Increasing evidence shows that children with Communication Disorders (CDs) may show gross, fine, and visual-motor difficulties compared to children with typical development. Accordingly, the present study aims to characterize gross, fine and visual-motor skills in children with CDs, distinguishing children with CDs into three subgroups, i.e., with Language Disorders (LD), Speech Sound Disorders (SSD), and LD + SSD. In Experiment 1, around 60% of children with CDs  showed clinical/borderline scores in balance skills, regardless of the type of communication deficit. However, children with LD, SSD, and LD + SSD did not differ in gross and fine motor skills. In Experiment 2, a higher percentage of children with CDs  obtained clinical/borderline scores in Visual Perception skills. Moreover, children with LD + SSD performed significantly worsen in Visual Perception and Fine Motor Coordination skills compared to children with SSD only. Our results underlined that CDs are generally associated with gross motor difficulties and that visual-motor difficulties are related to the type of communication deficit. Paying earlier attention to the motor skills of children with CDs could help clinicians design effective interventions. Language development in childhood is usually associated with a variety of cognitive functions, such as working memory ,2,3, audNeuroimaging findings have also documented the intertwining of motor and language skills in the left motor and premotor areas, including the primary motor cortex , the venCDs are common neurodevelopmental disorders, with an estimated prevalence of around 8% , charactOther CDs are the Childhood-Onset Fluency Disorder (Stuttering), the Social (Pragmatic) Communication Disorder, and the Unspecified Communication Disorder. Hereafter, we will mention CDs referring to LD, SSD, and LD + SSD only.Epidemiological studies documented a prevalence of LD around 3.3% (aged 3 to 17 years)  and a prIn children with CDs, it has been shown the presence of both fine motor deficits in activities that require muscles of the fingers, and gross motor deficits, in activities involving whole-body movement .Among fine motor deficits, reduced performance was found in tasks that required planning and sequencing, such as grasping and building towers with blocks.Specifically, a study aimed to test whether 32 children with SSD aged from 2 to 5 years had impairment in fine motor performance, assessed using the Peabody Developmental Motor Scales . FindingSimilarly, visual-motor skills were found to be compromised in children with LD or SSD. For example, a study found that visual-motor integration, Visual Perception, and Fine Motor Coordination skills, assessed by the Developmental Test of Visual-Motor Integration (VMI) , were maAmong gross motor deficits, it has been demonstrated that out of 125 children, 51% obtained borderline or clinical scores on the Movement Assessment Battery for Children , used toAlthough the growing knowledge of the intertwined impairments of motor and language skills in children with CDs, a few methodological problems need to be addressed. First, the motor skills of children with CDs have so far been investigated in most studies in small groups ,33,35, aSecond, in the considered studies, the relationship between language and motor aspects has been documented in groups of children with a heterogeneous and wide age range , and this may have influenced previous results. In fact, age is a factor that affects the development of motor skills. Accordingly, the motor skills of a 2-year-old child are different from those expected at 6 years of age. Furthermore, it should be considered that as children grow, the demands of the environment  gradually increase, and motor skills become an important means by which children interact with the external environment. Deficits in this area can, therefore, differently impact children’s functioning, including also the development of language, depending on their age .Starting from these considerations, the present study aims to investigate whether children with CDs, with a narrow age range (from 4 to 7 years) and distinguished into subgroups according to the type of communication deficit, have different motor impairments. In particular, we compared subgroups of children with LD, SSD, and LD + SSD in gross, fine, and visual-motor abilities.The cohort was retrospectively selected through a comprehensive database made of several hundred outpatients assessed at the Child and Adolescent Neuropsychiatry Unit of the Bambino Gesù Children’s Hospital .The clinical diagnosis of CDs  was made according to the Diagnostic and Statistical Manual of Mental Disorders, 5th ed. (DSM-5) criteria  based onTo evaluate cognitive level, the brief-IQ scales from the Leiter International Performance Scale—Third Edition (Leiter-3) —were admFor both Experiment 1 and 2, participants’ anonymity and data confidentiality were ensured. All procedures performed in the study involving human participants were in accordance with the 1964 Declaration of Helsinki and its subsequent amendments or comparable ethical standards.The cohort was composed of 147 Italian-speaker children , including 21 with LD, 36 with SSD, and 90 with the combined disorders.Criteria for inclusion in the study were the followings: (1) Clinical diagnosis of CDs  was used to assess gross and fine motor skills . This baAfter clinical eligibility assessment, the selected cohort was composed of 244 Italian-speaker children (F/M = 63/181) divided into three subgroups see : 34 withProcedures, as well as inclusion criteria in the study, were the same as in Experiment 1. The Beery–Buktenica Developmental Test of Visual Motor Integration (VMI) was used to assess visual-motor skills . This te2).In Experiment 1 and Experiment 2, differences among the three subgroups  on chronological age and nvIQ were tested separately by two Analyses of Variance (ANOVAs), while difference in gender was verified by means of a Chi-square  were applied to account for the comparisons of subgroups.In Experiment 1, subgroups with CDs (LD vs SSD vs LD + SSD) were compared on the MABC-2 components  using the Kruskal–Wallis ANOVA because the assumption of normality was not fulfilled. Multiple Comparisons 2.Children with LD, SSD, or LD + SSD were further classified as children with clinical/borderline scores  or children with no clinical scores (when standard scores were above 6). The percentage of children with clinical/borderline scores was calculated for each MABC-2 component  in overall sample, regardless of the subgroups with CDs . Difference in the distribution of Manual Dexterity, Aiming and Catching, and The Balance within each subgroup was verified by mean of a χp values (2-tailed) were applied to account for the comparisons of subgroups.In Experiment 2, subgroups with CDs (LD vs SSD vs LD + SSD) were compared on the VMI components  using the Kruskal–Wallis ANOVA because the assumption of normality was not fulfilled. Multiple Comparisons 2.Children with LD, SSD, or LD + SSD were further classified as children with clinical/borderline scores when composite scores on VMI were equal or below 85 or children with no clinical scores when standard scores were above 85. The percentage of children with clinical/borderline scores was calculated for each VMI component  in overall sample, regardless of the subgroups with CDs . Difference in the distribution of Visual Motor Integration, Visual Perception, and