Prevalence and Clinical Significance of Incidental Findings in the Maxillofacial Complex of Adolescent Orthodontic Patients: A Retrospective Cone Beam Computed Tomography Analysis.

OBJECTIVES: The aim of this study was to determine the prevalence and severity of incidental findings in the maxillofacial complex of orthodontic patients imaged with cone beam computed tomography (CBCT) and assign those findings an appropriate clinical significance. METHODOLOGY: Incidental findings (IF) were identified in 250 CBCT scans of adolescent orthodontic patients (aged 13-18 years) with a large field-of-view and categorized based on their anatomic location and placed into one of six subgroups based on anatomic region: i) sino-nasal, ii) dentoalveolar, iii) nasooropharyngeal airway, iv) temporomandibular joint, v) neck, vi) calcifications, and vi) miscellaneous findings. Additionally, findings were assigned a clinical significance score based on severity on a scale of mild, moderate and severe. Mild IF was defined as an IF that does not require any further investigation or referral. Moderate IF was defined as an IF that has the tendency to become clinically significant and should be observed periodically. IFs that warrant further investigation and/or intervention were designated as severe. RESULTS: The percentage of IFs in sino-nasal and dento-alveolar regions were 44.7% and 19.1% respectively. The percentage of IFs with mild, moderate, and severe clinical significance were 27%, 72%, and 1%, respectively. Out of the IFs involving calcifications, 80.8% were stylohyoid calcifications and <1% were cranial cavity IFs such as petroclinoid calcifications and falx cerebri calcifications. Among the sino-nasal findings, 1.2% were identified as severe. CONCLUSION: The sino-nasal region had the highest frequency of IFs. Understanding the prevalence of incidental findings and its clinical relevance is important for clinicians to allow for appropriate monitoring and timely treatment of patients.

Machine learning from clinical data sets of a contemporary decision for orthodontic tooth extraction.

OBJECTIVE: To examine the robustness of the published machine learning models in the prediction of extraction vs non-extraction for a diverse US sample population seen by multiple providers. SETTING AND SAMPLE POPULATION: Diverse group of 838 patients (208 extraction, 630 non-extraction) were consecutively enrolled. MATERIALS AND METHODS: Two sets of input features (117 and 22) including clinical and cephalometric variables were identified based on previous studies. Random forest (RF) and multilayer perception (MLP) models were trained using these feature sets on the sample population and evaluated using measures including accuracy (ACC) and balanced accuracy (BA). A technique to identify incongruent data was used to explore underlying characteristics of the data set and split all samples into 2 groups (G1 and G2) for further model training. RESULTS: Performance of the models (75%-79% ACC and 72%-76% BA) on the total sample population was lower than in previous research. Models were retrained and evaluated using G1 and G2 separately, and individual group MLP models yielded improved accuracy for G1 (96% ACC and 94% BA) and G2 (88% ACC and 85% BA). RF feature ranking showed differences between top features for G1 (maxillary crowding, mandibular crowding and L1-NB) and G2 (age, mandibular crowding and lower lip to E-plane). CONCLUSIONS: An incongruent data pattern exists in a consecutively enrolled patient population. Future work with incongruent data segregation and advanced artificial intelligence algorithms is needed to improve the generalization ability to make it ready to support clinical decision-making.

Capture-Tag-Release: A Strategy for Small Molecule Labeling of Native Enzymes.

A strategy for labeling native enzymes in a manner that preserves their activity is reported: capture-tag-release (CTR). Key to this approach is the small molecule CTR probe that contains an enzyme inhibitor, benzophenone crosslinker, and aryl phosphine ester. After UV-derived capture of the enzyme, addition of an azide-containing tag triggers a Staudinger ligation that labels the enzyme. A further consequence of the Staudinger ligation is fragmentation of the CTR probe, thus releasing the inhibitor and restoring enzymatic activity. As a proof-of-principle, the CTR strategy was applied to the hydrolase ß-galactosidase. The enzyme was efficiently labeled with biotin, and the kinetic data for the biotinylated enzyme were comparable to those for unlabeled ß-galactosidase. The CTR probe exhibits excellent targeting specificity, as it selectively labeled ß-galactosidase in a complex protein mixture.