Facial dysmorphology in children exposed in pregnancy to anticonvulsant medications correlates with deficits in IQ.

Some children exposed at conception to the antiepileptic drugs (AEDs) phenytoin (PHT), phenobarbital (PB), and carbamazepine (CBZ) have changes in their midface and fingers. It has been suggested that the anticonvulsant-exposed child with these subtle changes in facial features (the "anticonvulsant face") has a greater likelihood of having deficits in IQ in comparison with children exposed to the same anticonvulsants who do not have these features. 115 AED-exposed children (40, PHT; 34, PB; and 41, CBZ) between 6.5 and 16 years of age and 111 unexposed children matched by sex, race, and year in school were evaluated. The evaluations were (WISC-III), physical examination with measurements of facial features and digits and photographs. The AED-exposed children had cephalometric radiographs, but not the unexposed. Each parent had a similar examination of face and hands plus tests of intelligence. These AED-exposed children showed an increased frequency of a short nose and anteverted nares, features of the "anticonvulsant face." Lateral skull radiographs showed a decrease in the angle between the anterior cranial base and nasal bone, which produces anteverted nares. Mean IQs were significantly lower on one or more IQ measures for the children with these facial features. Shortening of the distal phalanges and small fingernails correlated with the presence of a short nose in that child. The findings in 115 children exposed at conception to either phenytoin, phenobarbital, or carbamazepine, as monotherapy, confirmed the hypothesis that those with a short nose and anteverted nares had a lower IQ than exposed children without those features.

In utero nicotine exposure affects murine palate development.

OBJECTIVES: Despite data linking smoking to increased risk of fetal morbidity and mortality, 11% of pregnant women continue to smoke or use alternative nicotine products. Studies confirm that nicotine exposure during pregnancy increases the incidence of birth defects; however, little research has focused on specific anatomic areas based on timing of exposure. We aim to determine critical in utero and postnatal periods of nicotine exposure that affect craniofacial development, specifically palate growth. Malformation of the palatal structures can result in numerous complications including facial growth disturbance, or impeding airway function. We hypothesized that both in utero and postnatal nicotine exposure will alter palate development. MATERIALS AND METHODS: We administered pregnant C57BL6 mice water supplemented with 100 µg/mL nicotine during early pregnancy, throughout pregnancy, during pregnancy and lactation, or lactation only. Postnatal day 15 pups underwent micro-computed tomography (µCT) analyses specific to the palate. RESULTS: Resultant pups revealed significant differences in body weight from lactation-only nicotine exposure, and µCT investigation revealed several dimensions affected by lactation-only nicotine exposure, including palate width, palate and cranial base lengths, and mid-palatal suture width. CONCLUSIONS: These results demonstrate the direct effects of nicotine on the developing palate beyond simple tobacco use. Nicotine exposure through tobacco alternatives, cessation methods, and electronic nicotine delivery systems (ENDS) may disrupt normal growth and development of the palate during development and the postnatal periods of breastfeeding. Due to the recent dramatic increase in the use of ENDS, future research will focus specifically on this nicotine delivery method.

Accuracy of artificial intelligence-assisted growth prediction in skeletal Class I preadolescent patients using serial lateral cephalograms for a 2-year growth interval.

