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[[File:Rete neurale completa1-2.png|left|frameless]] | [[File:Rete neurale completa1-2.png|left|frameless]] | ||
The term 'Cognitive Neural Network' | |||
The term 'Cognitive Neural Network' (CNN), distinct from traditional machine learning, engages clinicians actively in the diagnostic process, enhancing their capabilities through a dynamic learning environment. Unlike static systems, the CNN evolves in real-time, guiding clinicians with intelligent feedback and learning from each interaction. | |||
CNNs operate on a different premise than standard machine learning models, focusing on enhancing the clinician's diagnostic process through active interaction rather than passive data analysis. This approach allows for real-time cognitive development and decision-making support, making the CNN an invaluable tool in clinical settings. | |||
Central to the CNN's functionality is its integration with basic knowledge databases such as Pubmed, which provides a robust platform for clinicians to access current medical data and research (represented as <math>KB_t</math> and <math>KB_c</math>). This integration ensures that diagnostics are not only based on real-time data but are also continually updated with the latest medical research and findings. | |||
The use of CNN in the diagnosis of 'Hemimasticatory Spasm' demonstrates its practical application. The complex nature of this condition and the prolonged diagnostic journey of patient Mary Poppins underscore the necessity for a sophisticated approach like the CNN, which leverages quantum probability and structured diagnostic pathways. | |||
Unlike automated diagnostic tools, the CNN places the clinician at the center of the diagnostic process. This clinician-led approach ensures that each diagnostic path is not only explored thoroughly but is also tailored to the individual patient’s needs, leading to more effective and personalized treatment plans. | |||
While the potential of CNNs in transforming medical diagnostics is immense, their integration into clinical practice requires overcoming significant barriers. These include technological adaptation, data privacy concerns, and the need for substantial training for medical personnel. | |||
The Cognitive Neural Network is a pioneering tool in the field of medical diagnostics, promising to revolutionize how clinicians interact with and utilize diagnostic tools. By enhancing the accuracy and efficacy of diagnostics through a profound cognitive process, CNNs hold the potential to significantly improve patient outcomes.<blockquote> | |||
== Keywords == | |||
'''Cognitive Neural Network''' - A key term that highlights the unique approach of using an interactive and cognitive network in medical diagnostics. | |||
'''Machine Learning in Medicine''' - Emphasizes the distinction between traditional machine learning applications and the interactive CNN. | |||
'''Hemimasticatory Spasm Diagnosis''' - Specific to the case study provided, focusing on the diagnosis of a complex medical condition. | |||
'''Quantum Probability in Medicine''' - Integrates the concept of quantum probability in diagnosing and understanding complex biological systems. | |||
'''Medical Knowledge Base''' - Refers to the integration of comprehensive databases like Pubmed into the diagnostic process. | |||
'''Clinician-led Diagnostic Tools''' - Highlights the role of clinicians in actively driving the diagnostic process using CNN. | |||
'''Real-time Medical Diagnostics''' - Focuses on the real-time capabilities of CNNs in providing diagnostic support. | |||
'''Medical Diagnostic Innovations''' - Broad term to capture searches related to new technologies and methodologies in diagnostics. | |||
'''Patient-centered Diagnostics''' - Emphasizes personalized treatment plans and diagnostics centered around individual patient needs. | |||
'''Advanced Diagnostic Processes''' - Pertains to sophisticated diagnostic approaches that leverage new technologies and cognitive processes.</blockquote> | |||
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}} | }} | ||
== Introduction == | == Introduction== | ||
In the chapter '[[1° Clinical case: Hemimasticatory spasm - en|1st Clinical case: Hemimasticatory spasm]]' we immediately reached a conclusion bypassing all the cognitive, clinical and scientific process which underlies the diagnostic definition but it is not that simple otherwise our poor patient Mary Poppins, would not have had to wait 10 years for the correct diagnosis.