Intraoral Ultrasound Imaging Using a Rotational Transducer with Periodontal Feature Identification by Machine Learning.

artificial intelligence digital health intraoral imaging machine learning precision health ultrasound imaging

Journal

ACS sensors
ISSN: 2379-3694
Titre abrégé: ACS Sens
Pays: United States
ID NLM: 101669031

Informations de publication

Date de publication:
23 Aug 2024
Historique:
medline: 23 8 2024
pubmed: 23 8 2024
entrez: 23 8 2024
Statut: ppublish

Résumé

Innovative intraoral ultrasound devices with smart artificial intelligence-based identification for dento-anatomy could provide crucial information for oral health diagnosis and treatment and shed light on real-time detection of developmental dentistry. However, the grand challenge is that the current ultrasound technologies are meant for external use due to their bulkiness and low frequency. We report a compact versatile ultrasound intraoral device that consists of a rotational probe head robustly pivoted around a hand-held and portable handle for real-time imaging of intraoral anatomy using high-frequency ultrasonography (up to 25 MHz). The intraoral ultrasound device that could be adjusted for various orientations of the imaging planes by rotating the head provides real-time, high-resolution ultrasonograms of intraoral structures, including dento-periodontium of most tooth types and maxillary palate. Machine learning-based algorithms are integrated to automate the identification of important structures, including alveolar bone and cementum-enamel junction. The intraoral ultrasound device smartened with artificial intelligence could innovate oral health diagnosis and treatment plans toward precision health and patient care.

Identifiants

pubmed: 39175386
doi: 10.1021/acssensors.4c00124
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3898-3906

Auteurs

Lawrence H Le (LH)

Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada T6G 2R7.
Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2 V2.
School of Dentistry, University of Alberta, Edmonton, Alberta, Canada T6G 1C9.
Department of Physics, University of Alberta, Edmonton, Alberta, Canada T6G 2E1.

Kim-Cuong T Nguyen (KT)

Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada T6G 2R7.
Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2 V2.

Thanh-Giang La (TG)

Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada T6G 2R7.

Vu Duc Nguyen (VD)

Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada T6G 2R7.

Minh Binh Le (MB)

Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada T6G 2R7.

Punithakumar Kumaradevan (P)

Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada T6G 2R7.

Neelambar Kaipatur (N)

School of Dentistry, University of Alberta, Edmonton, Alberta, Canada T6G 1C9.

Paul W Major (PW)

School of Dentistry, University of Alberta, Edmonton, Alberta, Canada T6G 1C9.

Edmond H M Lou (EHM)

Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2 V2.
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 1H9.

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Classifications MeSH