OBJECTIVE: To investigate the accuracy of artificial intelligence-assisted growth prediction using a convolutional neural network (CNN) algorithm and longitudinal lateral cephalograms (Lat-cephs). MATERIALS AND METHODS: A total of 198 Japanese preadolescent children, who had skeletal Class I malocclusion and whose Lat-cephs were available at age 8 years (T0) and 10 years (T1), were allocated into the training, validation, and test phases (n = 161, n = 17, n = 20). Orthodontists and the CNN model identified 28 hard-tissue landmarks (HTL) and 19 soft-tissue landmarks (STL). The mean prediction error values were defined as 'excellent,' 'very good,' 'good,' 'acceptable,' and 'unsatisfactory' (criteria: 0.5 mm, 1.0 mm, 1.5 mm, and 2.0 mm, respectively). The degree of accurate prediction percentage (APP) was defined as 'very high,' 'high,' 'medium,' and 'low' (criteria: 90%, 70%, and 50%, respectively) according to the percentage of subjects that showed the error range within 1.5 mm. RESULTS: All HTLs showed acceptable-to-excellent mean PE values, while the STLs Pog', Gn', and Me' showed unsatisfactory values, and the rest showed good-to-acceptable values. Regarding the degree of APP, HTLs Ba, ramus posterior, Pm, Pog, B-point, Me, and mandibular first molar root apex exhibited low APPs. The STLs labrale superius, lower embrasure, lower lip, point of lower profile, B', Pog,' Gn' and Me' also exhibited low APPs. The remainder of HTLs and STLs showed medium-to-very high APPs. CONCLUSION: Despite the possibility of using the CNN model to predict growth, further studies are needed to improve the prediction accuracy in HTLs and STLs of the chin area.

Comparison of semi and fully automated artificial intelligence driven softwares and manual system for cephalometric analysis.

BACKGROUND: Cephalometric analysis has been used as one of the main tools for orthodontic diagnosis and treatment planning. The analysis can be performed manually on acetate tracing sheets, digitally by manual selection of landmarks or by recently introduced Artificial Intelligence (AI)-driven tools or softwares that automatically detect landmarks and analyze them. The use of AI-driven tools is expected to avoid errors and make it less time consuming with effective evaluation and high reproducibility. OBJECTIVE: To conduct intra- and inter-group comparisons of the accuracy and reliability of cephalometric tracing and evaluation done manually and with AI-driven tools that is WebCeph and CephX softwares. METHODS: Digital and manual tracing of lateral cephalometric radiographs of 54 patients was done. 18 cephalometric parameters were assessed on each radiograph by 3 methods, manual method and by using semi (WebCeph) and fully automatic softwares (Ceph X). Each parameter was assessed by two investigators using these three methods. SPSS was then used to assess the differences in values of cephalometric variables between investigators, between softwares, between human investigator means and software means. ICC and paired T test were used for intra-group comparisons while ANOVA and post-hoc were used for inter-group comparisons. RESULTS: Twelve out of eighteen variables had high intra-group correlation and significant ICC p-values, 5 variables had relatively lower values and only one variable (SNO) had significantly low ICC value. Fifteen out of eighteen variables had minimal detection error using fully-automatic method of cephalometric analysis. Only three variables had lowest detection error using semi-automatic method of cephalometric analysis. Inter-group comparison revealed significant difference between three methods for eight variables; Witts, NLA, SNGoGn, Y-Axis, Jaraback, SNO, MMA and McNamara to Point A. CONCLUSION: There is a lack of significant difference among manual, semiautomatic and fully automatic methods of cephalometric tracing and analysis in terms of the variables measured by these methods. The mean detection errors were the highest for manual analysis and lowest for fully automatic method. Hence the fully automatic AI software has the most reproducible and accurate results.

Lateral cephalometric parameters among Arab skeletal classes II and III patients and applying machine learning models.

