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|link=https://cantiere.masticationpedia.org//index.php/File:Schrodingers_cat.svg|alt=|left|frameless]] | |link=https://cantiere.masticationpedia.org//index.php/File:Schrodingers_cat.svg|alt=|left|frameless]] | ||
The discussed chapter explores the application of quantum mechanics principles to understand the complex and stochastic dynamics of the masticatory system. It challenges the deterministic views typically held in classical dental sciences, suggesting that phenomena such as "Normocclusion or Malocclusion" should not be solely interpreted through classical mechanics due to their inherent quantum characteristics. The narrative introduces Schrödinger's cat as a metaphor to demonstrate how the masticatory system, like the quantum example, may exist in superposed states that are not observable without interaction. This concept is used to question the traditional methods of diagnosing and understanding malocclusion in orthodontics and maxillofacial surgery. | |||
The text delves into the philosophical implications of quantum superposition by likening it to the complex interactions within the masticatory system. This is exemplified through the discussion of two patients: one with a classical diagnosis of malocclusion and another whose post-orthognathic surgery state reveals significant trigeminal nerve asymmetries despite apparent occlusal normalcy. These cases illustrate that macroscopic evaluations may not fully capture the underlying mesoscopic phenomena essential for a comprehensive understanding of the system's state. | |||
Further, the narrative employs the paradox of Schrödinger's cat to challenge the reader's understanding of observation and measurement in quantum physics, relating it back to the diagnosis in dental sciences. It suggests that the state of the masticatory system, like the quantum state of the cat, might exist in a superposition that requires new diagnostic models to measure effectively. This quantum perspective prompts a reconsideration of how dental professionals interpret clinical data, urging a shift from deterministic to probabilistic and quantum-informed approaches. | |||
The chapter concludes by proposing a need for dental science to adopt quantum mathematical models to better grasp the underlying dynamics of dental and facial abnormalities. This approach aims to foster a deeper understanding of the masticatory system's behavior beyond traditional methods, advocating for a paradigm shift in dental diagnostics and treatment planning.<blockquote> | |||
== Keywords == | |||
'''Quantum Mechanics in Dentistry''' - Explores the application of quantum physics principles in understanding complex dental systems, specifically how these principles challenge traditional deterministic views. | |||
'''Masticatory System Complexity''' - Focuses on the complex interactions within the masticatory system that resemble quantum phenomena, highlighting the limitations of classical approaches in diagnosing and understanding dental issues. | |||
'''Malocclusion Quantum Analysis''' - Discusses the reinterpretation of malocclusion through quantum mechanics, suggesting that dental conditions might exist in multiple states simultaneously, much like quantum superposition. | |||
'''Schrödinger’s Cat in Medical Diagnostics''' - Uses the metaphor of Schrödinger's cat to illustrate the challenges of observing and diagnosing medical conditions without influencing the system, relevant for understanding hidden complexities in dental health. | |||
'''Quantum Superposition in Orthodontics''' - Relates quantum superposition to orthodontic diagnoses, proposing that the true state of dental alignments may involve complex, unobservable interactions until measured. | |||
'''Trigeminal Nerve Asymmetry''' - Focuses on the diagnostic challenges posed by trigeminal nerve asymmetries in patients, which may not be fully explained by traditional occlusal analyses. | |||
'''Quantum Mathematical Models in Dentistry''' - Advocates for the integration of quantum mathematical models in dental science to enhance the accuracy and depth of diagnostics and treatment planning. | |||
'''Probabilistic Models in Dental Diagnostics''' - Suggests shifting from deterministic to probabilistic models in dental diagnostics to better accommodate the inherent uncertainties and complex behaviors of dental systems.</blockquote> | |||
{{ArtBy| | |||
| autore = Gianni Frisardi | | autore = Gianni Frisardi | ||
| autore2 = Alice Bisirri | | autore2 = Alice Bisirri | ||
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[[File:EEG.jpeg| thumb|'''Figure 4:''' An EEG trace corresponds to the space-time summation of a series of wave frequencies <math>(\delta,\theta,\alpha,\beta,\gamma)</math> where a dot (red / arrow) will correspond to positions spatially different than the recorded wave frequencies. (Lagrange coordinates)|alt=|center|385x385px]] | [[File:EEG.jpeg| thumb|'''Figure 4:''' An EEG trace corresponds to the space-time summation of a series of wave frequencies <math>(\delta,\theta,\alpha,\beta,\gamma)</math> where a dot (red / arrow) will correspond to positions spatially different than the recorded wave frequencies. (Lagrange coordinates)|alt=|center|385x385px]] | ||
===== Electroencephalography (EEG) ===== | =====Electroencephalography (EEG) ===== | ||
To stay on the neurophysiological theme, let's consider EEG electroencephalography. The measuring instrument basically measures nothing more than the difference in ionic electric potential 'dipole' that moves at sustained speeds here and there between the neural interconnections (Lagrangian coordinates) <ref>Bin-Qiang Chen, Bai-Xun Zheng, Chu-Qiao Wang, Wei-Fang Sun. [https://pubmed.ncbi.nlm.nih.gov/34026718/ Adaptive Sparse Detector for Suppressing Powerline Component in EEG Measurements]. Front Public Health. 2021 May 7;9:669190. doi: 10.3389/fpubh.2021.669190. eCollection 2021. | To stay on the neurophysiological theme, let's consider EEG electroencephalography. The measuring instrument basically measures nothing more than the difference in ionic electric potential 'dipole' that moves at sustained speeds here and there between the neural interconnections (Lagrangian coordinates) <ref>Bin-Qiang Chen, Bai-Xun Zheng, Chu-Qiao Wang, Wei-Fang Sun. [https://pubmed.ncbi.nlm.nih.gov/34026718/ Adaptive Sparse Detector for Suppressing Powerline Component in EEG Measurements]. Front Public Health. 2021 May 7;9:669190. doi: 10.3389/fpubh.2021.669190. eCollection 2021. | ||
</ref>). Figure 4 | </ref>). Figure 4 | ||
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