Using Networks To Understand Medical Data: The Case of Class III Malocclusions

A system of elements that interact or regulate each other can be represented by a mathematical object called a network. While network analysis has been successfully applied to high-throughput biological systems, less has been done regarding their application in more applied fields of medicine; here we show an application based on standard medical diagnostic data. We apply network analysis to Class III malocclusion, one of the most difficult to understand and treat orofacial anomaly. We hypothesize that different interactions of the skeletal components can contribute to pathological disequilibrium; in order to test this hypothesis, we apply network analysis to 532 Class III young female patients. The topology of the Class III malocclusion obtained by network analysis shows a strong co-occurrence of abnormal skeletal features. The pattern of these occurrences influences the vertical and horizontal balance of disharmony in skeletal form and position. Patients with more unbalanced orthodontic phenotypes show preponderance of the pathological skeletal nodes and minor relevance of adaptive dentoalveolar equilibrating nodes. Furthermore, by applying Power Graphs analysis we identify some functional modules among orthodontic nodes. These modules correspond to groups of tightly inter-related features and presumably constitute the key regulators of plasticity and the sites of unbalance of the growing dentofacial Class III system. The data of the present study show that, in their most basic abstraction level, the orofacial characteristics can be represented as graphs using nodes to represent orthodontic characteristics, and edges to represent their various types of interactions. The applications of this mathematical model could improve the interpretation of the quantitative, patient-specific information, and help to better targeting therapy. Last but not least, the methodology we have applied in analyzing orthodontic features can be applied easily to other fields of the medical science.</p

Complex networks for data-driven medicine: the case of Class III dentoskeletal disharmony

In the last decade, the availability of innovative algorithms derived from complexity theory has inspired the development of highly detailed models in various fields, including physics, biology, ecology, economy, and medicine. Due to the availability of novel and ever more sophisticated diagnostic procedures, all biomedical disciplines face the problem of using the increasing amount of information concerning each patient to improve diagnosis and prevention. In particular, in the discipline of orthodontics the current diagnostic approach based on clinical and radiographic data is problematic due to the complexity of craniofacial features and to the numerous interacting co-dependent skeletal and dentoalveolar components. In this study, we demonstrate the capability of computational methods such as network analysis and module detection to extract organizing principles in 70 patients with excessive mandibular skeletal protrusion with underbite, a condition known in orthodontics as Class III malocclusion. Our results could possibly constitute a template framework for organising the increasing amount of medical data available for patients' diagnosis

The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?

Artificial intelligence (AI) models and procedures hold remarkable predictive efficiency in the medical domain through their ability to discover hidden, non-obvious clinical patterns in data. However, due to the sparsity, noise, and time-dependency of medical data, AI procedures are raising unprecedented issues related to the mismatch between doctors' mentalreasoning and the statistical answers provided by algorithms. Electronic systems can reproduce or even amplify noise hidden in the data, especially when the diagnosis of the subjects in the training data set is inaccurate or incomplete. In this paper we describe the conditions that need to be met for AI instruments to be truly useful in the orthodontic domain. We report some examples of computational procedures that are capable of extracting orthodontic knowledge through ever deeper patient representation. To have confidence in these procedures, orthodontic practitioners should recognize the benefits, shortcomings, and unintended consequences of AI models, as algorithms that learn from human decisions likewise learn mistakes and biases

Geoffroy de Lagasnerie, Logique de la création

Les universitaires ont raison d’être inquiets. La société ignore ce qu’ils sont et ce qu’ils font. Le journalisme véhicule les pires poncifs à leur sujet. Quant à leur institution, elle vient de traverser la pire crise de son histoire et ce n’est pas fini. Plusieurs électrochocs récents, souvent aux allures burlesques, ont secoué la vieille maison qui se voit poussée à la réflexivité : une blessure narcissique, suite à la publication en 2003 du classement dit de Shanghai qui reléguait les éta..

A network approach to orthodontic diagnosis

Background –  Network analysis, a recent advancement in complexity science, enables understanding of the properties of complex biological processes characterized by the interaction, adaptive regulation, and coordination of a large number of participating components. Objective –  We applied network analysis to orthodontics to detect and visualize the most interconnected clinical, radiographic, and functional data pertaining to the orofacial system. Materials and Methods –  The sample consisted of 104 individuals from 7 to 13 years of age in the mixed dentition phase without previous orthodontic intervention. The subjects were divided according to skeletal class; their clinical, radiographic, and functional features were represented as vertices (nodes) and links (edges) connecting them. Results –  Class II subjects exhibited few highly connected orthodontic features (hubs), while Class III patients showed a more compact network structure characterized by strong co-occurrence of normal and abnormal clinical, functional, and radiological features. Restricting our analysis to the highest correlations, we identified critical peculiarities of Class II and Class III malocclusions. Conclusions –  The topology of the dentofacial system obtained by network analysis could allow orthodontists to visually evaluate and anticipate the co-occurrence of auxological anomalies during individual craniofacial growth and possibly localize reactive sites for a therapeutic approach to malocclusion

Complexity and data mining in dental research: A network medicine perspective on interceptive orthodontics

Procedures and models of computerized data analysis are becoming researchers' and practitioners' thinking partners by transforming the reasoning underlying biomedicine. Complexity theory, Network analysis and Artificial Intelligence are already approaching this discipline, intending to provide support for patient's diagnosis, prognosis and treatments. At the same time, due to the sparsity, noisiness and time-dependency of medical data, such procedures are raising many unprecedented problems related to the mismatch between the human mind's reasoning and the outputs of computational models. Thanks to these computational, non-anthropocentric models, a patient's clinical situation can be elucidated in the orthodontic discipline, and the growth outcome can be approximated. However, to have confidence in these procedures, orthodontists should be warned of the related benefits and risks. Here we want to present how these innovative approaches can derive better patients' characterization, also offering a different point of view about patient's classification, prognosis and treatment

Prognostic approach to Class III malocclusion through case‐based reasoning

ObjectiveThis investigation evaluates the evidence of case‐based reasoning (CBR) in providing additional information on the prediction of future Class III craniofacial growth.Settings and sample populationThe craniofacial characteristics of 104 untreated Class III subjects (7‐17 years of age), monitored with two lateral cephalograms obtained during the growth process, were evaluated.Materials and methodsData were compared with the skeletal characteristics of subjects who showed a high degree of skeletal imbalance (‘prototypes’) obtained from a large data set of 1263 Class III cross‐sectional subjects (7‐17 years of age).ResultsThe degree of similarity of longitudinal subjects with the most unbalanced prototypes allowed the identification of subjects who would develop a subsequent unfavourable skeletal growth (accuracy: 81%). The angle between the palatal plane and the sella‐nasion line (PP‐SN angle) and the Wits appraisal were two additional craniofacial features involved in the early prediction of the adverse progression of the Class III skeletal imbalance.ConclusionsCase‐based reasoning methodology, which uses a personalized inference method, may bring additional information to approximate the skeletal progression of Class III malocclusion.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/171230/1/ocr12466.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/171230/2/ocr12466_am.pd