BACKGROUND: The World Health Organization considers malocclusion one of the most essential oral health problems. This disease influences various aspects of patients' health and well-being. Therefore, making it easier and more accurate to understand and diagnose patients with skeletal malocclusions is necessary. OBJECTIVES: The main aim of this research was the establishment of machine learning models to correctly classify individual Arab patients, being citizens of Israel, as skeletal class II or III. Secondary outcomes of the study included comparing cephalometric parameters between patients with skeletal class II and III and between age and gender-specific subgroups, an analysis of the correlation of various cephalometric variables, and principal component analysis in skeletal class diagnosis. METHODS: This quantitative, observational study is based on data from the Orthodontic Center, Jatt, Israel. The experimental data consisted of the coded records of 502 Arab patients diagnosed as Class II or III according to the Calculated_ANB. This parameter was defined as the difference between the measured ANB angle and the individualized ANB of Panagiotidis and Witt. In this observational study, we focused on the primary aim, i.e., the establishment of machine learning models for the correct classification of skeletal class II and III in a group of Arab orthodontic patients. For this purpose, various ML models and input data was tested after identifying the most relevant parameters by conducting a principal component analysis. As secondary outcomes this study compared the cephalometric parameters and analyzed their correlations between skeletal class II and III as well as between gender and age specific subgroups. RESULTS: Comparison of the two groups demonstrated significant differences between skeletal class II and class III patients. This was shown for the parameters NL-NSL angle, PFH/AFH ratio, SNA angle, SNB angle, SN-Ba angle. SN-Pg angle, and ML-NSL angle in skeletal class III patients, and for S-N (mm) in skeletal class II patients. In skeletal class II and skeletal class III patients, the results showed that the Calculated_ANB correlated well with many other cephalometric parameters. With the help of the Principal Component Analysis (PCA), it was possible to explain about 71% of the variation between the first two PCs. Finally, applying the stepwise forward Machine Learning models, it could be demonstrated that the model works only with the parameters Wits appraisal and SNB angle was able to predict the allocation of patients to either skeletal class II or III with an accuracy of 0.95, compared to a value of 0.99 when all parameters were used ("general model"). CONCLUSION: There is a significant relationship between many cephalometric parameters within the different groups of gender and age. This study highlights the high accuracy and power of Wits appraisal and the SNB angle in evaluating the classification of orthodontic malocclusion.

Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs.

PURPOSE: The purpose of the present clinical study was to compare the Ricketts and Steiner cephalometric analysis obtained by two experienced orthodontists and artificial intelligence (AI)-based software program and measure the orthodontist variability. MATERIALS AND METHODS: A total of 50 lateral cephalometric radiographs from 50 patients were obtained. Two groups were created depending on the operator performing the cephalometric analysis: orthodontists (Orthod group) and an AI software program (AI group). In the Orthod group, two independent experienced orthodontists performed the measurements by performing a manual identification of the cephalometric landmarks and a software program (NemoCeph; Nemotec) to calculate the measurements. In the AI group, an AI software program (CephX; ORCA Dental AI) was selected for both the automatic landmark identification and cephalometric measurements. The Ricketts and Steiner cephalometric analyses were assessed in both groups including a total of 24 measurements. The Shapiro-Wilk test showed that the data was normally distributed. The t-test was used to analyze the data (α = 0.05). RESULTS: The t-test analysis showed significant measurement discrepancies between the Orthod and AI group in seven of the 24 cephalometric parameters tested, namely the corpus length (p = 0.003), mandibular arc (p < 0.001), lower face height (p = 0.005), overjet (p = 0.019), and overbite (p = 0.022) in the Ricketts cephalometric analysis and occlusal to SN (p = 0.002) and GoGn-SN (p < 0.001) in the Steiner cephalometric analysis. The intraclass correlation coefficient (ICC) between both orthodontists of the Orthod group for each cephalometric measurement was calculated. CONCLUSIONS: Significant discrepancies were found in seven of the 24 cephalometric measurements tested between the orthodontists and the AI-based program assessed. The intra-operator reliability analysis showed reproducible measurements between both orthodontists, except for the corpus length measurement. CLINICAL SIGNIFICANCE: The artificial intelligence software program tested has the potential to automatically obtain cephalometric analysis using lateral cephalometric radiographs; however, additional studies are needed to further evaluate the accuracy of this AI-based system.

Evaluation of mandibular growth in a prepubertal Class I and Class II population: a longitudinal analysis.