Fine Motor Coordination within each subgroup was verified by means of a χp value < 0.05 was considered statistically significant.For all analyses, a rho) were performed separately for LD, SSD, and LD + SSD on significant results identified by the Kruskal–Wallis ANOVA. Bonferroni’s correction was applied for multiple comparisons.In order to evaluate a potential relation between individual factors  and motor skills, Spearman’s rank correlations , gender  and for nvIQ .The three subgroups with CDs did not differ for chronological age , Manual Dexterity (p = 0.28), Aiming and Catching (p = 0.43), and Balance (p = 0.14).As shown in 22 = 3.18, p = 0.20), Aiming and Catching  and Balance .The percentage of children with clinical/borderline scores for each MABC-2 component, regardless of the subgroups with CDs , was 53% in Manual Dexterity, 47.6% in Aiming and Catching, and 62.6% in Balance components. No significant difference emerged across the three subgroups in the distribution of children with clinical/borderline scores see  in Manua2, 241 = 2.05, p = 0.13), gender  and for nvIQ .The three subgroups did not differ for chronological age . However, a significant difference emerged in the Visual Perception component among the three subgroups (p = 0.0015). Post hoc documented significantly worse scores in LD + SSD than in SSD (p = 0.004).As shown in p = 0.041). Comparisons among subgroups revealed that participants with LD + SSD showed significantly worse scores in the Fine Motor Coordination component than the subgroup with only SSD (p = 0.039).Moreover, a significant difference emerged in the Fine Motor Coordination component among the three groups  as well as in Motor Coordination component . However, a significant difference in the distribution of children with clinical/borderline scores emerged across the three subgroups in the Visual Perception component .The percentage of children with clinical/borderline scores for each VMI component, regardless of the subgroups with CDs , was 16% in Visual Motor Integration, 31.6% in Visual Perception, and 28.7% in Fine Motor Coordination components. Specifically, the distribution of children with clinical/borderline scores across the three subgroups did not differ in Visual Motor Integration , Visual Perception , and the Motor Coordination components  whereby as nvIQ increased, greater scores in VMI components were observed. No significant correlations between age and Visual Motor Integration , Visual Perception , and Motor Coordination components  were observed.In the LD group, significant correlations were found between nvIQ and Visual Motor Integration , whereby as nvIQ increased, greater scores in the VMI component were observed. No significant correlations between nvIQ and Visual Perception  and Motor Coordination component  were documented. In addition, no significant correlations between age and Visual Motor Integration , Visual Perception , and Motor Coordination component  emerged.In the SSD group, significant correlations were found between nvIQ and Visual Motor Integration , Visual Perception , whereby as nvIQ increased, greater scores in VMI components were observed. No significant correlations between nvIQ and the Motor Coordination component . Moreover, a significant correlation was found between age and the Visual Perception component . However, no significant correlations were found between age and Visual Motor Integration  and Motor Coordination components .In the LD + SSD group, significant correlations were found between nvIQ and Visual Motor Integration (p 0.05/18 = 0.002), only the correlation between nvIQ and Visual Perception  survived.After Bonferroni’s correction . Therefore, 1:2 children with CDs—regardless of the type of deficits—may show gross and/or fine motor difficulties.Our results are consistent with previous studies in which children with CDs tend to perform poorly on motor tasks showing difficulties in stepping, running, stair climbing, and finger gait ,43. FurtOf interest, when considering gross motor skills, we observed that children with LD, SSD, and LD + SSD did not differ in MABC-2, generally obtaining clinical/borderline scores (≤6). This result further underlined that gross motor impairment is not specific to a subgroup of CDs but this aspect is compromised in children with CDs.From a neurobiological perspective, the comorbidity between CDs and gross motor deficits could be the result of shared motor and language neuroanatomical networks . AccordiConsidering neuropsychological processes, motor and cognitive-linguistic deficits could be the result of a common procedural learning deficit ,49,50. TWhen considering fine and visual-motor skills, we observed that children with LD + SSD performed lower in the Visual Perception and Fine Motor Coordination components of VMI than the group with SSD alone, but not compared with the group with LD alone. Consistent with our findings, it was documented that children with comorbid LD + SSD are more impaired compared to children with SSD alone in terms of discrimination and Fine Motor Coordination  .Evidence demonstrated that perceptual and behavioral judgments pertaining to the auditory domain could be influenced by visual information. Specifically, auditory perceptual performance can be enhanced when the auditory information is paired with spatially and temporally coincident visual information. Conversely, pairing visual information that differs from auditory information in certain stimulus characteristics such as space, time, and semantics often results in the perception of multisensory illusions . MoreoveThe results of the combined research suggest that there is significant facilitation of auditory comprehension and learning in the presence of salient visual cues, as well as an improvement in speech production ,58. The However, it is worth considering that nvIQ was found to be positively correlated with performance in the LD + SSD group . Therefore, although our groups did not differ significantly in demographic variables, some aspects  may still have affected the results previously discussed and need to be taken into consideration for the next studies.The co-occurrence of motor problems and language impairment may have important consequences for a child’s academic performance ,28. MoreCurrently, several studies have supported the idea that rehabilitation based on audiovisual training may be the future of therapeutic interventions for individuals with CDs ,63,64,65In line with the suggestions of the American Academy of Pediatrics Council on Children With Disabilities, overall results encourage screening children with CDs also for motor difficulties  and viceOverall, our results underlined that CDs are generally associated with gross motor difficulties and that visual-motor difficulties are related to the type of communication deficit. Thus, early identification of motor deficits and the specific characteristics of subgroups of children with CDs might help the clinicians to provide cues for effective interventions, suggesting proposing motor rehabilitation such as psychomotricity. This is a very important finding considering that many children with CDs will later develop a specific learning disorder, such as in writing.Future research is needed to better investigate the role of motor skills in the development of CDs in order to identify the most appropriate therapy and to improve the quality of life of children and their families.