<blockquote>It should be emphasized that it is not a question of negligence on the part of clinicians rather of the complexity of 'biological systems' and above all of a mindset still anchored to a 'classical probability'. The 'Classical probability' categorizes healthy and diseased phenotypes according to symptoms and signs sampled clinicians instead of probing the 'State' of the system in the temporal evolution. This concept, anticipated in the chapter '[[Logic of medical language: Introduction to quantum-like probability in the masticatory system]]' and in '[[Conclusions on the status quo in the logic of medical language regarding the masticatory system]]' has laid the foundations for a medical language more articulated and less deterministic, mainly focused on the 'State' of the 'Mesoscopic System' whose purpose is, essentially, to decrypt the message in machine language generated by the Central Nervous System. We will assist in the description of other clinical cases that will be reported in the next Masticationpedia chapters. </blockquote>This model, which we propose with the term 'Cognitive Neural Network' abbreviated as 'CNN' is a dynamic cognitive intellectual process of the clinician who interrogates the network for self-training. The 'CNN' is not a 'Machine Learning' because while the latter must be trained by the clinician, with statistical and prediction adjustments, the 'CNN' trains the clinician or rather directs the clinician to the diagnosis while always being questioned following a logical human, hence the term 'Cognitive'. | In the chapter '[[1° Clinical case: Hemimasticatory spasm - en|1st Clinical case: Hemimasticatory spasm]]' we immediately reached a conclusion bypassing all the cognitive, clinical and scientific process which underlies the diagnostic definition but it is not that simple otherwise our poor patient Mary Poppins, would not have had to wait 10 years for the correct diagnosis.<blockquote>It should be emphasized that it is not a question of negligence on the part of clinicians rather of the complexity of 'biological systems' and above all of a mindset still anchored to a 'classical probability'. The 'Classical probability' categorizes healthy and diseased phenotypes according to symptoms and signs sampled clinicians instead of probing the 'State' of the system in the temporal evolution. This concept, anticipated in the chapter '[[Logic of medical language: Introduction to quantum-like probability in the masticatory system]]' and in '[[Conclusions on the status quo in the logic of medical language regarding the masticatory system]]' has laid the foundations for a medical language more articulated and less deterministic, mainly focused on the 'State' of the 'Mesoscopic System' whose purpose is, essentially, to decrypt the message in machine language generated by the Central Nervous System. We will assist in the description of other clinical cases that will be reported in the next Masticationpedia chapters. </blockquote>This model, which we propose with the term 'Cognitive Neural Network' abbreviated as 'CNN' is a dynamic cognitive intellectual process of the clinician who interrogates the network for self-training. The 'CNN' is not a 'Machine Learning' because while the latter must be trained by the clinician, with statistical and prediction adjustments, the 'CNN' trains the clinician or rather directs the clinician to the diagnosis while always being questioned following a logical human, hence the term 'Cognitive'. | ||
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In essence, the encrypted machine language message sent out by the Central Nervous System in the 10 years of illness of our patient Mary Poppins was interpreted, through verbal language, as Orofacial Pain from Temporomandibular Disorders'. We have remarked several times, however, that human verbal language is distorted by vagueness and ambiguity therefore, not being a formal language such as mathematical language, it can generate diagnostic errors. The message in machine language sent out by the Central Nervous System to be searched for is not pain (pain is a verbal language) but the 'Anomaly of System State' in which the organism was in that time period. Hence the shift from the semiotics of the symptom and the clinical sign to the '[[System logic|System Logic]]' which, through 'System's Theory' models, quantify the system's responses to incoming stimuli, even in healthy subjects. | In essence, the encrypted machine language message sent out by the Central Nervous System in the 10 years of illness of our patient Mary Poppins was interpreted, through verbal language, as Orofacial Pain from Temporomandibular Disorders'. We have remarked several times, however, that human verbal language is distorted by vagueness and ambiguity therefore, not being a formal language such as mathematical language, it can generate diagnostic errors. The message in machine language sent out by the Central Nervous System to be searched for is not pain (pain is a verbal language) but the 'Anomaly of System State' in which the organism was in that time period. Hence the shift from the semiotics of the symptom and the clinical sign to the '[[System logic|System Logic]]' which, through 'System's Theory' models, quantify the system's responses to incoming stimuli, even in healthy subjects. | ||