OBJECTIVE: This study aims to identify the presence, timing, and magnitude of a prepubertal mandibular growth spurt in a Class I and Class II population. METHODS: From the Burlington and Iowa Growth study of the AAOF Craniofacial Growth Legacy Collection, 83 Class I subjects (37 females and 46 males) and 32 Class II subjects (18 males and 14 females) were identified, as having at least seven consecutive annual lateral cephalograms taken from 5 to 11 years of age. Only subjects with a normodivergent facial pattern were considered. A customized cephalometric analysis was performed, and total mandibular length, defined as the distance between Condylion (Co) and Gnathion (Gn), was calculated. RESULTS: Overall, a significant early peak of mandibular growth was present in all the subjects analysed both in Class I (4.69 mm for males and 4.18 mm for females; P < .05) and in Class II (5.85 mm for males and 4.05 mm for females; P < .05). No differences between males and females were found for the timing of this peak (7 years for Class I and Class II females and 7 years for Class I and 6.5 years for Class II males). In males, a significantly larger peak was observed in Class II than Class I subjects (P = .007). LIMITATIONS: The main limitations of this study were the impossibility of using a suitable growth indicator to identify the timing of the early mandibular growth peak and the limited Class II records retrievable. CONCLUSION: This investigation suggests that a prepubertal mandibular growth peak is consistently present in both Class I and Class II males and females of clinically significant magnitude. Despite that, chronological age confirms to be unsuitable to identify this peak.

Development and validation of predictive models for skeletal malocclusion classification using airway and cephalometric landmarks.

OBJECTIVE: This study aimed to develop a deep learning model to predict skeletal malocclusions with an acceptable level of accuracy using airway and cephalometric landmark values obtained from analyzing different CBCT images. BACKGROUND: In orthodontics, multitudinous studies have reported the correlation between orthodontic treatment and changes in the anatomy as well as the functioning of the airway. Typically, the values obtained from various measurements of cephalometric landmarks are used to determine skeletal class based on the interpretation an orthodontist experiences, which sometimes may not be accurate. METHODS: Samples of skeletal anatomical data were retrospectively obtained and recorded in Digital Imaging and Communications in Medicine (DICOM) file format. The DICOM files were used to reconstruct 3D models using 3DSlicer (slicer.org) by thresholding airway regions to build up 3D polygon models of airway regions for each sample. The 3D models were measured for different landmarks that included measurements across the nasopharynx, the oropharynx, and the hypopharynx. Male and female subjects were combined as one data set to develop supervised learning models. These measurements were utilized to build 7 artificial intelligence-based supervised learning models. RESULTS: The supervised learning model with the best accuracy was Random Forest, with a value of 0.74. All the other models were lower in terms of their accuracy. The recall scores for Class I, II, and III malocclusions were 0.71, 0.69, and 0.77, respectively, which represented the total number of actual positive cases predicted correctly, making the sensitivity of the model high. CONCLUSION: In this study, it is observed that the Random Forest model was the most accurate model for predicting the skeletal malocclusion based on various airway and cephalometric landmarks.

Evaluation of the soft tissue facial profile in different skeletal malocclusions in relation to age.

BACKGROUND: The aim of the study was to assess the thickness of the soft tissue facial profile (STFP) in relation to the skeletal malocclusion, age and gender. METHODS: All patients, aged 7-35 years, who were seeking orthodontic treatment at the Department of Orthodontics, Medical University of Warsaw between 2019 and 22 were included in the study. All patients had lateral head radiographs taken before the treatment. The cephalometric analysis was performed including the STFP analysis. The patients were allocated to one of six groups based on age and skeletal relations (ANB angle). The minimum number of patients in each group was 60 with equal gender distribution. The STFP analysis included ten linear measurements. RESULTS: A total of 300 patients were included in the study and allocated to five groups. Group 6 (growing patients with skeletal Class III malocclusion) was not included in the study as it failed to achieve the assumed group size. There were significant differences in the thickness of the STFP in relation to the skeletal malocclusions. Adults with skeletal Class III malocclusion had significantly thicker subnasal soft tissues compared to patients with skeletal Class I and Class II malocclusions. The thickness of the lower lip in patients with Class II skeletal malocclusion was significantly bigger compared to the other groups. Children and adolescents with Class II malocclusions had thicker lower lip in comparison to the group with Class I malocclusion. The majority of the STFP measurements were significantly smaller in children and adolescents compared to adults. The thickness of the STFP in males was significantly bigger in all age groups compared to females. CONCLUSIONS: The thickness of facial soft tissues depends on the patient's age and gender. The degree of compensation of the skeletal malocclusion in the STFP may be a decisive factor during orthodontic treatment planning regarding a surgical approach or a camouflage treatment of skeletal defects.