Automatic tooth segmentation of a digital dental model using deep learning showed high segmentation success rate, accuracy, and efficiency; thus, it can be used for orthodontic diagnosis and appliance fabrication.This study evaluates the accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning. We developed a dynamic graph convolutional neural network (DGCNN)-based algorithm for automatic tooth segmentation and classification using 516 digital dental models. We segmented 30 digital dental models using three methods for comparison: (1) automatic tooth segmentation (AS) using the DGCNN-based algorithm from LaonSetup software, (2) landmark-based tooth segmentation (LS) using OrthoAnalyzer software, and (3) tooth designation and segmentation (DS) using Autolign software. We evaluated the segmentation success rate, mesiodistal (MD) width, clinical crown height (CCH), and segmentation time. For the AS, LS, and DS, the tooth segmentation success rates were 97.26%, 97.14%, and 87.86%, respectively ( The first step of successful orthodontic treatment is accurate diagnosis and establishing an optimised treatment plan. An orthodontist collects various types of patient data, such as clinical examinations, radiographs, intra- and extra-oral clinical photographs, and dental models, prepares a problem list, sets treatment goals, and establishes orthodontic treatment plans accordingly. Several treatment options are typically available depending on patients’ individual needs, even with similar orthodontic conditions. In this situation, a diagnostic model setup showing expected treatment outcomes helps patients and orthodontists make decisions.1 with a digital model provides information on changes in tooth movement, angle, and arch form by superimposition, which helps determine various treatment options. Moreover, studies have found no significant difference in reliability when compared with a manual setup2. At present, digital orthodontic solutions, such as computer-aided-design/computer-aided manufacturing (CAD/CAM) software and intra- and extra-oral scanners, are used by orthodontists to diagnose and fabricate orthodontic appliances4.Manual setup using plaster models requires considerable time and effort in an orthodontic lab. Furthermore, comparing the various options using plaster models is difficult and cannot be used for all treatment cases. In contrast, a virtual setup6. Hou et al.7 reported that using a digital setup changed treatment plans in 23.6% of diagnostic cases, and the reliability of the treatment plan selected was enhanced, especially in difficult cases and cases in which residents made the decision with little clinical experience. Many orthodontists routinely use virtual setups for various digital orthodontic procedures, such as clear aligners8, individual jigs for indirect bonding9, custom brackets, and arch wires10.Diagnostic setup aids in extraction, interproximal reduction, anchorage preparation, and treatment mechanics, enabling precise treatment plansRepresentative commercial orthodontic CAD/CAM software, such as OrthoAnalyzer , SureSmile , OrthoStudio , OrthoCAD,  ClinCheck, and Autolign , contain functions for segmentation of digital tooth models, three-dimensional (3D) tooth movement, virtual setup, and orthodontic device production. The early versions of orthodontic CAD/CAM software were less efficient because they required considerable manual intervention. Since then, the algorithms have improved, increasing the accuracy and efficiency of tooth segmentation, classification, and automation levels.11, and the arch perimeters of the virtual setup of the digital model tended to be shorter than those of a manual setup with a plaster model12. This is because the plaster model can easily reproduce the proximal surface of the tooth by trimming after segmentation, whereas the digital model obtains a hollow image based on surface data; thus, reproducing the shape of the proximal surface that cannot be scanned is difficult.Accurate tooth segmentation is essential for model setup. The tooth segmentation methods used in digital models may affect the model analysis and setup as the shape of the proximal surface and mesiodistal (MD) width recorded may vary depending on the method. The MD width of each tooth in a digital setup model was shown to be shorter than the conventional plaster setup model in most cases13. Region-based methods include k-means clustering, random walk, fitting primitives, and fast marching watershed, which distinguish the mesh using the similarity of surrounding regions but have difficulties in defining tooth models with variable shapes as semantic regions. Feature curve-based methods include the snake evolution method, morphological skeleton extraction, and plan-view range image; among these, the snake evolution method is considered the most popular method for tooth segmentation14. It is, however, sensitive to curvature noise and has poor reliability when the scanned data are poor13. The specificity of the tooth shape, the tight line between teeth and gingiva, and the close contact between adjacent teeth make automatic and precise tooth segmentation difficult15.Several studies have introduced their own methods for 3D object segmentation. Various deep learning techniques have been developed to segment 3D objects. General segmentation methods can be classified into region-based and feature curve-based methods18 that work directly with raw point clouds are active research subjects19. Dynamic graph convolutional neural networks (DGCNNs) have also been applied to segment 3D objects, which can be further used for the segmentation and classification of digital dental models20. DGCNNs improve segmentation performance by combining edge convolution based on PointNet16, a deep learning model using point clouds, which are the basic units for reproducing 3D objects. This technique can be utilized in tooth segmentation and classification.Recently, various deep learning techniques have been developed to segment 3D objects, and point cloud deep learning modelsThe purpose of this study is to compare and evaluate the accuracy and efficiency of the DGCNN-based segmentation and classification of digital dental models with two existing commercially available software programs. Herein, ‘the three different tooth segmentation methods of the digital tooth model do not significantly differ in the success rate, time, and tooth size’ is set as the null hypothesis.p-value. Table We analysed the reliability of measurements and found that the intraclass correlation coefficients (ICCs) for the intra-rater reliability were 0.987–0.997 Table . In the p < 0.001, post hoc test: LS, AS > DS).The success rates for automatic segmentation were 97.26% and 97.14% in the AS and LS, respectively, which were higher than the 87.86% success rate of the DS Table . These rp < 0.001, post hoc test: DS > AS > LS). The CCH means (95% CI) were 7.65 , 7.52 , and 7.58  mm in the LS, DS, and AS, respectively. CCH was significantly higher in the LS than the DS and the AS . The means (95% CI) of segmentation times were 424.17 , 150.73 , and 57.73  s in the LS, DS, and AS, respectively. We found a significant difference in the segmentation times of the three groups .Furthermore, we compared the MD width, height, and segmentation time of the three segmentation types Table . The meap < 0.001, post hoc: DS > AS > LS in upper incisal, lower incisal, lower canine, and lower premolar; DS, AS > LS in upper canine, upper and lower molar, DS > LS, AS in upper premolar). The means (95% CI) of CCH error ranged from −0.03  to 0.00  mm, −0.21  to −0.09  mm, and −0.11  to −0.06  mm in the LS, DS, and AS groups, respectively. There were statistically significant differences in all tooth groups .We compared the tooth size error by tooth group Table . The meap < 0.05) (Table p < 0.001) depending on the software used, and the post hoc test showed that DS (−0.019) > AS (−0.052) > LS (−0.121).We observed significant effects of tooth group, software, MD width/CCH, and first-order interactions of Tooth Group * Software, Tooth Group * MD width/CCH, and Software * MD width/CCH on the tooth size errors in the generalised linear mixed model (GLMM) analysis (5) Table . Results23, automated cephalometric landmarking25, diagnosis of impaction26, determination of skeletal maturity for growth stage evaluation27, and automatic segmentation and setup of digital models28. Reducing the time and effort required for simple tasks for diagnosis and appliance fabrication allows the users to focus more on making decisions.In orthodontics, artificial intelligence, including deep learning, can be applied to diagnosis and treatment planning for orthodontic extractions or orthognathic surgeryIn this study, we verified the MD width and CCH, presented as continuous variables, for intra-rater reliability using ICC. The success and failure of segmentation, presented as nominal variables using Cohen's kappa, indicated very high evaluation reproducibility: ICC and Cohen’s kappa were 0.987–0.997 and 0.885–1.000, respectively. In addition, this study showed statistically significant differences in segmentation success rate, time, and size of segmented teeth using three different orthodontic CAD/CAM programs; thereby, rejecting the null hypothesis.We designed the DGCNN-based segmentation model in two stages to prevent degradation of the segmentation performance due to differences in number of vertices between tooth and gingiva. In the first stage, the digital dental model was segmented into gingiva and dentition using the two-class DGCNN model. In the second stage, the digital dental model was segmented into individual tooth and gingiva using the seventeen-class DGCNN model after adjusting the number of gingiva vertices, which were segmented in the first stage, not to exceed twice the number of individual tooth vertices.DGCNN using point clouds is advantageous for semantic segmentation and classification of a digital dental model, but it suffers from poor resolution for tooth margins. We attempted to obtain a clear tooth margin by supplementary use of curve-based mesh segmentation using skeleton/pruning algorithm. However, in some cases, the closed loop may not be formed due to the unclear curvature of the scanned data, or a closed loop may be formed in the wrong area, such as the tooth groove. Therefore, the segmentation and classification by DGCNN was optimized by supplementing the curvature-based mesh segmentation.To compare and evaluate the accuracy and efficiency of the DGCNN-based segmentation model, we used two existing commercially available software. Both software packages used for comparison  were selected for the following reasons: the first reason is popularity; both are popular with orthodontists. Second, we considered their functionality for the tooth segmentation; OrthoAnalyzer is characterized by the need to set precise MD points for tooth segmentation, and Autolign has the need to set approximate MD points. Third, we considered their versatility. Some orthodontic software packages create closed working environments that prevent exportation of segmented teeth. OrthoAnalyzer and Autolign can export segmented teeth as stereolithography files, which can be imported to Meshmixer and Geomagic Control X software for success/failure determination and tooth-size measurement.This study presented the digital dental model segmentation success and failure for its clinical applications. As the purpose of tooth segmentation is to diagnose and fabricate orthodontic appliances, such as custom brackets, clear aligners, and indirect bonds, accurate tooth surface models are essential to fabricate orthodontic appliances, and defects cannot be allowed. Considering the width and height of the bracket base and the undercut needed to obtain the retention of the removable application, the cervical ± 25% line was set as the success baseline. Therefore, criteria for determining whether the segmentation was successful or not include the cervical margin of the segmented tooth not deviating beyond ± 25% of the cervical margin of the actual tooth and finding no defects in the occlusal or incisal edge of the segmented tooth.A high segmentation success rate increases user convenience by reducing the time and effort required to modify segmentation splines. This study showed high segmentation success rate Table  in all tThe MD width of the experimental group used the results provided by each software program. Consequently, results varied depending on different measurement and calculation methods. OrthoAnalyzer, used in the LS, set the mesial and distal points of individual teeth before tooth segmentation and calculated the MD width using the virtual plane formed by the screen view at the MD point setting. In contrast, Autolign and LaonSetup used in the DS and the AS, respectively, were normalised after tooth segmentation to calculate the MD width. Even if the software user sets the MD points precisely on an unsegmented dental model, they are impossible to set in the occlusion area of the interproximal region. Owing to the location characteristics of the measurement points, the MD width measured before tooth segmentation is likely to be measured more conservatively than when measured on the segmented teeth. In this study, the MD width of LS also showed an error of −0.35 mm and −0.23 mm when compared with DS and AS, respectively.The MD widths of upper molars were recorded as larger in DS and AS groups than in the LS group. This may be because of the characteristic shape of the upper molars and the method of MD width measurement. An upper molar often forms a parallelogram in occlusal view, in contrast to other teeth whose height of contour on the MD surface is clear. When an upper molar is in the shape of a parallelogram, the heights of contour in mesial and distal surfaces show many differences in the bucco-lingual position. Without considering these morphological features, the MD width of a normalised upper molar can cause errors in measurement, leading to inaccurately large sizes. Therefore, the MD width measurements of upper molars require corrections.14 proposed an image reconstruction method for an adjacent occlusion using a generative adversarial network and obtained an average improvement of 0.004 mm compared with the conventional method.A limitation of digital models is that measuring accurate MD width is difficult because of the presence of occlusions in the interproximal area. When using a plaster model, the adjacent surface is reproduced naturally during the teeth section, but in the case of a digital model with only surface information, the occlusions in contact with the adjacent area during segmentation remain empty after segmentation. To solve this problem, Kim et al.CCH can affect vertical bracket positioning and thus requires evaluation of accuracy. Compared with the DS and AS, LS had fewer