Web infrastructure for data management, storage and computation

Brosset, Serge; Dumont, Maxime; Cevidanes, Lucia; Soroushmehr, S. M. Reza; Bianchi, Jonas; Gurgel, Marcela; Deleat‐Besson, Romain; Le, Celia; Ruellas, Antônio Carlos de Oliveira; Yatabe, Marília; Rosas, Liliane; Gonçalves, João; Najarian, Kayvan; Gryak, Jonathan; Paniagua, Beatriz; Styner, Martin; Prieto, Juan Carlos

doi:10.1117/12.2582283

Cited by 6 publications

(11 citation statements)

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“…The image pre-processing included T1 CBCT orientation, 15 T2 registration, 29 as well as T1 and T2 IOS registration to the CBCT scans 30 with validated semi-automated tools (Figure 1) and completely automated tools (Figure 2). 8,[19][20][21][22][23][24]26,31,32 The T1 CBCTs orientation was performed positioning the mandibular 3D model as follows: The lower border of the mandible, mesial surface of first molars and midline were aligned, respectively, with the axial, coronal and sagittal axis. 15 Subsequently, the T2 CBCT images were manually approximated to the T1 images and a voxel-based registration was performed.…”

Section: Image Processingmentioning

confidence: 99%

“…The integration of these image modalities may optimize its diagnostic value, granting researchers and clinicians a comprehensive and enhanced analysis of the roots, root canals and crowns, 12,[17][18][19][20] also aiding in critical tooth position assessments. Additionally, the advent of innovative artificial intelligence (AI)-based tools [19][20][21][22][23][24][25][26] have facilitated merging root canals and crown segmentations, as well as automatic placement of landmarks and quantification of tooth movement.…”

Section: Introductionmentioning

confidence: 99%

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Automated artificial intelligence‐based three‐dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery

Gurgel,

Alvarez,

Aristizabal

et al. 2023

Orthod Craniofacial Res

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Objective(s)This study aims to evaluate the influence of the piezocision surgery in the orthodontic biomechanics, as well as in the magnitude and direction of tooth movement in the mandibular arch using novel artificial intelligence (AI)‐automated tools.Materials and MethodsNineteen patients, who had piezocision performed in the lower arch at the beginning of treatment with the goal of accelerating tooth movement, were compared to 19 patients who did not receive piezocision. Cone beam computed tomography (CBCT) and intraoral scans (IOS) were acquired before and after orthodontic treatment. AI‐automated dental tools were used to segment and locate landmarks in dental crowns from IOS and root canals from CBCT scans to quantify 3D tooth movement. Differences in mesial‐distal, buccolingual, intrusion and extrusion linear movements, as well as tooth long axis angulation and rotation were compared.ResultsThe treatment time for the control and experimental groups were 13.2 ± 5.06 and 13 ± 5.52 months respectively (P = .176). Overall, anterior and posterior tooth movement presented similar 3D linear and angular changes in the groups. The piezocision group demonstrated greater (P = .01) mesial long axis angulation of lower right first premolar (4.4 ± 6°) compared with control group (0.02 ± 4.9°), while the mesial rotation was significantly smaller (P = .008) in the experimental group (0.5 ± 7.8°) than in the control (8.5 ± 9.8°) considering the same tooth.ConclusionThe open source‐automated dental tools facilitated the clinicians' assessment of piezocision treatment outcomes. The piezocision surgery prior to the orthodontic treatment did not decrease the treatment time and did not influence in the orthodontic biomechanics, leading to similar tooth movements compared to conventional treatment.

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Section: Image Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated artificial intelligence‐based three‐dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery

Gurgel,

Alvarez,

Aristizabal

et al. 2023

Orthod Craniofacial Res

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“…The Learning Using Privileged Information (LUPI) implemented in the TMJPI tool uses biological data to train the machine learning model but classifies new patients based on clinical and imaging data only, which is the current standard of care. These tools are available in an open-source web system DSCI (Data Storage for Computation and Integration) used for data management with storage and integration of patient information from multiple sources (33).…”

Section: Diagnostic Performance Of the Markers In Machine Learning Al...mentioning

confidence: 99%

Quantitative bone imaging biomarkers and joint space analysis of the articular Fossa in temporomandibular joint osteoarthritis using artificial intelligence models

Tamara

Turkestani²,

Bianchi³

et al. 2022

Front. Dent. Med

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Temporomandibular joint osteoarthritis (TMJ OA) is a disease with a multifactorial etiology, involving many pathophysiological processes, and requiring comprehensive assessments to characterize progressive cartilage degradation, subchondral bone remodeling, and chronic pain. This study aimed to integrate quantitative biomarkers of bone texture and morphometry of the articular fossa and joint space to advance the role of imaging phenotypes for diagnosis of Temporomandibular Joint Osteoarthritis (TMJ OA) in early to moderate stages by improving the performance of machine-learning algorithms to detect TMJ OA status. Ninety-two patients were prospectively enrolled (184 h-CBCT scans of the right and left mandibular condyles), divided into two groups: 46 control and 46 TMJ OA subjects. No significant difference in the articular fossa radiomic biomarkers was found between TMJ OA and control patients. The superior condyle-to-fossa distance (p < 0.05) was significantly smaller in diseased patients. The interaction effects of the articular fossa radiomic biomarkers enhanced the performance of machine-learning algorithms to detect TMJ OA status. The LightGBM model achieved an AUC 0.842 to diagnose the TMJ OA status with Headaches and Range of Mouth Opening Without Pain ranked as top features, and top interactions of VE-cadherin in Serum and Angiogenin in Saliva, TGF-β1 in Saliva and Headaches, Gender and Muscle Soreness, PA1 in Saliva and Range of Mouth Opening Without Pain, Lateral Condyle Grey Level Non-Uniformity and Lateral Fossa Short Run Emphasis, TGF-β1 in Serum and Lateral Fossa Trabeculae number, MMP3 in Serum and VEGF in Serum, Headaches and Lateral Fossa Trabecular spacing, Headaches and PA1 in Saliva, and Headaches and BDNF in Saliva. Our preliminary results indicate that condyle imaging features may be more important in regards to main effects, but the fossa imaging features may have a larger contribution in terms of interaction effects. More studies are needed to optimize and further enhance machine-learning algorithms to detect early markers of disease, improve prediction of disease progression and severity to ultimately better serve clinical decision support systems in the treatment of patients with TMJ OA.

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“…After defining the proper source for data collection, it is essential to verify the quality of data and prepare a final data set for analysis or machine training. 31 In 1955, a mathematician named John McCarthy coined the term artificial intelligence (AI) to describe the ability of machines to conduct tasks that lie within the range of intelligent activities.…”

Section: Data Processingmentioning

confidence: 99%

“…After defining the proper source for data collection, it is essential to verify the quality of data and prepare a final data set for analysis or machine training 31 …”

Section: Development Of Clinical Decision Support Systems (Cdsss)mentioning

confidence: 99%

Clinical decision support systems in orthodontics: A narrative review of data science approaches

Turkestani

Bianchi

Deleat‐Besson

et al. 2021

Orthod Craniofacial Res

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Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (c) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems.

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Web infrastructure for data management, storage and computation

Cited by 6 publications

References 0 publications

Automated artificial intelligence‐based three‐dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery

Automated artificial intelligence‐based three‐dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery

Quantitative bone imaging biomarkers and joint space analysis of the articular Fossa in temporomandibular joint osteoarthritis using artificial intelligence models

Clinical decision support systems in orthodontics: A narrative review of data science approaches

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