All this conceptuality is replicated in the proposed 'CNN' model by dividing the process into incoming triggers (Input) and outgoing data (Output) to then be reiterated in a loop managed cognitively by the clinician up to the generation of a single node useful for the definitive diagnosis. The model basically breaks down as follows: | All this conceptuality is replicated in the proposed 'CNN' model by dividing the process into incoming triggers (Input) and outgoing data (Output) to then be reiterated in a loop managed cognitively by the clinician up to the generation of a single node useful for the definitive diagnosis. The model basically breaks down as follows: | ||
* '''Input:''' By incoming trigger, we mean the cognitive process that the clinician implements as a function of the considerations received from previous statements, as has been pointed out in the chapters concerning the 'Medical language logic'. In our case, through the '<math>\tau</math> Consistency Demarcator, the neurological context was defined as suitable instead of the dental one pursuing a clinical diagnostic explanation of TMDs. | *'''Input:''' By incoming trigger, we mean the cognitive process that the clinician implements as a function of the considerations received from previous statements, as has been pointed out in the chapters concerning the 'Medical language logic'. In our case, through the '<math>\tau</math> Consistency Demarcator, the neurological context was defined as suitable instead of the dental one pursuing a clinical diagnostic explanation of TMDs. | ||
**This trigger is of essential importance because it allows the clinician to point out the network analysis 'Initialization command' which will connect a large sample of data corresponding to the set trigger. To this essential 'Initialization command', as an algorithmic decryption key, is added the last closing command which is equally important as it depends on the intuition of the clinician who will consider the decryption process finished. | **This trigger is of essential importance because it allows the clinician to point out the network analysis 'Initialization command' which will connect a large sample of data corresponding to the set trigger. To this essential 'Initialization command', as an algorithmic decryption key, is added the last closing command which is equally important as it depends on the intuition of the clinician who will consider the decryption process finished. | ||
**In Figure 1 the structure of the 'CNN' is represented where the difference between the most common neural network structures can be noted and in which the first stage is structured with a high number of input variables. In our 'CNN' the first stage corresponds only to a node and precisely to the network analysis of 'Initialization command' called ' <math>\tau</math> Coherence Demarcator', the subsequent loops of the network, which allow the clinician to terminate or to reiterate the network, (1st loop open, 2st loop open,...... nst loop open) are decisive for concluding the decryption process ( Decrypted Code ). This step will be explained in more detail later in the chapter. | ** In Figure 1 the structure of the 'CNN' is represented where the difference between the most common neural network structures can be noted and in which the first stage is structured with a high number of input variables. In our 'CNN' the first stage corresponds only to a node and precisely to the network analysis of 'Initialization command' called ' <math>\tau</math> Coherence Demarcator', the subsequent loops of the network, which allow the clinician to terminate or to reiterate the network, (1st loop open, 2st loop open,...... nst loop open) are decisive for concluding the decryption process ( Decrypted Code ). This step will be explained in more detail later in the chapter. | ||
[[File:Immagine 17-12-22 alle 11.34.jpeg|center|500x500px|'''Figure 1:'''Graphical representation of the 'CNN' proposed by Masticationpedia|thumb]] | [[File:Immagine 17-12-22 alle 11.34.jpeg|center|500x500px|'''Figure 1:'''Graphical representation of the 'CNN' proposed by Masticationpedia|thumb]] | ||
<center></center> | <center></center> | ||