errors in the whole tooth group, and DS and AS produced shorter CCHs than reference group (REF). However, according to the CCH of each tooth group Table , the maxThis study performed repeated measurement of digital dental models conducted with three different segmentation methods, and there were within-subject correlations that were not independent. In addition, segmentation failure resulted in a missing value because the MD width and CCH could not be measured. Therefore, we used GLMM as a statistical method to analyse the main effects and the first-order interactions of the MD width and CCH errors. The segmentation success rate of the DS was lower than that of the remainder of the group, but the mean error according to the segmentation method was the lowest in the DS. This was the result of evaluating successful teeth segmentation and excluding missing values due to failure of segmentation. In addition, the mean error value results between groups of post hoc tests were within 0.12 mm and thus clinically acceptable.The mean segmentation time of the AS was 57.73 s, which was shorter than those of the DS and the LS, with means of 150.73 and 424.17 s, respectively, presenting significant differences in efficiency. This was attributed to the differences in the segmentation processes; in all three experimental groups, the digital model was orientated to the coordinate system, but the subsequent process differed for each group. In the case of LS, which required precise marking of the mesial and distal points of all teeth, the segmentation time was the longest due to the necessity of axes specification of each tooth. Similarly, the DS also required marking of mesial and distal points for all teeth but did not require precise marking, so the time required for point designation was short. In the case of AS, segmentation and classification were performed without manual intervention after orientation; thus, it took the shortest time for segmentation. As the convenience of using segmentation increases and the requirement of manual intervention decreases, it is important to automate segmentation and reduce the time and effort required for correction by decreasing the segmentation failure rate.A limitation of this study is that we used digital models of permanent dentition in good condition without teeth and gingival defects. Therefore, it is not possible to determine the tooth segmentation ability in cases such as a missing tooth, severe wearing, dental caries, partial eruption, and third molar. In subsequent studies, it will be necessary to use digital dental models of various conditions.Because the OrthoAnalyzer software used in LS was used to obtain the reference data, bias may have occurred. To reduce the potential bias, the splines of all segmented teeth in the REF method were corrected. Moreover, the MD width of REF was measured using Geomagic Control X software. There was no correction after segmentation across experimental groups , and raw MD width data provided by software were used. Therefore, depending on the segmentation accuracy and the method of calculating MD width, values may differ from REF.This study used different automatic tooth segmentation software in different groups. Because the three software programs were not developed and distributed simultaneously, their performances may vary depending on the software version. Moreover, the software continues to be updated to new versions; thus, improvements in accuracy, convenience, and speed of segmentation and classification can be expected. In addition, because the CAD/CAM software for orthodontics used in this study was distributed for commercial use, the detailed algorithms were not disclosed, making a direct comparison of the segmentation methods impossible. Therefore, this study focused on comparing the software from the point of view of a user and compared the use of the software and results of tooth segmentations.This retrospective study was approved by Yonsei University Dental Hospital Institutional Review Board (IRB No. 2-2021-0033) and passed the exemption review of informed consent on the use of patients’ intraoral scan data. All clinical examinations were conducted in accordance with the Declaration of Helsinki.Among the 1005 digital dental model sets of patients treated by the Department of Orthodontics at Yonsei University Dental Hospital between January 2010 and February 2019, we selected 546 digital dental models satisfying the following criteria.Over 14-year-old orthodontic patients with second molars eruption.Digital dental models without any defects in the teeth and gingiva.Mild and moderate crowding.The inclusion criteria of the digital models were as follows:Congenital tooth deformity.Severe dental caries and tooth wear.Congenital or acquired missing tooth.Supernumerary tooth.Severe crowding.The exclusion criteria of the digital models were as follows:We used 516 dental models for deep learning-based tooth segmentation training, and the 30 dental models to evaluate the accuracy and efficiency of segmentation and classification of digital tooth models based on DGCNN model using two commercially available software programs.20 as shown in Fig. We performed segmentation and classification of digital dental models based on DGCNN29. We established the tooth margin, that showed a negative value as compared to the mean curvature of the tooth, as a feature vertex, and formed a closed loop by applying the skeleton pruning algorithm30. We obtained segmentation results with clear margins by merging the teeth and gingiva segmented by the DGCNN model and the teeth segmented by a mesh curvature closed loop.As a post-processing process to determine precise tooth proximal and cervical margin, we divided the interdental area based on the mesh curvatureThirty digital dental models were used in the experiment. As 28 segmented teeth were evaluated per digital dental model, 840 teeth were evaluated in each of the four methods. With an observed sample size of n = 30 per group, power analysis of variance (two-tailed) conducted a posteriori using G*Power software version 3.1.9.2  indicated > 99% power was needed to detect a medium effect size (Cohen’s d = 0.25) at a significance level of 0.05.A schematic diagram for this study is shown in Fig. To evaluate the accuracy and efficiency of automatic segmentation using the three types of software, we measured the MD width and CCH of segmented teeth, segmentation time, and success rate. We used a total of 3, 360 teeth, 28 teeth from 30 identical digital models, each segmented in three experimental groups and one control group, for evaluation. Orthodontic specialist (J.I.) measured ten randomly selected digital models twice within a two-week interval to confirm the reliability of the measurements.We extracted the segmented teeth of the three experimental groups  and the control group (REF) as individual teeth using Meshmixer  and imported into the Geomagic Control X software , where we superimposed the corresponding teeth using the best fit method. Overlapped teeth were imported into Meshmixer and rearranged to measure the MD width and CCH. We established a virtual occlusal plane and positioned the incisal tip and buccal cusp of the overlapping teeth. Then, we adjusted the rotation, angulation, and inclination of the teeth to measure the MD width and CCH. The accuracy of the MD width of the three experimental groups was evaluated using the values output from each