Genetic Insights into Skeletal Malocclusion: The Role of the FBN3 rs7351083 SNP in the Romanian Population.

Background and Objectives: irregularities in the growth and development of the jawbones can lead to misalignments of maxillary and mandibular structures, a complex condition known as skeletal malocclusion, one of the most common oral health problems. Skeletal malocclusions, particularly Class II and Class III, can significantly affect facial appearance, chewing efficiency, speech, and overall oral health, often requiring orthodontic treatment or surgery to correct. These dentofacial anomalies are influenced by genetic and environmental factors and exhibit diverse phenotypic expressions. Materials and Methods: in this study, we investigated the correlation between the rs7351083 SNP of the FBN3 gene that encodes a member of the fibrillin protein family and malocclusion risk in a group of 57 patients from Romania. Results: the results shed light on the relationship between the selected genetic marker and the investigated dentofacial disorder, revealing a positive association between the reference allele (A) and Class II and that the alternate allele (G) is associated with Class III. Conclusions: cephalometric analysis revealed no significant differences among genotypes, suggesting that while genetic factors are implicated in malocclusion, they may not directly affect cephalometric parameters or that the sample size was too small to detect these differences. The discovery of an A > T transversion in one individual with a Class II deformity underscores the genetic diversity within the population and the necessity of comprehensive genotyping to uncover rare genetic variants that might influence craniofacial development and the risk of malocclusion. This study highlights the need for larger studies to confirm these preliminary associations.

Dental and skeletal maturation of Chinese male children with unilateral cleft lip and palate.

The aim of this study was to determine whether the relationship between dental age (DA), cervical stage (CS) and chronological age (CA) in Chinese male children with unilateral cleft lip and palate (UCLP) is similar to that of children without clefts. Panoramic and cephalometric radiographs of 105 male UCLP patients, aged from 8 to 16 years, were collected and compared to 210 age-matched healthy control males. The Demirjian and cervical vertebral maturation (CVM) methods were used to visually examine the radiographs and Spearman's correlation analysis was used to identify differences between the two groups with regards to CS, DA and CA. There was a significant positive correlation between DA and CA in both groups and the mean CA-DA difference was significantly higher in children with UCLP when compared to controls (0.319 vs. 0.003, p < 0.05). A significant delay in tooth development was detected in UCLP children from 10 to 12 years-of-age. Both the UCLP and control groups showed high correlations between CS and DA. Calcification stage D appeared only before CS3; however, from CS5 to 6, all teeth have almost completed their maturation phase. Chinese male UCLP patients are likely to experience delayed tooth development compared to healthy controls, especially during the fast-growing period. Evaluating the stages of tooth mineralization could represent a rapid method to assess growth potential.

LOW CERTAINTY OF EVIDENCE SUPPORTS THE APPLICATION OF (AI) FOR THE AUTOMATIC DETECTION OF CEPHALOMETRIC LANDMARKS WITH PROSPECTS FOR IMPROVEMENTS.

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis. J Digit Imaging. 2023 Jun;36(3):1158-1179. doi:10.1007/s10278-022-00766-w. SOURCE OF FUNDING: The study was financed in part by the Coordenacao de Aperfeicoamentode Pessoal de Nivel Superior-Brazil (CAPES)-Finance Code 001. TYPE OF STUDY/DESIGN: Systematic review and meta-analysis.

The role of sphenoid bone in basilar invagination pathophysiology.

