Risk factors associated with the severity of pericoronitis of mandibular third molar.

PURPOSE: The factors related to pericoronitis severity are unclear, and this study aimed to address this knowledge gap. MATERIALS AND METHODS: In total, 113 patients with pericoronitis were included, and their demographic, clinical, and radiographic characteristics were recorded. The Patient-Clinician Pericoronitis Classification was used to score and categorize the severity of pericoronitis. Statistical analysis was conducted to examine the participants' characteristics, validity of the Patient-Clinician Pericoronitis Classification, and risk factors associated with the severity of pericoronitis. RESULTS: The demographic, clinical, and radiographic characteristics of males and females were similar, except for Winter's classification, pain, and intraoral swelling. The constructive validity of the Patient-Clinician Pericoronitis Classification was confirmed with three latent factors, including infection level, patient discomfort, and social interference. Ordinal logistic multivariate regression analysis revealed that upper respiratory tract infection was the sole risk factor associated with pericoronitis severity in males (odds ratio = 4.838). In females, pericoronitis on the right side (odds ratio = 2.486), distal radiolucency (odds ratio = 5.203), and menstruation (odds ratio = 3.416) were significant risk factors. CONCLUSION: This study demonstrated the constructive validity of the Patient-Clinician Pericoronitis Classification. Among females, pericoronitis in mandibular third molars on the right side with radiolucency in menstruating individuals was more severe. In males, upper respiratory tract infection was the sole risk factor associated with pericoronitis severity. CLINICAL RELEVANCE: Individuals with risk factors should be aware of severe pericoronitis in the coming future.

Framework for Radiation Oncology Department-wide Evaluation and Implementation of Commercial Artificial Intelligence Autocontouring.

PURPOSE: Artificial intelligence (AI)-based autocontouring in radiation oncology has potential benefits such as standardization and time savings. However, commercial AI solutions require careful evaluation before clinical integration. We developed a multidimensional evaluation method to test pretrained AI-based automated contouring solutions across a network of clinics. METHODS AND MATERIALS: Curated data included 121 patient planning computed tomography (CT) scans with a total of 859 clinically approved contours used for treatment from 4 clinics. Regions of interest (ROIs) were generated with 3 commercial AI-based automated contouring software solutions (AI1, AI2, AI3) spanning the following disease sites: brain, head and neck (H&N), thorax, abdomen, and pelvis. Quantitative agreement between AI-generated and clinical contours was measured by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Qualitative assessment was performed by multiple experts scoring blinded AI-contours using a Likert scale. Workflow and usability surveying was also conducted. RESULTS: AI1, AI2, and AI3 contours had high quantitative agreement in 27.8%, 32.8%, and 34.1% of cases (DSC >0.9), performing well in pelvis (median DSC = 0.86/0.88/0.91) and thorax (median DSC = 0.91/0.89/0.91). All 3 solutions had low quantitative agreement in 7.4%, 8.8%, and 6.1% of cases (DSC <0.5), performing worse in brain (median DSC = 0.65/0.78/0.75) and H&N (median DSC = 0.76/0.80/0.81). Qualitatively, AI1 and AI2 contours were acceptable (rated 1-2) with at most minor edits in 70.7% and 74.6% of ROIs (2906 ratings), higher for abdomen (AI1: 79.2%) and thorax (AI2: 90.2%), and lower for H&N (29.0/35.6%). An end-user survey showed strong user preference for full automation and mixed preferences for accuracy versus total number of structures generated. CONCLUSIONS: Our evaluation method provided a comprehensive analysis of both quantitative and qualitative measures of commercially available pretrained AI autocontouring algorithms. The evaluation framework served as a roadmap for clinical integration that aligned with user workflow preference.