* '''Output:''' The outgoing data from the network, which substantially correspond to a precise cognitive trigger request, returns a large number of data classified and correlated to the requested keyword. The clinician will have to devote time and concentration to continue decrypting the machine code. In fact, we have witnessed how, following the indications dictated by research criteria such as the 'Research Diagnostic Criteria' (RDC), our patient Mary Poppins was immediately categorized as 'TMDs'. We have also suggested a way to expand diagnostic capabilities in dentistry through a 'fuzzy' model that would allow to range in contexts other than one's own. This shows the complexity in making differential diagnoses and the difficulties in following a classical semiotic roadmap because we are anchored too much to verbal language and too little to a quantum culture of biological systems. This borders on the concept of machine language and initialization decryption command that we will briefly explain in the next paragraph. | *'''Output:''' The outgoing data from the network, which substantially correspond to a precise cognitive trigger request, returns a large number of data classified and correlated to the requested keyword. The clinician will have to devote time and concentration to continue decrypting the machine code. In fact, we have witnessed how, following the indications dictated by research criteria such as the 'Research Diagnostic Criteria' (RDC), our patient Mary Poppins was immediately categorized as 'TMDs'. We have also suggested a way to expand diagnostic capabilities in dentistry through a 'fuzzy' model that would allow to range in contexts other than one's own. This shows the complexity in making differential diagnoses and the difficulties in following a classical semiotic roadmap because we are anchored too much to verbal language and too little to a quantum culture of biological systems. This borders on the concept of machine language and initialization decryption command that we will briefly explain in the next paragraph. | ||
=== Initiatialization command === | ===Initiatialization command=== | ||
For a moment let's imagine that the brain speaks the language of a computer and not vice versa as happens in engineering, to distinguish the aforementioned difference between machine language and human verbal language. To write a sentence, a word or a formula, the computer does not use the classic verbal mode (alphabet) or the decimal mode (numbers) with which we write mathematical formulas but its own 'writing' language code called html code for the web. Let's take as an example the writing of a fairly complex formula, it is presented to our brain in the verbal language with which we have learned to read a mathematical equation, in the following form: | For a moment let's imagine that the brain speaks the language of a computer and not vice versa as happens in engineering, to distinguish the aforementioned difference between machine language and human verbal language. To write a sentence, a word or a formula, the computer does not use the classic verbal mode (alphabet) or the decimal mode (numbers) with which we write mathematical formulas but its own 'writing' language code called html code for the web. Let's take as an example the writing of a fairly complex formula, it is presented to our brain in the verbal language with which we have learned to read a mathematical equation, in the following form: | ||
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+2\sum_{\alpha_1<\alpha_2}\cos\theta_{\alpha_1\alpha_2}\sqrt{P(A=\alpha_1)P(B=\beta|A=\alpha_1)} P(A=\alpha_2) P(B=\beta|a=\alpha_2)<blockquote>Just as the lack of part of the binary code corrupts the representation of the formula, similarly the decryption of the machine language of the CNS is a source of vagueness and ambiguity of the verbal language and contextually of diagnostic error.</blockquote> | +2\sum_{\alpha_1<\alpha_2}\cos\theta_{\alpha_1\alpha_2}\sqrt{P(A=\alpha_1)P(B=\beta|A=\alpha_1)} P(A=\alpha_2) P(B=\beta|a=\alpha_2)<blockquote>Just as the lack of part of the binary code corrupts the representation of the formula, similarly the decryption of the machine language of the CNS is a source of vagueness and ambiguity of the verbal language and contextually of diagnostic error.</blockquote> | ||
=== Cognitive process === | ===Cognitive process === | ||
----The heart of the 'CNN' model lies in the cognitive process referred exclusively to the clinician who is at the helm while the network essentially remains the compass that warns of off course and/or suggests other alternative routes but the decision-making responsibility always refers to the clinician ( human mind). In this simple definition, we will perceive it better at the end of the chapter, the synergism 'Neural network' and 'Human cognitive process' of the clinician will be self-implementing because, on the one hand, the clinician is trained or better guided by the neural network (database), while, the last one will be trained on the latest updated scientific-clinical event. Basically, the definitive diagnosis will add an additional piece of information to the temporal base knowledge <math>Kb_t</math>. This model differs substantially from 'machine learning' just by observing the two models in their structural configuration (Figures 1 and 3). | ----The heart of the 'CNN' model lies in the cognitive process referred exclusively to the clinician who is at the helm while the network essentially remains the compass that warns of off course and/or suggests other alternative routes but the decision-making responsibility always refers to the clinician ( human mind). In this simple definition, we will perceive it better at the end of the chapter, the synergism 'Neural network' and 'Human cognitive process' of the clinician will be self-implementing because, on the one hand, the clinician is trained or better guided by the neural network (database), while, the last one will be trained on the latest updated scientific-clinical event. Basically, the definitive diagnosis will add an additional piece of information to the temporal base knowledge <math>Kb_t</math>. This model differs substantially from 'machine learning' just by observing the two models in their structural configuration (Figures 1 and 3). | ||
