Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Transformers with Off-Nominal Turns Ratios

Transformers with Off-Nominal Turns Ratios

In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the rated...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Chromosome-level genome assembly of Trichiurus japonicus resolves species boundaries and evolutionary history of cutlassfishes in Northwest Pacific.

BMC genomics·2026

Same author

Histone deacetylase BrHDA19 is involved in regulating bolting time in Chinese cabbage.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026

Same author

Wound healing based on polysaccharide-metal composites: Antibacterial, anti-inflammatory, and tissue regeneration.

International journal of biological macromolecules·2026

Same author

Wholly differentiable virtual lens for ultrathin-plate broadband achromatic imaging.

Optics letters·2026

Same author

Whiteness, Cytocompatibility, and Antibacterial Activity of Carbonate Apatite Granules Containing Zinc Oxide for Bone Regeneration.

ACS omega·2026

Same author

A programmable bioresorbable electrochemical microneedle sensor array for perioperative monitoring of organ health.

Nature biomedical engineering·2026

Same journal

Risk prediction of sepsis-associated acute kidney injury: development, validation of a machine learning model with multicenter data.

BMC medical informatics and decision making·2026

Same journal

Trajectory analysis of sleep disorders and anxiety-depression in female breast cancer patients undergoing chemotherapy: based on group-based Multi-Trajectory Model and machine learning.

BMC medical informatics and decision making·2026

Same journal

Multitask learning of longitudinal circulating biomarkers and clinical outcomes: identification of optimal machine-learning and deep-learning models.

BMC medical informatics and decision making·2026

Same journal

Comparative machine learning approaches to prognosticate clinical outcomes in oral and maxillofacial space infections: a retrospective analysis.

BMC medical informatics and decision making·2026

Same journal

Development and validation of machine learning models for early diagnosis of hemophagocytic lymphohistiocytosis in pediatric Epstein-Barr virus infection.

BMC medical informatics and decision making·2026

Same journal

Clinical subphenotypes in septic patients with new-onset atrial fibrillation: validation and parsimonious classifier model development.

BMC medical informatics and decision making·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 19, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Transformer-based models in dentistry: a systematic review.

Xuhan Zhu^1,2, Mingwei Zhou³, Masahiro Shimogishi¹

¹Department of Regenerative and Reconstructive Dental Medicine, Institute of Science Tokyo, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan.

BMC Medical Informatics and Decision Making

|June 18, 2026

Summary

This summary is machine-generated.

Transformer models show promise in dental AI, with hybrid CNN-Transformer approaches outperforming pure Transformers. Clinical translation requires further validation and standardization for widespread adoption in dental diagnostics.

Keywords:

Convolutional neural network Deep learning Dental diagnostics Dental imaging Medical AI Systematic review Transformer models

More Related Videos

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

Published on: February 23, 2024

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Related Experiment Videos

Last Updated: Jun 19, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

Published on: February 23, 2024

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Area of Science:

Artificial Intelligence in Dentistry
Medical Imaging Analysis
Deep Learning Architectures

Background:

Transformer models excel in medical imaging by capturing long-range dependencies, surpassing traditional CNNs.
Their use in dentistry spans diagnostics, prediction, and generation, but a systematic review is lacking.

Purpose of the Study:

To systematically review and assess the performance and clinical relevance of Transformer-based models in dental imaging and diagnostics.
To provide an up-to-date evaluation of current applications and future potential.

Main Methods:

Systematic literature search across major databases (Medline, Embase, Web of Science, Scopus, Cochrane) from 2020 to August 2025.
Adherence to PRISMA-DTA guidelines and PROSPERO-registered protocol for diagnostic test accuracy reviews.
Independent reviewer screening, data extraction, and bias assessment of 112 included studies.

Main Results:

Hybrid CNN-Transformer models (91 studies) outperformed pure Transformer models (23 studies) in segmentation, classification, and detection tasks.
Hybrid models showed higher accuracy in various dental assessments, with a 5-8% performance gap between internal and external validation.
Generative and predictive applications show promising technical performance, but limitations include data scarcity and lack of real-world validation.

Conclusions:

Transformer models offer significant advancements for dental AI, demonstrating superior technical performance in controlled studies.
Clinical translation is in early stages, necessitating standardized datasets, benchmarks, explainability, and prospective multi-center validation.
Wider adoption requires addressing regulatory, ethical, and real-world workflow integration challenges.