Dataset Information

Reverse Engineering in Orthodontics.

ABSTRACT: Three-dimensional (3D) imaging has advanced greatly and is used extensively in orthodontics. It is worth outlining and reviewing the developments of reverse engineering (RE) as its applications are growing more widespread and diverse. Data from an existing object are used to create a digital model. A traditional RE process is usually performed in these stages: (1) obtaining data, (2) restructuring the surfaces, and (3) creating a useful model. They are classified as (1) laser projection based and (2) fringe projection based. This digital technology has been used in creating 3D model scanning, 3D digital model superimposition, diagnostic setup, volumetric assessment of tooth wear, soft tissue facial analysis, incorporation of digital model to 3D facial image, lip position and smile reproducibility, analysis of tooth position after orthodontic treatment, and anthropometric measurements. This system has proven itself to have a varied probability of applications and researches in the field of orthodontics. Similar to every single system, even RE has its own benefits and shortcomings. The complexity of the process and high cost are the major disadvantages reported so far. Rapid advancement of this technology possibly will rapidly inverse the negative results that emerged previously. As a future work, innovative use of RE technology is necessary to make this system triumph in the field of orthodontics.

SUBMITTER: Ghafoor H

PROVIDER: S-EPMC6340487 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Publications

Reverse Engineering in Orthodontics.

Ghafoor Hajra H

Turkish journal of orthodontics 20181201 4

Three-dimensional (3D) imaging has advanced greatly and is used extensively in orthodontics. It is worth outlining and reviewing the developments of reverse engineering (RE) as its applications are growing more widespread and diverse. Data from an existing object are used to create a digital model. A traditional RE process is usually performed in these stages: (1) obtaining data, (2) restructuring the surfaces, and (3) creating a useful model. They are classified as (1) laser projection based an ...[more]

PMID: 30701225

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Project description:The human ability to introspect on thoughts, perceptions or actions − metacognitive ability − has become a focal topic of both cognitive basic and clinical research. At the same time it has become increasingly clear that currently available quantitative tools are limited in their ability to make unconfounded inferences about metacognition. As a step forward, the present work introduces a comprehensive modeling framework of metacognition that allows for inferences about metacognitive noise and metacognitive biases during the readout of decision values or at the confidence reporting stage. The model assumes that confidence results from a continuous but noisy and potentially biased transformation of decision values, described by a confidence link function. A canonical set of metacognitive noise distributions is introduced which differ, amongst others, in their predictions about metacognitive sign flips of decision values. Successful recovery of model parameters is demonstrated, and the model is validated on an empirical data set. In particular, it is shown that metacognitive noise and bias parameters correlate with conventional behavioral measures. Crucially, in contrast to these conventional measures, metacognitive noise parameters inferred from the model are shown to be independent of performance. This work is accompanied by a toolbox (ReMeta) that allows researchers to estimate key parameters of metacognition in confidence datasets. eLife digest Metacognition is a person’s ability to think about their own thoughts. For example, imagine you are walking in a dark forest when you see an elongated object. You think it is a stick rather than a snake, but how sure are you? Reflecting on one’s certainty about own thoughts or perceptions – confidence – is a prime example of metacognition. While our ability to think about our own thoughts in this way provides many, perhaps uniquely human, advantages, confidence judgements are prone to biases. Often, humans tend to be overconfident: we think we are right more often than we actually are. Internal noise of neural processes can also affect confidence. Understanding these imperfections in metacognition could shed light on how humans think, but studying this phenomenon is challenging. Current methods are lacking either mechanistic insight about the sources of metacognitive biases and noise or rely on unrealistic assumptions. A better model for how metacognition works could provide a clearer picture. Guggenmos developed a mathematical model and a computer toolbox to help researchers investigate how humans or animals estimate confidence in their own thoughts and resulting decisions . The model splits metacognition apart, allowing scientists to explore biases and sources of noise at different phases in the process. It takes two kinds of data: the decisions study participants make, and how sure they are about their decision being correct. It then recreates metacognition in three phases: the primary decision, the metacognitive readout of the evidence, and the confidence report. This allows investigators to see where and when noise and bias come into play. Guggenmos tested the model using independent data from a visual discrimination task and found that it was able to predict how confident participants reported to be in their decisions. Metacognitive ability can change in people with mental illness. People with schizophrenia have often been found to be overconfident in their decisions, while people with depression can be underconfident. Using this model to separate the various facets of metacognition could help to explain why. It could also shed light on human thinking in general.

Dataset Information

Reverse Engineering in Orthodontics.

Publications

Reverse Engineering in Orthodontics.

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