program. For comparison, the control group values were measured by Meshmixer. For measuring CCH, the segmented teeth of all groups re-orientated in Meshmixer were loaded into Geomagic Control X software, and the distance between the virtual occlusal plane and the lowest point of the gingival margin of the clinical crown was measured  of the digital model after orientation. In the AS group using LaonSetup, the software automatically classified and segmented the teeth.p values of less than 0.05 were considered as indicating statistical significance.Data of numerical variables were summarised by their mean . The reliability of the measurement of tooth size error was calculated by determining the intraclass correlation coefficient . Cohen’s kappa determined the reliability of success and failure of tooth segmentation with nominal variables. Differences in variables across groups were compared with Friedman’s test or Cochran’s Q test as appropriate. We used the Shapiro–Wilk test to verify whether the data followed a normal distribution. Independent variables affecting the response (tooth size error) were simultaneously evaluated by a GLMM. GLMM jointly considered the main and the first-order interaction effects. Bonferroni’s test was used for post hoc multiple comparisons. All statistical analyses were performed using SPSS version 26.0 statistical software, and Supplementary Figure S1.


This is the protocol for a Campbell systematic review. The objectives are as follows: to produce a mega‐map which identifies, maps and provides a visual interactive display, based on systematic reviews on all the main aspects of elder abuse in both the community and in institutions, such as residential and long‐term care institutions. The maiAround one in six people 60 years and older experience some form of abuse in community settings annually. Some 12% suffer psychological abuse; 7%, financial abuse; 4%, neglect; 3%, physical abuse; and 1% experience sexual abuse  .In relation to the characteristics of perpetrators, they include:The evidence supporting risk and protective factors at the level of relationships, the community and society is generally weaker. These may include type of relationship , but these vary by type of abuse and by culture; marital status (though findings are mixed); and levels of ageism in society. The evidence for many other risk factors is either weaker or mixed—for example, gender of victims, age, race/ethnicity  helplines, the most widely used interventions in most countries; (2) caregiver interventions, which provide services to relieve the burden of caregiving; (3) multidisciplinary teams which coordinate care and reduce fragmentation in response to elder abuse; (4) money management programmes which aim to reduce risk of financial exploitation, especially of people with cognitive impairment; and (5) emergency shelters  and type of abuse whose prevalence is being measured ;2.Consequences, organized by type of consequence  and by type of abuse;3.Risk and protective factors, organized by type of risk or protective factor at the individual victim, individual perpetrator, relationship, community and societal levels, as well as the level of institutions and type of abuse; and4.Interventions, organized by type of intervention  and type of abuse.The scope of the map will cover the following:The definition of the scope of, as well as the framework for see , the map1.4This mega‐map will be informed by the public health approach to the prevention of health conditions, including violence, and will draw on three interconnected aspects of the public health approach: 1) the four steps of the public health approach  are not clear, elder abuse cannot be measured accurately and its prevalence and distribution (Step 1) cannot be established with confidence. If elder abuse cannot be measured accurately, it will be more difficult to identify risk and protective factors and underlying causes correctly (Step 2). If risk and protective factors and underlying causes are not identified correctly (Step 2), interventions to prevent elder abuse (Step 3) are unlikely to be effective and difficult to implement and scale up (Step 4).The socio‐ecological model: The public health approach draws on the socio‐ecological systems model which helps to identify and explain the multidimensional aspects of elder abuse , relationship, community and societal or systems levels—are considered to put people at risk of elder abuse Figure . IdentifClassification of interventions: In addition, we will draw on the distinction between preventing, detecting and responding, which is widely used in public health, to organize interventions as follows ;To mitigate the impairment caused by elder abuse (aimed at older people who have experienced abuse).1.5https://maltraitancedesaines.com/en/). Our searches will not be in any way limited to this database and will extend across multiple databases. Examples of systematic reviews include the following:No completed EGM on any aspect of elder abuse were identified. There are, however, many relevant systematic reviews on the prevalence and consequences of, and risk and protective factors and interventions for elder abuse. The database maintained by Sherbrooke University in Canada has, for instance, identified 64 systematic reviews covering various aspects of elder abuse or of related issues . Prevalence of elder abuse in Iran: A systematic review and meta‐analysis. Journal of Elder Abuse & Neglect, 30(5), 408–423. doi:10.1080/08946566.2018.1523765Arab‐Zozani, M., Mostafazadeh, N., Arab‐Zozani, Z., Ghoddoosi‐Nejad, D., Hassanipour, S., & Soares, J. J. F. (2018). The prevalence of elder abuse and neglect in Iran: A systematic review and meta‐analysis. Age Ageing, 37(2), 151–160. doi:10.1093/ageing/afm194Cooper, C., Selwood, A., & Livingston, G. (2008). The prevalence of elder abuse and neglect: A systematic review. East Asian Archives of Psychiatry, 27(2), 43.Ho, C. S., Wong, S.‐Y., Chiu, M. M., & Ho, R. (2017). Global prevalence of elder abuse: A metaanalysis and meta‐regression. Trauma Violence Abuse, 14(4), 316–325. doi:10.1177/1524838013495963Sooryanarayana, R., Choo, W. Y., & Hairi, N. N. (2013). A review on the prevalence and measurement of elder abuse in the community. Trauma Violence & Abuse, 16(2), 199–219. doi:10.1177/1524838014555033Yan, E., Chan, K. L., & Tiwari, A. (2015). A systematic review of prevalence and risk factors for elder abuse in Asia. Trauma, Violence, & Abuse, 20(2), 245–259.Yon, Y., Mikton, C., Gassoumis, Z. D., & Wilber, K. H. (2019). The prevalence of self‐reported elder abuse among older women in community settings: a systematic review and meta‐analysis. The Lancet Global Health, 5(2), e147–e156.Yon, Y., Mikton, C. R., Gassoumis, Z. D., & Wilber, K. H. (2017). Elder abuse prevalence in community settings: A systematic review and meta‐analysis. European Public Health Association, 29(1), 58‐67. doi:10.1093/eurpub/cky093Yon, Y., Ramiro‐Gonzalez, M., Mikton, C. R., Huber, M., & Sethi, D. (2019). The prevalence of elder abuse in institutional settings: a systematic review and meta‐analysis. Prevalence:Health & Social Care in the Community, 19(1), 3–14. doi:10.1111/j.1365‐2524.2010.00964.xMcGarry, J., Simpson, C., & Hinchliff‐Smith, K. (2011). The impact of domestic abuse for older women: A review of the literature. Trauma, Violence & Abuse, 20(2), 197–213. doi:10.1177/1524838017692798Yunus, R. M., Hairi, N. N., & Choo, W. Y. (2019). Consequences