Assessment of an Artificial Intelligence Mandibular Osteotomy Design System: A Retrospective Study.

BACKGROUND: In this study, an AI osteotomy software was developed to design the presurgical plan of mandibular angle osteotomy, which is followed by the comparison between the software-designed presurgical plan and the traditional manual presurgical plan, thus assessing the practicability of applying the AI osteotomy software in clinical practices. METHODS: (1) Develop an AI osteotomy software: design an algorithm based on convolutional neural networks capable of learning feature point and processing clustering segmentation; then, select 2296 cases of successful 3D mandibular angle osteotomy presurgical plans, followed by using those 2296 cases to train the deep learning algorithm; (2) compare the osteotomy presurgical plan of AI osteotomy software and that of manual: first step: randomly selecting 80 cases of typical female head 3D CTs, and designing those 80 cases by means of AI osteotomy software designing (group A) and manually designing (group B), respectively; second step: comparing several indexes of group A and those of group B, including the efficiency index (time from input original CT data to osteotomy presurgical plan output), the safety index (the minimum distance from the osteotomy plane to the mandibular canal), the symmetry indexes (bilateral difference of mandibular angle, mandibular ramus height and mandibular valgus angle) and aesthetic indexes (width ratio between middle and lower faces (M/L), mandibular angle and mandibular valgus angle). RESULTS: The efficiency index: the design time of group A is 1.768 ± 0.768 min and that of group B is 26.108 ± 1.137 min, with P = 0.000; the safety index: The minimum distances from the osteotomy plane to the mandibular canal are 3.908 ± 0.361mm and 3.651 ± 0.437mm, p = 0.117 in groups A and B, respectively; The symmetry indexes: Bilateral differences of mandibular angle are 1.824 ± 1.834° and 1.567 ± 1.059° in groups A and B, respectively, with P = 0.278; bilateral differences of mandibular ramus height are 2.083 ± 1.263 and 2.965 ± 1.433, respectively, with P = 0.119 in groups A and B; Aesthetic indexes: M/L in groups A and B is 1.364 ± 0.074 and 1.371 ± 0.067, respectively, with P = 0.793; mandibular angles in groups A and B are 127.724 ± 5.800° and 127.242 ± 5.545°, respectively, with P = 0.681; Valgus angles in groups A and B are 11.474 ± 5.380 and 9.743 ± 4.620, respectively, with P = 0.273. CONCLUSIONS: With high efficiency, as well as safety, symmetry and aesthetics equivalent to those of a manual design, the AI osteotomy software designing can be used as an alternative method for manual osteotomy designing. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Assessment of the Second Mesiobuccal Root Canal in Maxillary First Molars: A Cone-beam Computed Tomographic Study.

INTRODUCTION: The purpose of this study was to investigate the incidence and location of the second mesiobuccal (MB2) root canal of the maxillary first molar and the relationship between the presence of an MB2 canal and the distribution of canal orifices on the pulpal floor with the aid of cone-beam computed tomographic (CBCT) technology. METHODS: A total of 1008 maxillary first molars (548 patients) were randomly selected and analyzed through CBCT imaging. The association between the incidence of MB2 canals and potential impacting factors including sex, side, age, and the distribution of the main root canal orifices on the pulpal floor was explored. The interorifice distances (ie, the length of a line between the center point of any 2 orifices) at the pulpal floor level were measured using Mimics 10.01 software (ImageWorks, Materialise, Belgium). RESULTS: The majority of 3-rooted maxillary first molars showed 2 root canals (85.4%) in the mesiobuccal root. The incidence of MB2 canals had no statistically significant difference between the left and right sides (P > .05) but had a significant association with the patients' sex and age (P < .05). Receiver operating characteristic curve analysis showed a high diagnostic accuracy (the area under the receiver operating characteristic curve = 0.92) of using the distance ratio of the interorifice distance between the main mesiobuccal and the palatal root canal orifices to the interorifice distance between the distobuccal and the palatal root canal orifices to predict the presence of an MB2 canal. A larger distance ratio (>1.26) indicated a highly probable existence of an MB2 canal. In this study, no molar presented an MB2 canal with a distance ratio of less than 1.16, whereas all molars with a ratio greater than 1.37 presented an MB2 canal without exception. A Bland-Altman scatterplot showed great agreement between the distances of the main mesiobuccal and the distobuccal canal orifices and the second mesiobuccal and the distobuccal canal orifices. CONCLUSIONS: Understanding the incidence of MB2 canals and the distribution pattern of canal orifices on the pulpal floor may help clinicians to quickly identify and locate MB2 canals.

Family and College Environmental Exposures Mediate the Relationship between Parental Education and Depression among College Students.

BACKGROUND: Depression is a major health concern for college students due to its substantial morbidity and mortality. Although low parental education has been identified as a factor in depression in college students, the mechanisms through which parental educational achievement affects students' depression are not well understood. We tested whether adverse family and college environments mediate the relationship between parental educational level and depression among Chinese college students. METHODS: A total of 5180 respondents were selected using a cross-sectional survey. We examined the association of parental education, adverse family and college environments with depression in college students using the Adolescent Self-Rating Life Events Checklist, Beck Depression Inventory and socio-demographic questionnaires. RESULTS: Lower parental educational level is significantly correlated with depression in college students in our sample. Additionally, low family economic status, paternal or maternal unemployment, long periods spent apart from family, family conflicts, having been scolded and beaten by parents, poor or dissatisfying test performance, conflict with friends, heavy course load and failure in selection processes are also associated with parental education. Low family economic status, paternal or maternal unemployment, long periods spent apart from family, family conflicts, poor or dissatisfying test performance, conflict with friends and heavy course load mediated the relationship between parental education and depression in college students. CONCLUSIONS: Adverse family and college environments could explain the influence of parental educational level on depression in college students.

The Role of Family Environment in Depressive Symptoms among University Students: A Large Sample Survey in China.

OBJECTIVE: To explore the relationship between family environment and depressive symptoms and to evaluate the influence of hard and soft family environmental factors on depression levels in a large sample of university students in China. METHODS: A multi-stage stratified sampling procedure was used to select 6,000 participants. The response rate was 88.8%, with 5,329 students completing the Beck Depression Inventory (BDI) and the Family Environment Scale Chinese Version (FES-CV), which was adapted for the Chinese population. Differences between the groups were tested for significance by the Student's t-test; ANOVA was used to test continuous variables. The relationship between soft family environmental factors and BDI were tested by Pearson correlation analysis. Hierarchical linear regression analysis was conducted to model the effects of hard environmental factors and soft environmental factors on depression in university students. RESULTS: A total of 11.8% of students scored above the threshold of moderate depression (BDI≧14). Hard family environmental factors such as parent relationship, family economic status, level of parental literacy and non-intact family structure were associated with depressive symptoms. The soft family environmental factors--conflict and control--were positively associated with depression, while cohesion was negatively related to depressive symptom after controlling for other important associates of depression. Hierarchical regression analysis indicated that the soft family environment correlates more strongly with depression than the hard family environment. CONCLUSIONS: Soft family environmental factors--especially cohesion, conflict and control--appeared to play an important role in the occurrence of depressive symptoms. These findings underline the significance of the family environment as a source of risk factors for depression among university students in China and suggest that family-based interventions and improvement are very important to reduce depression among university students.