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Intelligent Sensing in Dentistry

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Biomedical Sensors".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 348

Editor


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Guest Editor
Department of Restorative Dentistry, University of Washington, Seattle, WA, USA
Interests: optical coherence tomography, intraoral sensor, restorative dental materials; resin composites; adhesives; invasive dentistry; diagnostic modalities; digital; clinical
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Special Issue Information

Dear Colleagues,

This Special Issue aims to present and disseminate the most recent advances in sensor technologies and Artificial Intelligence (AI) for dental imaging and diagnosis.

AI is rapidly reshaping diagnosis and predictive decision-making in dentistry. At its core, AI leverages machine learning and deep learning to extract clinically meaningful patterns from complex sensor data, often beyond human perception. Diagnostic AI emphasizes the sensing, detection, and classification of oral conditions, health, and disease using images, signals, or structured clinical data from various dental sensors, including imaging detectors, optical probes, and other physical or chemical sensing modalities.

For this Special Issue, we will consider contributions that address common data inputs in dentistry, including the following topics:

  • Conventional radiographic imaging: CMOS/PSP X-ray sensors, image enhancement, and automated lesion detection
  • Cone Beam Computed Tomography (CBCT) volumes: Flat-panel detector technology, 3D reconstruction algorithms, and AI-assisted volumetric analysis
  • Intraoral scans and photographs: Optical sensing (structured light, confocal microscopy, or photogrammetry), 3D surface reconstruction, and color sensing.
  • Optical Coherence Tomography (OCT): Interferometric sensing, fiber-optic probes, and high-resolution subsurface imaging of dental
  • Other optical, mechanical, or chemical sensing and imaging: Fluorescence sensors, Raman spectroscopy, tactile sensors, and electrochemical sensors for biomarkers.

Key sensor-enabled diagnostic applications in dentistry encompass carrier and defect detection, periapical pathology identification, bone loss and periodontal staging, implant stability analysis, color matching, smile design, esthetics, temporomandibular disorder assessment, and oral cancer detection.

Beyond conventional diagnostic models, this Special Issue also welcomes emerging contributions related to agentic AI—AI systems capable of goal‑directed behavior, autonomous decision‑making, iterative reasoning, and interaction with clinical workflows—particularly when such systems are designed to integrate multi-modal sensor data from dental environments.

Dr. Alireza Sadr
Guest Editor

Manuscript Submission Information

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Keywords

  • cone beam computed tomography
  • intraoral scanner
  • optical coherence tomography
  • deep learning
  • dental sensors
  • digital dentistry
  • diagnostic artificial intelligence

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Published Papers (1 paper)

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Research

13 pages, 3156 KB  
Article
Feasibility of Using a Flex Constant to Monitor Implant Stability Changes in a Porcine Model
by Yen-Wei Chen, Weiwei Xu, Alireza Sadr, Kanako Nagatomo, Garrett Porter, Irving S. Scher, I-Chung Wang and I. Y. Shen
Sensors 2026, 26(17), 5516; https://doi.org/10.3390/s26175516 - 31 Aug 2026
Viewed by 203
Abstract
This experimental study investigated the feasibility of using a flex constant (displacement-to-force ratio) to monitor changes in dental implant stability in a porcine model. The stability change was incurred by thawing a porcine model from a frozen state. The changes were simultaneously monitored [...] Read more.
This experimental study investigated the feasibility of using a flex constant (displacement-to-force ratio) to monitor changes in dental implant stability in a porcine model. The stability change was incurred by thawing a porcine model from a frozen state. The changes were simultaneously monitored using resonance frequency analysis (RFA) for comparison. A Straumann BL 4.1×10 mm SLA implant was placed in the retromolar area of a fresh porcine mandible following the manufacturer’s surgical protocol, with the motor set at a torque of 35 N·cm. The porcine mandible was frozen to create a high-stability condition and thawed at room temperature to yield a low-stability condition. Before thawing, a regular connection (RC) locator abutment, 6 mm in length, was connected to the implant platform and secured with a torque of 10 N·cm. A custom-made motor-sensor unit, comprising a haptic unbalanced motor and a digital accelerometer, was press-fit onto the locator abutment. The motor applied a harmonic force (F) to the abutment–implant system, and the accelerometer measured the corresponding displacement (x). The flex constant was obtained as x/F. Implant stability quotient (ISQ) was also measured using Penguin RFA for comparison. To demonstrate the feasibility, only one mandible with one implant was tested. Three rounds of tests were performed to ensure repeatability. For all three rounds of tests, ISQ dropped from high values (>80) in the frozen state to a low value (65–75) in the thawed state, occurring about 30–40 min after thawing. The drop in ISQ indicated reduction in implant stability when the frozen mandible thawed. The flex constant increased from 0.4 to 0.5 μm/N in the frozen state to 0.9–1.8 μm/N in the thawed state, with the transition occurring 30–50 min after thawing began. The increase in the flex constant implied larger displacement under the same force, indicating reduced stability. A transition peak was also observed in the flex constant measurement between the frozen and thawed states, likely due to thermal expansion and moisture condensation at the interface between the motor-sensor unit and the locator abutment. Test results from the porcine model indicate that a flex constant can reflect the changes in dental implant stability as an RFA device. Full article
(This article belongs to the Special Issue Intelligent Sensing in Dentistry)
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