of Elder Abuse and Neglect: A Systematic Review of Observational Studies. Same question as above. ConsequencesAge Ageing, 42(3), 292–298. doi:10.1093/ageing/afs195Johannesen, M., & LoGiudice, D. (2013). Elder abuse: A systematic review of risk factors in community‐dwelling elders. Gerontologist, 56(Suppl_2), S194–S205. doi:10.1093/geront/gnw004Pillemer, K., Burnes, D., Riffin, C., & Lachs, M. S. (2016). Elder abuse: Global situation, risk factors, and prevention strategies. Aggression and Violent Behavior, 50, 101339.Storey, J. E. (2020). Risk factors for elder abuse and neglect: A review of the literature. Risk and protective factorsAge and Ageing, 45(2), 216–227.Ayalon, L., Lev, S., Green, O., & Nevo, U. (2016). A systematic review and meta‐analysis of interventions designed to prevent or stop elder maltreatment. Cochrane Database of Systematic Reviews (8).Baker, P. R., Francis, D. P., Hairi, N. N., Othman, S., & Choo, W. Y. (2016). Interventions for preventing abuse in the elderly. Journal of Elder Abuse & Neglect, 23(4), 348–365.Daly, J. M., Merchant, M. L., & Jogerst, G. J. (2011). Elder abuse research: A systematic review. Journal of elder abuse & neglect, 29(2–3), 102–133.Fearing, G., Sheppard, C. L., McDonald, L., Beaulieu, M., & Hitzig, S. L. (2017). A systematic review on community‐based interventions for elder abuse and neglect. 14, 1797. Ploeg, J., Fear, J., Hutchison, B., MacMillan, H., & Bolan, G. (2009). A systematic review of interventions for elder abuse. Journal of Elder Abuse & Neglect, 21(3), 187–210.Mileski, M., Lee, K., Bourquard, C., Cavazos, B., Dusek, K., Kimbrough, K., & McClay, R. (2019). Preventing the abuse of residents with dementia or Alzheimer's disease in the long‐term care setting: A systematic review. Clinical interventions in aging, Trauma, Violence, & Abuse, 1524838019889359. Rosen, T., Elman, A., Dion, S., Delgado, D., Demetres, M., Breckman, R., & National Collaboratory to Address Elder Mistreatment Project Team. (2019). Review of programs to combat elder mistreatment: focus on hospitals and level of resources needed. Journal of the American Geriatrics Society, 67(6), 1286–1294.Mohd Mydin, F. H., Yuen, C. W., & Othman, S. (2019). The effectiveness of educational intervention in improving primary health‐care service providers' knowledge, identification, and management of elder abuse and neglect: A systematic review. Interventions:An EGM on interventions for elder abuse is currently being developed by a team at Lanzhou University in China including Jieyun Li, Liping Guo, Howard White, Jingwen Li, XiuxiaLi, and Kehu Yang. We have been collaborating with them very closely and are striving to align the component of our mega‐map that addresses interventions with theirs to ensure that they are both compatible and complementary. Moreover, we have been reviewing each other's Title Registration Forms and Protocols and the Lanzhou team will help us with our searches in Mandarin.Although our mega‐map will complement Lanzhou's team EGM on interventions for elder abuse, it will also differ from theirs in the following respects: (a) our mega‐map covers the prevalence and consequences of and risk and protective factors for elder abuse, in addition to interventions; and (b) will be based only on systematic reviews. The Lanzhou team's EGM on interventions will include information on primary studies and will complement this mega‐map by providing a more fine‐grained mapping of interventions and outcomes measures.2The overall aim is to produce a mega‐map which identifies, maps and provides a visual interactive display, based on systematic reviews on all the main aspects of elder abuse in both the community and in institutions, such as residential and long‐term care institutions.The specific objectives are to:1.Identify, appraise and map available systematic reviews on the:–Prevalence of elder abuse;–Consequences of elder abuse;–Risk and protective factors for elder abuse at the levels of the victim, perpetrator, relationships, community and societal levels; and–Interventions to prevent, detect and respond to elder abuse;2.Develop a taxonomy of interventions to prevent, detect and respond to elder abuse and outcomes of such interventions.3.Provide information in the mega‐map which summarize the aim of the systematic reviews included, the methods used, its main findings and conclusions, an appraisal of the quality of the review, and a link to it.4.Develop two visual and interactive maps based on the systematic reviews: The first will consist of a matrix with rows and columns with systematic reviews in the cells; the second will consist of map of the world with the distribution by countries of the primary studies included in the systematic reviews.This will done in both community and institutional settings and an overview will be provided in a summary report;33.1EGMs are maps of a specific sector or sub‐sector which typically includes both systematic reviews and primary studies. Mega‐maps are maps which are broader in scope covering a large sector or several sectors that includes only systematic reviews and other maps. Produced using the same systematic approach as systematic reviews, both EGMs and mega‐maps usually show what evidence is there, not what the evidence says , setting (community or institution), WHO‐defined regions of the world, and World Bank‐defined country income level).3.3The framework for the map has been developed by drawing on typologies in existing systematic reviews and the expertise of the team working on the map, several members of which are world‐renowned experts on elder abuse. The development of the framework has also been informed by a meeting of the advisory and stakeholders group which reviewed an earlier version of the framework and helped define the scope of the mega‐map ;∘Mortality;∘Physical health symptoms;∘Psychological/mental health symptoms;∘Social and economic consequences .∘Other consequences of elder abuse.Studies on risk and protective factors for elder abuse, divided into studies on:∘Individual level factors related to the victim ;∘Individual level factors related to the perpetrator ;∘Relationship level factors ;∘Community and societal level risk factors ;∘The characteristics of care institutions .Studies on interventions to prevent, detect and respond to elder abuse, each divided into the following, based on the target of the intervention:∘Older people;∘Professional caregivers;∘Non‐professional caregivers ;∘Perpetrators of elder abuse (for response only);∘The general population (for prevention and detection only);∘The level of systems .