Automatic Segmentation of Mandibular Ramus and Condyles

Le, Celia; Deleat‐Besson, Romain; Prieto, Juan Carlos; Brosset, Serge; Dumont, Maxime; Zhang, Winston; Cevidanes, Lucia; Bianchi, Jonas; Ruellas, Antônio Carlos de Oliveira; Gomes, Liliane; Gurgel, Marcela; Massaro, Camila; Castillo, Aron Aliaga–Del; Yatabe, Marília; Benavides, Erika; Soki, Fabiana N.; Turkestani, Najla Al; Evangelista, Karine; Gonçalves, João Roberto; Valladares‐Neto, José; Silva, Maria Alves Garcia; Chaves, Cauby Maia; Costa, Fabio; Garib, Daniela Gamba; Oh, Heesoo; Gryak, Jonathan; Styner, Martin; Fillion‐Robin, Jean‐Christophe; Paniagua, Beatriz; Najarian, Kayvan; Soroushmehr, S. M. Reza

doi:10.1109/embc46164.2021.9630727

Cited by 8 publications

(9 citation statements)

References 17 publications

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“…These studies applied artificial intelligence, more specifically deep learning by neural networks, thereby eliminating the need of manual image segmentation and operator variability [ 36 , 37 ]. Artificial intelligence has been applied for automatic segmentation of the mandibular ramus and condyle [ 38 ]. The authors concluded that their findings suggest that CBCT image segmentation of the mandibular ramus and condyle from different clinical centers can be analyzed effectively [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence has been applied for automatic segmentation of the mandibular ramus and condyle [ 38 ]. The authors concluded that their findings suggest that CBCT image segmentation of the mandibular ramus and condyle from different clinical centers can be analyzed effectively [ 38 ]. Hence, in future studies the semi-automatic segmentation of the mandibular condyle applied in the present study may be substituted with a fully automatic segmentation using artificial intelligence.…”

Section: Discussionmentioning

confidence: 99%

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A Semi-Automatic Approach for Holistic 3D Assessment of Temporomandibular Joint Changes

Holte

Sæderup²,

Pinholt

2023

JPM

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The literature lacks a reliable holistic approach for the three-dimensional (3D) assessment of the temporomandibular joint (TMJ) including all three adaptive processes, which are believed to contribute to the position of the mandible: (1) adaptive condylar changes, (2) glenoid fossa changes, and (3) condylar positional changes within the fossa. Hence, the purpose of the present study was to propose and assess the reliability of a semi-automatic approach for a 3D assessment of the TMJ from cone-beam computed tomography (CBCT) following orthognathic surgery. The TMJs were 3D reconstructed from a pair of superimposed pre- and postoperative (two years) CBCT scans, and spatially divided into sub-regions. The changes in the TMJ were calculated and quantified by morphovolumetrical measurements. To evaluate the reliability, intra-class correlation coefficients (ICC) were calculated at a 95% confidence interval on the measurements of two observers. The approach was deemed reliable if the ICC was good (>0.60). Pre- and postoperative CBCT scans of ten subjects (nine female; one male; mean age 25.6 years) with class II malocclusion and maxillomandibular retrognathia, who underwent bimaxillary surgery, were assessed. The inter-observer reliability of the measurements on the sample of the twenty TMJs was good to excellent, ICC range (0.71–1.00). The range of the mean absolute difference of the repeated inter-observer condylar volumetric and distance measurements, glenoid fossa surface distance measurements, and change in minimum joint space distance measurements were (1.68% (1.58)–5.01% (3.85)), (0.09 mm (0.12)–0.25 mm (0.46)), (0.05 mm (0.05)–0.08 mm (0.06)) and (0.12 mm (0.09)–0.19 mm (0.18)), respectively. The proposed semi-automatic approach demonstrated good to excellent reliability for the holistic 3D assessment of the TMJ including all three adaptive processes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Semi-Automatic Approach for Holistic 3D Assessment of Temporomandibular Joint Changes

Holte

Sæderup²,

Pinholt

2023

JPM

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“…The very high variations in clinician-diagnosed parameters are known to often skew the prediction model 1,4 while isolation of condyles from radiographs and correlations to diagnostic biomarkers have been successfully established. 9,10 However, a systematic compilation of literature on the subject of prediction modelling in TMJ disorders is still lacking. 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 temporomandibular disorders.…”

Section: Rationale and Objectivementioning

confidence: 99%

“…Yet, the roles of radiomic data and clinicians in machine learning of TMJ disorders are mostly received with mixed reactions. The very high variations in clinician‐diagnosed parameters are known to often skew the prediction model 1,4 while isolation of condyles from radiographs and correlations to diagnostic biomarkers have been successfully established 9,10 . However, a systematic compilation of literature on the subject of prediction modelling in TMJ disorders is still lacking.…”

Section: Introductionmentioning

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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“…Deep learning has been widely used in CBCT images. 20 , 21 This technique is used in a variety of fields, including segmentation of the upper airway, 22 , 23 , 24 segmentation of the inferior alveolar nerve, 25 , 26 bone-related disease, 27 , 28 tooth segmentation and endodontics, 29 temporomandibular joint and sinus disease, 30 , 31 dental implant, 32 , 33 and landmark localisation. 34 , 35 Previous studies evaluated the caries detection performance of deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

Dental Caries Detection and Classification in CBCT Images Using Deep Learning

Esmaeilyfard,

Bonyadifard,

Paknahad

2024

International Dental Journal

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Automatic Segmentation of Mandibular Ramus and Condyles

Cited by 8 publications

References 17 publications

A Semi-Automatic Approach for Holistic 3D Assessment of Temporomandibular Joint Changes

A Semi-Automatic Approach for Holistic 3D Assessment of Temporomandibular Joint Changes

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

Dental Caries Detection and Classification in CBCT Images Using Deep Learning

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