Updated radiograph quality

Aruede, Glenda

doi:10.1038/s41415-021-2793-6

Cited by 1 publication

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regulating these patient parameters is completely beyond the control of investigators working with retrospective data and could have affected Kim et al's study 18 where the authors had to discard 778 images as they were deemed ‘ambiguous’ and still resulted with a model sensitivity below 60%. As most radiomic data is closely guarded by institutional ethics, it is not possible to verify if the images had originally satisfied the criteria for diagnostically acceptable radiographs 44 . Only a few investigators in the current review made their codes and datasets completely and freely available for use 22,25,26,33 .…”

Section: Discussionmentioning

confidence: 99%

“…As most radiomic data is closely guarded by institutional ethics, it is not possible to verify if the images had originally satisfied the criteria for diagnostically acceptable radiographs. 44 Only a few investigators in the current review made their codes and datasets completely and freely available for use. 22,25,26,33 As a general rule for primary data acquisition, panoramic radiographs should feature an occlusal plane curved slightly upwards, turbinates confined within the nose, hard palate shadow to cross maxillary sinuses above the apices of posterior teeth, hyoid bone and spine that do not superimpose, condyles that are of equal length, ramus and spine that are equidistant on both sides and a hyoid bone that is identical on both sides.…”

Section: Diagnostic Parameters Extracted From Radiomic Data and Adjun...mentioning

confidence: 99%

See 1 more Smart Citation

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

Farook

Dudley

2023

J of Oral Rehabilitation

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%