Basilar invagination (BI) is characterized by rostral dislocation of the cervical spine toward the skull base. The craniometrics of the skull base have shown significant differences among craniocervical junction malformations. The sphenoid bone is the center of the skull base; however, no study has evaluated this bone in cases of BI. This was a cross-sectional study of MRI databanks from two institutions of the author's practice between 1985 and 2020. The craniometrics of the sphenoid bone were measured in BI patients and controls. Fifty-eight MRIs were selected, including 28 BI patients and 30 controls. The mean sphenoid crest-clivus length was 32.66 ± 4.7 mm in the BI group and 29.98 ± 3.0 mm in the control group (p = 0.01). The mean sphenoid planum-top of Dorsum sellae length was 28.53 ± 3.7 mm in the BI group and 26.45 ± 3.2 mm in the control group (p = 0.02). The mean tuberculum sellae-sphenoid floor height was 18.52 ± 4.4 mm in the BI group and 21.32 ± 2.9 mm in the control group (p = 0.00). The mean sella turcica-sphenoid floor height was 10.35 ± 3.8 mm in the BI group and 12.24 ± 3.5 mm in the control group (p = 0.05). The mean clivus length was 29.81 ± 6.3 mm in the BI group and 40.86 ± 4.2 mm in the control group (p = 0.00). The mean sphenoid length was 58.34 ± 7.4 mm in the BI group and 67.31 ± 6.0 mm in the control group (p = 0.00). The mean sphenoid angle was 116.33 ± 8.7° in the BI group and 112.36 ± 6.9° in the control group (p = 0.05). The BI sphenoid bone has shorter vertical dimensions and longer horizontal measures. This morphology promotes a flattening of the sphenoid angle. The sphenoid bone is significantly altered in BI, favoring the congenital hypothesis in the pathophysiology of this disease.

Effects of jaw movement in bimaxillary orthognathic surgery on the upper airway: Computational fluid dynamics analysis.

Maxillomandibular repositioning in orthognathic surgeries has both morphologic and functional effects. These surgeries are thought to change the pharyngeal space and cause obstructive sleep apnoea syndrome, however. The primary purpose of this study is to evaluate the effects of jaw movement in bimaxillary orthognathic surgery on airway function and to identify the morphometric factors that can predict postoperative airway function. The subjects were 11 males and 12 females who had undergone orthognathic surgeries of the maxilla and mandible. The results of cephalometric analysis, cross-sectional area of the pharynx (CSA), pharyngeal volume and computational fluid dynamics (CFD) were compared. The CSA of the nasal (CSA1), total volume and total nasal volume decreased after surgery with statistical significance. Velocity at the oropharyngeal space (V2) increased after surgery with statistical significance. V2, CSA of the oropharyngeal space (CSA2) and PV were correlated with the horizontal posterior movement of point B, point Menton and overjet. V2 and CSA2 were correlated with SNB before and after surgery in all 46 analyses. Changes in pharyngeal airflow were more affected by pressure drop in the pharyngeal space (ΔPp) than by pressure drop in the nasal space (ΔPn). The relationship between the actual amount of change in the cephalometric reference point and the airway function is evident. CFD may thus be very useful as morphological analysis in preoperative treatment decision making.

Evaluation of the accuracy of fully automatic cephalometric analysis software with artificial intelligence algorithm.

OBJECTIVE: The aim of this study is to evaluate whether fully automatic cephalometric analysis software with artificial intelligence algorithms is as accurate as non-automated cephalometric analysis software for clinical diagnosis and research. MATERIALS AND METHODS: This is a retrospective archive study using lateral cephalometric radiographs taken from individuals aged 12-20 years. Cephalometric measurement data were obtained from these lateral cephalometric radiographs by manual landmark marking with non-automated computer software (Dolphin 11.8). Again, the same radiographs were made using fully automatic digital cephalometric analysis software OrthoDx™ (AI-Powered Orthodontic Imaging System, Phimentum) and WebCeph (Assemblecircle, Seoul, Korea) with artificial intelligence algorithm, without manual intervention of the researcher and fully automatic markings and measurements were made by the software. RESULTS: According to the consistency test, a statistically significant good level of consistency was found between Dolphin and OrthoDx™ measurements and Dolphin and WebCeph measurements in angular measurements (ICC > 0.75, P < .01, ICC > 0.75, P < 0, respectively. 01). A weak level of consistency was found in linear measurement and soft tissue parameters in both software (ICC < 0.50, P < .05, ICC < 0.50, P < .05), and the difference between measurements was statistically found to be different from "0." CONCLUSION: The results obtained from fully automatic cephalometric analysis software with artificial intelligence algorithms are similar to the results of non-automated cephalometric analysis software, although there are differences in some parameters. To minimize the margin of error in artificial intelligence-based fully automatic cephalometric software, the manual intervention of the observer is needed.