[[File:Joim12822-fig-0004-m.jpeg|alt=|left|thumb|200x200px|'''Figure 3:''' Graphic representation of an archetypal ANN in which it can be seen in the first stage of initialization where there are five input nodes<ref name=":1">G S Handelman, H K Kok, R V Chandra, A H Razavi, M J Lee, H Asadi. eDoctor: machine learning and the future of medicine.J Intern Med.2018 Dec;284(6):603-619.doi: 10.1111/joim.12822. Epub 2018 Sep 3.</ref> while in the 'CNN' model the first stage is composed of only one node. Follow text. ]]Figure 3 shows a typical neural network, also known as artificial NNs. These artificial NNs attempt to use multiple layers of calculations to mimic the concept of how the human brain interprets and draws conclusions from information.<ref name=":1" /> NNs are essentially mathematical models designed to handle complex and disparate information, and this algorithm's nomenclature comes from its use of synapse-like "nodes" in the brain.<ref>Schwarzer G, Vach W, Schumacher M. On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med 2000; 19: 541–61.</ref> The learning process of a NN can be supervised or unsupervised. A neural network is said to learn in a supervised manner if the desired output is already targeted and introduced into the network by data training, while, unsupervised NN has no such pre-identified target outputs and the goal is to group similar units close together in certain areas of the range of values. The supervised module takes data (e.g., symptoms, risk factors, imaging and laboratory findings) for training on known outcomes and searches for different combinations to find the most predictive combination of variables. NN assigns more or less weight to certain combinations of nodes to optimize the predictive performance of the trained model.<ref>Abdi H. A neural network primer. J Biol Syst 1994; 02: 247–81.</ref> | [[File:Joim12822-fig-0004-m.jpeg|alt=|left|thumb|200x200px|'''Figure 3:''' Graphic representation of an archetypal ANN in which it can be seen in the first stage of initialization where there are five input nodes<ref name=":1">G S Handelman, H K Kok, R V Chandra, A H Razavi, M J Lee, H Asadi. eDoctor: machine learning and the future of medicine.J Intern Med.2018 Dec;284(6):603-619.doi: 10.1111/joim.12822. Epub 2018 Sep 3.</ref> while in the 'CNN' model the first stage is composed of only one node. Follow text. ]]Figure 3 shows a typical neural network, also known as artificial NNs. These artificial NNs attempt to use multiple layers of calculations to mimic the concept of how the human brain interprets and draws conclusions from information.<ref name=":1" /> NNs are essentially mathematical models designed to handle complex and disparate information, and this algorithm's nomenclature comes from its use of synapse-like "nodes" in the brain.<ref>Schwarzer G, Vach W, Schumacher M. On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med 2000; 19: 541–61.</ref> The learning process of a NN can be supervised or unsupervised. A neural network is said to learn in a supervised manner if the desired output is already targeted and introduced into the network by data training, while, unsupervised NN has no such pre-identified target outputs and the goal is to group similar units close together in certain areas of the range of values. The supervised module takes data (e.g., symptoms, risk factors, imaging and laboratory findings) for training on known outcomes and searches for different combinations to find the most predictive combination of variables. NN assigns more or less weight to certain combinations of nodes to optimize the predictive performance of the trained model.<ref>Abdi H. A neural network primer. J Biol Syst 1994; 02: 247–81.</ref> | ||
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But let's see in detail how a 'CNN' is built | But let's see in detail how a 'CNN' is built | ||
== Cognitive Neural Network == | == Cognitive Neural Network== | ||
