5 to 10° head tilt.

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

Cited by 13 publications

References 21 publications

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

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

Automation and deep (machine) learning in temporomandibular joint disorder radiomics: A systematic review

Facial and mandibular landmark tracking with habitual head posture estimation using linear and fiducial markers

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