Evaluation of optimal anterior-posterior position of upper incisors through ANS point: a retrospective study on a Chinese sample.

OBJECTIVES: This study was designed to determine the optimal anterior-posterior (AP) position of upper incisors through Anterior Nasal Spine (ANS) point. MATERIALS AND METHODS: Lateral cephalometric radiographic images of 690 patients were collected and divided into a derivation group and a validation group, and the former were subdivided into a proper AP position (PAP) group and an improper AP position (iPAP) group. The distance from facia-axis (FA) point of upper incisors to the line perpendicular to Frankfort horizontal (FH) plane through ANS (FA-ANS) was measured, and the relationship between FA-ANS and several cephalometric indices were studied through Pearson correlation analysis. Receiver operating characteristic (ROC) curves for different clinical indices were analyzed to evaluate the diagnostic efficiency of optimal AP position of upper incisors. RESULTS: The average value of FA-ANS in PAP group was 0.57±1.99, which was significantly different from FA-ANS in iPAP group. Cephalometric indices such as U1-NA, U1-SN, AB-NPo, UL-TVL, Wits, and ANB were found to be correlated with FA-ANS. The receiver operating characteristic (ROC) curves represented a greater diagnostic efficiency of FA-ANS compared with other clinical indices. CONCLUSIONS: ANS point, as a stable skeletal landmark, could be used to access an optimal AP position of upper incisors, providing aids to clinical diagnosis and treatment goal determination for clinical practice. CLINICAL RELEVANCE: A new index FA-ANS, together with other traditional indices, could help determine the optimal position of upper incisors and provide a personalized therapeutic plan.

Does cleft lip and palate affect the severity of malocclusion?

OBJECTIVE: The purpose of this study was to evaluate the 3D anatomical features of unilateral (UCLP) and bilateral (BCLP) complete cleft lip and palate with those of skeletal Class III dentofacial deformities. MATERIALS AND METHODS: In total, 92 patients were divided into cleft and noncleft groups. The cleft group comprised 29 patients with UCLP and 17 patients with BCLP. The noncleft group comprised 46 patients with Class III dentofacial deformities. 3D anatomical landmarks were identified and the corresponding measurements were made on the cone-beam computed tomography (CBCT). RESULTS: The differences between the affected and unaffected sides of the patients with UCLP were nonsignificant. The differences between the patients with UCLP and BCLP were nonsignificant except for the SNA angle. Significant differences between the patients with clefts and Class III malocclusion were identified for the SNA, A-N perpendicular, and A-N Pog line, indicating that the maxillae of the patients in the cleft group were more retrognathic and micrognathic. Relative to the noncleft group patients, the cleft group patients had a significantly smaller ramus height. CONCLUSION: The affected and unaffected sides of the patients with UCLP did not exhibit significant differences. The maxillae of the patients with UCLP were significantly more retrognathic than those of the patients with BCLP. The maxillae and mandibles of the patients in the cleft group were more micrognathic and retropositioned relative to those of the noncleft Class III patients. CLINICAL RELEVANCE: The maxillary and mandibular findings indicated greater deficiencies in the patients with UCLP or BCLP than in those with skeletal Class III malocclusion. Appropriate surgical design should be administered.

Retrospective investigation of the 3D effects of the Carriere Motion 3D appliance using model and cephalometric superimposition.