In this paragraph it seems necessary to explain the clinical process followed with the support of the 'CNN' following step by step the cognitive queries to the network and the cognitive analysis performed on the data in response from the network. The map has also been shown in figure 4 with links to the network responses that can be viewed for more consistent documentation: | In this paragraph it seems necessary to explain the clinical process followed with the support of the 'CNN' following step by step the cognitive queries to the network and the cognitive analysis performed on the data in response from the network. The map has also been shown in figure 4 with links to the network responses that can be viewed for more consistent documentation: | ||
* '''<math>\tau</math> Coherence Demarcator:''' As we have previously described, the first step is a network analysis initialization command that derives, in fact, from a previous cognitive processing of the assertions in the dental context <math>\delta_n</math> and the neurological one <math>\gamma_n</math> to which the ' <math>\tau</math> Coherence Demarcator' gave absolute weight by, effectively, eliminating the dental context <math>\delta_n</math> from the process. From what emerges from the neurological assertions <math>\gamma_n</math> the 'State' of the Trigeminal Nervous System appears unstructured highlighting anomalies of the trigeminal reflexes for which the 'Initialization' command will be the '[https://pubmed.ncbi.nlm.nih.gov/?term=trigeminal+reflex+&size=200 Trigeminal Reflex]' to go and<nowiki/> test the database (Pubmed). | *'''<math>\tau</math> Coherence Demarcator:''' As we have previously described, the first step is a network analysis initialization command that derives, in fact, from a previous cognitive processing of the assertions in the dental context <math>\delta_n</math> and the neurological one <math>\gamma_n</math> to which the ' <math>\tau</math> Coherence Demarcator' gave absolute weight by, effectively, eliminating the dental context <math>\delta_n</math> from the process. From what emerges from the neurological assertions <math>\gamma_n</math> the 'State' of the Trigeminal Nervous System appears unstructured highlighting anomalies of the trigeminal reflexes for which the 'Initialization' command will be the '[https://pubmed.ncbi.nlm.nih.gov/?term=trigeminal+reflex+&size=200 Trigeminal Reflex]' to go and<nowiki/> test the database (Pubmed). | ||
*<nowiki/><nowiki/>'''1<sup>st</sup> loop open:''' This 'Initialization command', therefore, is considered as initial input for the Pubmed database which responds with 2,466 clinical and experimental data available to the clinician. The opening of the first true cognitive analysis is elaborated precisely on the analysis of the first result of the 'CNN' corresponding to 'Trigeminal Reflex'. In this phase we realize that a discrete percentage of data reveal a correspondence between trigeminal reflex abnormalities and demyelination problems, therefore the 1<sup>st</sup> loop open will correspond to: '[https://pubmed.ncbi.nlm.nih.gov/?term=trigeminal+reflex+demyelinating+neuropathy+&size=200 Demyelinating neuropathy]<nowiki>''</nowiki> which will return 14 sensitive data. Behind the choice of this key there is an active and dynamic cognitive process of the clinician. From the assertions in the neurological context, a neuropathic pathology was hypothesized in which the demyelinating aspect should also be co<nowiki/><nowiki/>nsidered. | *<nowiki/><nowiki/>'''1<sup>st</sup> loop open:''' This 'Initialization command', therefore, is considered as initial input for the Pubmed database which responds with 2,466 clinical and experimental data available to the clinician. The opening of the first true cognitive analysis is elaborated precisely on the analysis of the first result of the 'CNN' corresponding to 'Trigeminal Reflex'. In this phase we realize that a discrete percentage of data reveal a correspondence between trigeminal reflex abnormalities and demyelination problems, therefore the 1<sup>st</sup> loop open will correspond to: '[https://pubmed.ncbi.nlm.nih.gov/?term=trigeminal+reflex+demyelinating+neuropathy+&size=200 Demyelinating neuropathy]<nowiki>''</nowiki> which will return 14 sensitive data. Behind the choice of this key there is an active and dynamic cognitive process of the clinician. From the assertions in the neurological context, a neuropathic pathology was hypothesized in which the demyelinating aspect should also be co<nowiki/><nowiki/>nsidered. | ||
*'''2<sup>st</sup> loop o'''<nowiki/><nowiki/>'''pen:''' The process continues by focusing in ever greater detail on the keywords that correspond to our electrophysiological anomalous result data, i.e. the latency of the jaw jerk. This input corresponding to '[https://pubmed.ncbi.nlm.nih.gov/?term=trigeminal+reflex+demyelinating+neuropathy+latency Latency]' returns 6 sensitive data on which to process a further iteration of the loop. | *'''2<sup>st</sup> loop o'''<nowiki/><nowiki/>'''pen:''' The process continues by focusing in ever greater detail on the keywords that correspond to our electrophysiological anomalous result data, i.e. the latency of the jaw jerk. This input corresponding to '[https://pubmed.ncbi.nlm.nih.gov/?term=trigeminal+reflex+demyelinating+neuropathy+latency Latency]' returns 6 sensitive data on which to process a further iteration of the loop. | ||
