Influence of ambient temperature changes on intraoral scanning accuracy

Revilla‐León, Marta; Gohil, Aishwa; Barmak, Abdul Basir; Gómez‐Polo, Miguel; Pérez‐Barquero, Jorge Alonso; Att, Wael; Kois, John C.

doi:10.1016/j.prosdent.2022.01.012

Cited by 70 publications

(56 citation statements)

References 34 publications

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“…High-performance imaging applications like superresolution microscopy and cancer detection have made machine learning a potent general tool for scientific data processing: lung cancer diagnostic, human medium expression, and sample classification [ 13 ]. Deep learning was used to denoise SRS microscopy and spectra.…”

Section: Classification Difficultiesmentioning

confidence: 99%

Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm

et al. 2022

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Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemical species map for a lithium ore sample is produced using k -means clustering. Many researchers are now interested in biosignals. Uncertainty limits the algorithms’ capacity to evaluate these signals for further information. Even while AI systems can answer puzzles, they remain limited. Deep learning is used when machine learning is inefficient. Supervised learning needs a lot of data. Deep learning is vital in modern AI. Supervised learning requires a large labeled dataset. The selection of parameters prevents over- or underfitting. Unsupervised learning is used to overcome the challenges outlined above (performed by the clustering algorithm). To accomplish this, two processing processes were used: (1) utilizing nonlinear deep learning networks to turn data into a latent feature space ( Z ). The Kullback–Leibler divergence is used to test the objective function convergence. This article explores a novel research on hyperspectral microscopic picture using deep learning and effective unsupervised learning.

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Section: Classification Difficultiesmentioning

confidence: 99%

Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm

et al. 2022

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“…Dental literature has recently identified ambient temperature changes as a variable that can influence intraoral scanning accuracy. 49 The exposure of an IOS to ambient temperature changes can easily occur in a dental practice, university, or dental institution between working and nonworking hours or even during the same day. These ambient temperature changes decalibrate the IOS and, subsequently, reduce its scanning accuracy.…”

Section: Ambient Temperature Changesmentioning

confidence: 99%

“…Additionally, a specific calibration device and protocol is provided by each IOS manufacturer (Figure 4). Although IOS software programs deliver alerts requiring the calibration of the system based on the time since the last calibration or the number of intraoral digital scans acquired since the latter calibration, dental professionals should probably include protocols in their practices to ensure daily IOS calibration before starting data collection procedures 49 …”

Section: Introductionmentioning

confidence: 99%

A guide for maximizing the accuracy of intraoral digital scans. Part 1: Operator factors

Revilla‐León

Kois

2022

J Esthet Restor Dent

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Objectives To describe the factors related to the operator skills and decisions that influence the scanning accuracy of intraoral scanners (IOSs). A new classification for these factors is proposed to facilitate dental professionals' decision making when using IOSs and maximize the accuracy and reliability of intraoral digital scans. Overview Each IOS system is limited by the hardware and software characteristics of the selected device. The operator decisions that can influence the accuracy of IOSs include the scanning technology and system selection, scanning head size, calibration, scanning distance, exposure of the IOS to ambient temperature changes, ambient humidity, ambient lighting conditions, operator experience, scanning pattern, extension of the scan, cutting off, rescanning, and overlapping procedures. Conclusions The knowledge and understanding of the operator factors that impact scanning accuracy of IOSs is a fundamental element for maximizing the accuracy of IOSs and for successfully integrating IOSs in daily practices. Clinical Significance Operator skills and clinical decisions significantly impact intraoral scanning accuracy. Dental professionals must know and understand these influencing operator factors for maximizing the accuracy of IOSs.

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“…[22][23][24] Ensure that the IOS device has been previously calibrated accordingly the manufacturer's recommended protocol. 25 Then, export the standard tessellation language (STL) file. 2.…”

Section: F I G U R Ementioning

confidence: 99%

“… Acquire an intraoral digital scan of the maxillary and mandibular arches and a maxillo‐mandibular registration by using an intraoral scanner (IOS) (TRIOS 4, wireless, v. 21.2.0; 3Shape A/S) following the scanning pattern endorsed by the manufacturer under optimal ambient lighting conditions 22–24 . Ensure that the IOS device has been previously calibrated accordingly the manufacturer's recommended protocol 25 . Then, export the standard tessellation language (STL) file. Obtain a 360° facial scan of the patient at seal position by using a light structured facial scanner (Instarisa facial scanner; Instarisa) following the scanning pattern recommended by the manufacturer.…”

Section: Techniquementioning

confidence: 99%

Tracking and recording the lip dynamics for the integration of a dynamic virtual patient: A novel dental technique

Revilla‐León

Zeitler

Blanco-Fernández³

et al. 2022

Journal of Prosthodontics

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A step‐by‐step technique is described for tracking and recording, in real time, the lip dynamics of a patient and integrating them into a 3D virtual patient representation using facial trackers and motion engine software programs. The main advantage of this technique is that it enables capturing of the lip movements of the patient in real time; therefore, the lip movements are not simulated from a previously recorded video of the patient using animation software programs.

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Influence of ambient temperature changes on intraoral scanning accuracy

Cited by 70 publications

References 34 publications

Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm

Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm

A guide for maximizing the accuracy of intraoral digital scans. Part 1: Operator factors

Tracking and recording the lip dynamics for the integration of a dynamic virtual patient: A novel dental technique

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