OBJECTIVES: Carriere Motion 3D™ appliance (CMA) represents a method for molar distalization and correction of class II malocclusion. The aim was to investigate the 3D effects of the CMA by superimposing digital models and cephalometric X-rays. MATERIALS AND METHODS: We retrospectively examined 16 patients treated with CMA in combination with class II elastics. We compared digitized models and cephalometric X-rays of records taken before therapy and after the removal of CMA. The records were superimposed to assess the skeletal and dentoalveolar changes. The results of the cephalometric X-ray analysis were compared to an untreated age- and gender-matched sample. RESULTS: Class II occlusion was corrected after 11.85 ± 4.70 months by 3.45 ± 2.33 mm. The average distalization of the upper first molars was 0.96 ± 0.80 mm. The analysis of the cephalometric X-rays confirmed a distalization of the upper first molars with distal tipping and revealed a mesialization of the lower first molars of 1.91 ± 1.72 mm. Importantly, CMA resulted in a mild correction of the skeletal class II relationship (ANB: - 0.71 ± 0.77°; Wits: - 1.99 ± 1.74 mm) and a protrusion of the lower incisors (2.94 ± 2.52°). Compared to the untreated control group, there was significant distalization of the upper first molars and canines with mesialization and extrusion of the lower first molars. CONCLUSION AND CLINICAL RELEVANCE: CMA is an efficient method for treating class II malocclusions. However, the class II correction is only partially caused by a distalization of the upper molars.

Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis.

Using computer vision through artificial intelligence (AI) is one of the main technological advances in dentistry. However, the existing literature on the practical application of AI for detecting cephalometric landmarks of orthodontic interest in digital images is heterogeneous, and there is no consensus regarding accuracy and precision. Thus, this review evaluated the use of artificial intelligence for detecting cephalometric landmarks in digital imaging examinations and compared it to manual annotation of landmarks. An electronic search was performed in nine databases to find studies that analyzed the detection of cephalometric landmarks in digital imaging examinations with AI and manual landmarking. Two reviewers selected the studies, extracted the data, and assessed the risk of bias using QUADAS-2. Random-effects meta-analyses determined the agreement and precision of AI compared to manual detection at a 95% confidence interval. The electronic search located 7410 studies, of which 40 were included. Only three studies presented a low risk of bias for all domains evaluated. The meta-analysis showed AI agreement rates of 79% (95% CI: 76-82%, I2 = 99%) and 90% (95% CI: 87-92%, I2 = 99%) for the thresholds of 2 and 3 mm, respectively, with a mean divergence of 2.05 (95% CI: 1.41-2.69, I2 = 10%) compared to manual landmarking. The menton cephalometric landmark showed the lowest divergence between both methods (SMD, 1.17; 95% CI, 0.82; 1.53; I2 = 0%). Based on very low certainty of evidence, the application of AI was promising for automatically detecting cephalometric landmarks, but further studies should focus on testing its strength and validity in different samples.

Evaluation of AI Model for Cephalometric Landmark Classification (TG Dental).

The accuracy of cephalometric landmark identification for malocclusion classification is essential for diagnosis and treatment planning. Identifying these landmarks is often complex and time-consuming for orthodontists. An AI model for classification was recently developed. This model was investigated based on current regulatory considerations as a result of the strict regulations on software systems and the lack of information on artificial intelligence (AI) requirements in this publication. The platform developed by the ITU/WHO for AI is used to assess the models of the application. The auditing procedure assessed the development process concerning medical device regulations, data protection regulations, and ethical considerations. Upon that, the major tasks during the development were evaluated, such as qualification, annotation procedure, and data set attributes. The AI models were investigated under consideration of technical, clinical, regulatory, and ethical considerations. The risk to the patient and user's health can be considered low according to the International Medical Device Regulators Forum (IMDRF) definition. This application facilitates the decision and planning of malocclusion treatment based on lateral cephalograms without cephalometric landmarks. It is comparable with common standards in orthodontic diagnosis.