*'''3<sup>st</sup> loop open:''' In the statements of the neurological context, an anomaly is also observed in the amplitude of the jaw jerk as well as in latency. This 3<sup>st</sup> open loop corresponding to '[https://pubmed.ncbi.nlm.nih.gov/?term=trigeminal+reflex+demyelinating+neuropathy+latency+amplitude Amplitude]' returns only 2 data points on which to dwell to decide the keyword to reiterate the loop or definitively close the process. The result shows an article describing the electrophysiological evaluation of cranial neuropathies that was considered of low specific weight for our purposes while the other article highlights some trigeminal methodologies to test latency, amplitude of masticatory muscles including H-reflex . | * '''3<sup>st</sup> loop open:''' In the statements of the neurological context, an anomaly is also observed in the amplitude of the jaw jerk as well as in latency. This 3<sup>st</sup> open loop corresponding to '[https://pubmed.ncbi.nlm.nih.gov/?term=trigeminal+reflex+demyelinating+neuropathy+latency+amplitude Amplitude]' returns only 2 data points on which to dwell to decide the keyword to reiterate the loop or definitively close the process. The result shows an article describing the electrophysiological evaluation of cranial neuropathies that was considered of low specific weight for our purposes while the other article highlights some trigeminal methodologies to test latency, amplitude of masticatory muscles including H-reflex . | ||
*'''4<sup>st</sup> loop closed:''' The process, therefore, continues by inserting the algorithmic keyword '[https://pubmed.ncbi.nlm.nih.gov/?term=h+reflex H-reflex] ' which returns 3,701 clinical scientific data. | *'''4<sup>st</sup> loop closed:''' The process, therefore, continues by inserting the algorithmic keyword '[https://pubmed.ncbi.nlm.nih.gov/?term=h+reflex H-reflex] ' which returns 3,701 clinical scientific data. | ||
*'''5<sup>st</sup> loop open:''' The anomalies highlighted were mainly verified on the masseters so it can be deduced that keywords concerning the '[https://pubmed.ncbi.nlm.nih.gov/?term=h+reflex+masseter+muscle&size=200 Masseter muscle]' can be intercepted in the interrogated sample of the 4<sup>st</sup> closed loop, hence the 5<sup>st</sup> open loop which returns 30 data available for the 'CNN' | *'''5<sup>st</sup> loop open:''' The anomalies highlighted were mainly verified on the masseters so it can be deduced that keywords concerning the '[https://pubmed.ncbi.nlm.nih.gov/?term=h+reflex+masseter+muscle&size=200 Masseter muscle]' can be intercepted in the interrogated sample of the 4<sup>st</sup> closed loop, hence the 5<sup>st</sup> open loop which returns 30 data available for the 'CNN' | ||
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*'''8<sup>st</sup> loop closed:''' In this single node the clinician could terminate the loop but would not have solved anything because the decoding of the encrypted message has not yet been achieved. It should be noted that the electrophysiological method called 'heteronomous H-reflex' is able to highlight response anomalies from the temporal muscle for which the loop was continued by inserting the following specific keyword, ' [https://pubmed.ncbi.nlm.nih.gov/?term=Temporal+muscle+abnormal+response&size=200 Temporal muscle abnormal response]' which returns 137 data. | *'''8<sup>st</sup> loop closed:''' In this single node the clinician could terminate the loop but would not have solved anything because the decoding of the encrypted message has not yet been achieved. It should be noted that the electrophysiological method called 'heteronomous H-reflex' is able to highlight response anomalies from the temporal muscle for which the loop was continued by inserting the following specific keyword, ' [https://pubmed.ncbi.nlm.nih.gov/?term=Temporal+muscle+abnormal+response&size=200 Temporal muscle abnormal response]' which returns 137 data. | ||
*'''9<sup>st</sup> loop open:''' By studying the 137 articles appeared in Pubmed, the clinician intuits that the response abnormalities, in the temporal muscle through the H-reflex test, depend on a spread of the stimulus current in the unstructured axon and therefore further investigates the loop by interrogating the network for a further keyword the 'Lateral spread impulses' which, definitively, closes the cognitive process of the 'Neural Network' with a close article, the '[https://pubmed.ncbi.nlm.nih.gov/27072096/ '''Ephaptic transmission''' is the origin of the abnormal muscle response seen in hemifacial spasm]'<center>[[File:Immagine 17-12-22 alle 11.44.jpeg|center|600x600px|thumb|'''Figure 4:''' The active links of the 'CNN' highlighted in the template