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

Abstract

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.

Network analysis of three-dimensional hard-soft tissue relationships in the lower 1/3 of the face: Skeletal Class I-normodivergent malocclusion versus Class II-hyperdivergent malocclusion

“…If the intricate interrelation network of variables cannot be mined su ciently, it is impossible to accurately predict soft tissue morphology. The advantages of network analysis are that it can concentrate on relationships between components, visualize and quantify multiple relationships as a whole, nd out easily overlooked relationships, and re ect rules of complex systems comprehensively [42].…”

“…Network analysis is essentially a deep data mining method for extracting key information from complex systems. Once the network ("graph") is computed, the topological structure of the data (input) is abstracted and encoded in simpler structures, on which the machine learning algorithms can be run [42]. Hence, our work provided a basic data environment for machine learning algorithms to further data mining and prediction by building neural network regression models.…”

Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review

“…Compared to the currently used cone-beam CT, MRI has shown better results in the detection of periodontal structures, soft tissue debris, and unfilled spaces in radicular canals, which is necessary information for endodontic treatment [ 70 , 71 , 72 ]. Additionally, orthodontic treatment is mostly received during childhood, which makes MRI even more desirable [ 73 ]. As mentioned before, overfitting is a challenge for ML algorithms.…”

Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review

“…Moreover, there was a considerable discrepancy among the total number of patients included (sample size) and number of patients who were actually analysed, due to the majority of patients being allocated to a learning group for the purposes of training of the AI software. It is well known that the AI algorithms utilise initial datasets of patient characteristics that serve as a 'learning set' to predict future outcomes (Gili et al, 2021). Therefore, the appropriateness and amount of data used to train AI models can significantly affect the performance and validity of AI models.…”