A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration.

In the field of orthodontics, providing patients with accurate treatment time estimates is of utmost importance. As orthodontic practices continue to evolve and embrace new advancements, incorporating machine learning (ML) methods becomes increasingly valuable in improving orthodontic diagnosis and treatment planning. This study aimed to develop a novel ML model capable of predicting the orthodontic treatment duration based on essential pre-treatment variables. Patients who completed comprehensive orthodontic treatment at the Indiana University School of Dentistry were included in this retrospective study. Fifty-seven pre-treatment variables were collected and used to train and test nine different ML models. The performance of each model was assessed using descriptive statistics, intraclass correlation coefficients, and one-way analysis of variance tests. Random Forest, Lasso, and Elastic Net were found to be the most accurate, with a mean absolute error of 7.27 months in predicting treatment duration. Extraction decision, COVID, intermaxillary relationship, lower incisor position, and additional appliances were identified as important predictors of treatment duration. Overall, this study demonstrates the potential of ML in predicting orthodontic treatment duration using pre-treatment variables.

Can we predict orthodontic extraction patterns by using machine learning?

OBJECTIVE: To investigate the utility of machine learning (ML) in accurately predicting orthodontic extraction patterns in a heterogeneous population. MATERIALS AND METHODS: The material of this retrospective study consisted of records of 366 patients treated with orthodontic extractions. The dataset was randomly split into training (70%) and test sets (30%) and was stratified according to race/ethnicity and gender. Fifty-five cephalometric and demographic input data were used to train and test multiple ML algorithms. The extraction patterns were labelled according to the previous treatment plan. Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms were used to predict the patient's extraction patterns. RESULTS: The highest class accuracy percentages were obtained for the upper and lower 1st premolars (U/L4s) (RF: 81.63%, LR: 63.27%, SVM: 63.27%) and upper 1st premolars only (U4s) extraction patterns (RF: 61.11%, LR: 72.22%, SVM: 72.22%). However, all methods revealed low class accuracy rates (<50%) for the upper 1st and lower 2nd premolars (U4/L5s), upper 2nd and lower 1st premolars (U5/L4s), and upper and lower 2nd premolars (U/L5s) extraction patterns. For the overall accuracy, RF yielded the highest percentage with 54.55%, followed by SVM with 52.73% and LR with 49.09%. CONCLUSION: All tested supervised ML techniques yielded good accuracy in predicting U/L4s and U4s extraction patterns. However, they predicted poorly for the U4/L5s, U5/L4s, and U/L5s extraction patterns. Molar relationship, mandibular crowding, and overjet were found to be the most predictive indicators for determining extraction patterns.

Levels of Cadmium in Human Mandibular Bone.

Cadmium (Cd) is an environmental toxicant that accumulates in bone and alters bone turnover and metabolism. Periodontal disease is characterized by tooth loss and tissue destruction, specifically, loss of supporting bone around the teeth. We have previously shown that Cd causes loss of dental alveolar (tooth supporting) bone in a rodent model of long-term Cd poisoning. The overall goal of this study was to determine the possible association between levels of Cd in alveolar bone and evidence of periodontal disease in human cadavers. The extent of Cd accumulation in human mandible samples was analyzed. Levels of Cd in mandibular alveolar bone were compared to those in basal bone as well as the renal cortex in samples obtained from the cadavers. Alveolar bone contained significantly higher levels of Cd when compared to basal bone (p < 0.01). Cd levels in mandibular bone were significantly higher in female compared to male cadavers (p < 0.05). The kidney cortex had greater than 15-fold higher Cd levels compared to mandible bone. Additional analyses showed a possible association between levels of Cd in basal bone and the presence of periodontal disease in cadavers from which the samples were obtained. This study shows that Cd accumulates to relatively high levels within alveolar bone as compared to basal bone in the mandible and thus may have a significant and direct effect in the progression of changes in bone associated with periodontal disease.

Cadmium Exposure Disrupts Periodontal Bone in Experimental Animals: Implications for Periodontal Disease in Humans.

Cadmium (Cd) is an environmental contaminant that damages the kidney, the liver, and bones. Some epidemiological studies showed associations between Cd exposure and periodontal disease. The purpose of this study was to examine the relationship between Cd exposure and periodontal disease in experimental animals. Male Sprague/Dawley rats were given daily subcutaneous injections of Cd (0.6 mg/kg/day) for up to 12 weeks. The animals were euthanized, and their mandibles and maxillae were evaluated for levels of periodontal bone by measuring the distance from the cementoenamel junction (CEJ) to the alveolar bone crest (ABC) of the molar roots. After 12 weeks of Cd exposure in animals, there was a significantly greater distance between the CEJ and ABC in the palatal aspect of the maxillary molars and the lingual aspect of the mandibular molars when compared with controls (p < 0.0001). This study shows that Cd has significant, time-dependent effects on periodontal bone in an animal model of Cd exposure. These findings support the possibility of Cd being a contributing factor to the development of periodontal disease in humans.