correspond to the 'Pubmed' database and can be documented.]] | *'''9<sup>st</sup> loop open:''' By studying the 137 articles appeared in Pubmed, the clinician intuits that the response abnormalities, in the temporal muscle through the H-reflex test, depend on a spread of the stimulus current in the unstructured axon and therefore further investigates the loop by interrogating the network for a further keyword the 'Lateral spread impulses' which, definitively, closes the cognitive process of the 'Neural Network' with a close article, the '[https://pubmed.ncbi.nlm.nih.gov/27072096/ '''Ephaptic transmission''' is the origin of the abnormal muscle response seen in hemifacial spasm]'<center>[[File:Immagine 17-12-22 alle 11.44.jpeg|center|600x600px|thumb|'''Figure 4:''' The active links of the 'CNN' highlighted in the template correspond to the 'Pubmed' database and can be documented.]] | ||
<center></center> | <center></center> | ||
== Conclusion == | ==Conclusion== | ||
As demonstrated, the definition of Hemasticatory spasm in our patient Mary Poppins was not a clinically simple process, however, considering the topics presented in the previous chapters of Masticationpedia we have at least three elements of support available: | As demonstrated, the definition of Hemasticatory spasm in our patient Mary Poppins was not a clinically simple process, however, considering the topics presented in the previous chapters of Masticationpedia we have at least three elements of support available: | ||
# A vision of 'Quantum Probability' of physical-chemical phenomena in complex biological systems which will be discussed extensively in the specific chapters. | #A vision of 'Quantum Probability' of physical-chemical phenomena in complex biological systems which will be discussed extensively in the specific chapters. | ||
# A more formal and less vague language than the natural language that directs the diagnostic analysis to the first input node of the 'CNN' through the '<math>\tau</math> Coherence Demarcator' described in the chapter '[[1° Clinical case: Hemimasticatory spasm - en|1st Clinical case: Hemimasticatory spasm]]' | #A more formal and less vague language than the natural language that directs the diagnostic analysis to the first input node of the 'CNN' through the '<math>\tau</math> Coherence Demarcator' described in the chapter '[[1° Clinical case: Hemimasticatory spasm - en|1st Clinical case: Hemimasticatory spasm]]' | ||
# The 'CNN' process which, being managed and guided exclusively by the clinician, becomes an essential means for the definitive diagnosis. | #The 'CNN' process which, being managed and guided exclusively by the clinician, becomes an essential means for the definitive diagnosis. | ||
The 'CNN', in fact, is the result of a profound cognitive process that is performed on each step of the analysis in which, the clinician weighs his intuitions, clarifies his doubts, evaluates the reports, considers the contexts and advances step by step confronting the result of the answer coming from the Pubmed database. Substantially the Pubmed database represents the current level of basic knowledge <math>KB_t</math> at question time and the <math>KB_c</math> in the broadest specialist contexts. | The 'CNN', in fact, is the result of a profound cognitive process that is performed on each step of the analysis in which, the clinician weighs his intuitions, clarifies his doubts, evaluates the reports, considers the contexts and advances step by step confronting the result of the answer coming from the Pubmed database. Substantially the Pubmed database represents the current level of basic knowledge <math>KB_t</math> at question time and the <math>KB_c</math> in the broadest specialist contexts. | ||
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Note that if this chapter had been published in an impacted international scientific journal (Impact Factor) the <math>KB_t</math> and contextually a hypothetical 'machine learning' would have been enriched with a new content or that of the diagnosis of 'Hemimasticatory spasm' defined following the electrophysiological method of the heteronymous H-reflex. This conclusion will come in handy when we repeat the same procedure for other clinical cases in which the <math>KB_t</math> is updated to the database output. | Note that if this chapter had been published in an impacted international scientific journal (Impact Factor) the <math>KB_t</math> and contextually a hypothetical 'machine learning' would have been enriched with a new content or that of the diagnosis of 'Hemimasticatory spasm' defined following the electrophysiological method of the heteronymous H-reflex. This conclusion will come in handy when we repeat the same procedure for other clinical cases in which the <math>KB_t</math> is updated to the database output. | ||
=== Bibliography === | ===Bibliography=== | ||
<references /> | <references /> | ||