Computer-aided heuristics in orthodontics

Introduction: During the decision-making process, physicians rely on heuristics that consists of simple, useful procedures for solving problems, intuitive short-cuts that produce reliable decisions based on limited information. In clinical situations characterized by a high degree of uncertainty such as orthodontics, cognitive biases and judgment errors related to heuristics are not uncommon. The aim of this study is to promote trust in the effective interface between the intuitive reasoning of the orthodontic practitioner and the computational heuristics emerging from simple statistical models. Methods: We propose an integrative model based on the interaction between clinical reasoning and two computational tools, cluster analysis and Fast-and-Frugal Trees, with the aim to extract a structured craniofacial representation of untreated Class III subjects and to forecast the worsening of the malocclusion over time. Results: Cluster analysis of cephalometric values from 144 growing Class III subjects followed longitudinally (T1: mean age 10.2+-1.9 years; T2: mean age 13.8 ± 2.7 years) produced three morphological subgroups with predominant sagittal, vertical, and slight maxillomandibular imbalances. Fast-and-Frugal Trees applied to different subgroups extracted heuristics that improved the prediction of key features associated to adverse craniofacial growth. Conclusions: Provided that cephalometric values are placed in the appropriate framework, the matching between simple and fast computational approaches and clinical reasoning could help the intuitive logic, perception, and cognitive inferences of orthodontic practitioners on the outcome of patients affected by Class III disharmony, decreasing errors associated with flawed judgments and improving the accuracy of decision making.