▪With the help of the filters, it will be possible to select interventions to respond to elder abuse that focus on preventing recurrence or those interventions to respond to elder abuse that focus on mitigating consequences.3.3.2Columns in the framework will refer to elder abuse and the different sub‐types of elder abuse. Again, because this map includes studies on prevalence, consequences and risk and protective factors, and covers more than interventions, the columns will not strictly speaking refer to ʻoutcomes’, as is customary in maps of interventions. For instance, in relation to prevalence, elder abuse and its sub‐types (in the columns), will refer to the type of abuse being measured in the prevalence study in either the community or in institutions (in the rows). In relation to consequences, elder abuse and its sub‐types (in the columns), will be the predictors or ʻcauses’ and the different consequences the ʻeffect’ (in the rows). In the case of risk and protective factors, elder and its sub‐types (in the columns), will be the outcome predicted by the different types of risk and protective factors (in the rows). In relation to interventions, elder abuse and its sub‐types (in the columns) will be the outcome the different interventions are seeking to prevent, detect and respond to.Any elder abusePhysical abusePsychological abuseSexual abuseFinancial/material abuseNeglectSystemic/organizational abusePoly‐victimizationOther forms of abusePotential adverse and unintended outcomes (for interventions only).The following types of abuse will be included in the columns as 'outcomes' ;Online or face‐to‐face abuse ;Setting (review focusing on elder abuse in the community or in institutions or both in the community and in institutions);Source of report, mainly for reviews on prevalence ;Victim characteristics ;Perpetrator characteristics ;WHO‐defined geographical region (review includes at least one study from that region);World Bank‐defined country income level (review includes at least one study from a country at that income level);Conflict of interest (declaration of a conflict of interest).Filtering variables will include:3.43.4.1https://www.campbellcollaboration.org/what‐is‐a‐systematic‐review.htmlOnly systematic reviews and evidence and gap maps will be eligible for inclusion. We will follow the Campbell Collaboration's definition of systematic reviews—Reviews which do not fit this definition of systematic review will be excluded.Systematic reviews on the following aspects of elder abuse, in both the community and in institutions, will be eligible: prevalence, consequences, risk and protective factors and interventions to prevent, detect and respond to elder abuse. Systematic review which address more than one of these four aspects simultaneously or which address one or more of these four aspects as well as other aspects of elder abuse will also be included.Detailed eligibility criteria are described in Supporting Information Appendix 3.4.2Systematic reviews including some or only qualitative studies will be included, as will qualitative primary studies included in systematic reviews.3.4.3All the main types of settings in all regions of the world and in all country income levels in which elder abuse is likely to be recorded and in which interventions for elder abuse are likely to be implemented will be included. In practice, these will be divided into two main settings: community and institutional care settings.3.4.4On‐going systematic reviews and EGMs will be included. Authors will be contacted to check if and when they will be completed and will be included or excluded accordingly. If excluded we will aim to include them in updates of the mega‐map provided they have been completed by then.3.5Systematic literature searches will be conducted on a wide range of databases without language restriction covering health and related disciplines such as social sciences, social care and psychology, and databases indexing research specific to older people. Databases will include (but not be limited to); MEDLINE, ASSIA, SOCIndex, PsycINFO, AgeLine, the Cochrane Database of Systematic Reviews, and the Campbell web site. Searches for subject headings (where available) will be combined with controlled vocabulary and free text terms relating to the population (older people), terms relating to types of elder abuse and terms relating to types of systematic reviews. Methodological search filters will be applied where possible to identify systematic reviews. The proposed search strategy has been peer‐reviewed by two senior information specialists. An example of a complete search strategy can be found in Supporting Information Appendix Grey literature will be identified by searching repositories, relevant organizational websites, dissertation and theses databases, databases of conference abstracts, and academic search engines such as Google Scholar. Key journal websites will also be searched. Reference lists of included reviews will be checked for any additional relevant studies. In addition, we will contact relevant individuals and organizations for information about unpublished or ongoing studies.Search results will be imported into an Endnote library and duplicates will be removed. The references will then be imported into EPPI‐Reviewer for screening, data extraction and quality assessment.3.6Electronic databasesGrey Literature and other non‐standard database searchesWebsites from key organizations3.7All titles, abstracts and then full text of retrieved papers will be double screened, with a third‐party arbitrator in the event of disagreement. A data extraction form (with detailed definitions), detailed eligibility criteria and a screening tool—see Supporting Information Appendices Only primary studies included in the systematic reviews will be included. Should these primary studies include multiple reports on the same study, the unit of analysis for the mega‐map will be the underlying study (i.e. sample), and not the multiple reports.3.8Data extraction and coding will be done independently by two coders, with a third‐party arbitrator in the event of disagreement, using EPPI‐Reviewer.Within the cells of the interactive map, the symbols used to represent the included reviews and primary studies included in the reviews will be designed so they can be changed in size and/or colour to explore the secondary dimensions described above.https://eppi.ioe.ac.uk/cms/er4/EPPI‐Mapper/tabid/3790/Default.aspx).The coded data will be converted to a JSON file, and EPPI‐Mapper will be used to generate the interactive map  and, should it become available, use the Tool for Addressing Conflicts of Interest in Trials (https://tacit.one/).We will identify and extract information on conflicts of interest, when it is available, following the approach outlined in the Cochrane Handbook , is the largest and most recent review on the risk factors for elder abuse. And she has completed other literature reviews.Information retrieval: Has published in leading journals on research relating to elder abuse, ageism, violence prevention, violence risk and violence management.Dr. Michaela RogersContent experience: Is a registered social worker and academic with nearly 30 years of research and practice experience in the domestic abuse sector. Latterly, her research and scholarship has included a focus on older adults in relation to interpersonal violence and abuse.Methodology expertise: Qualitative researcher who has conducted systematic reviews.Information retrieval: She has published in the field of gender‐based violence and has recently contributed to national guidance for social workers working with families affected by domestic abuse.Dr. Fiona CampbellContent experience: She has a background in public health and public health nursing.Methodology expertise: Has over 20 years of experience in undertaking evidence synthesis to support decision making, policy and guideline development. She is an Editor with the Social Welfare Coordinating Group within the Campbell Collaboration. She has undertaken and supported the development of Cochrane and Campbell systematic reviews as well as for the WHO, NICE, NIHR, Alcohol Action, Public Health England and the Department of Health. She teaches a course in collaboration with the EPPI‐Centre on mapping reviews and developing Evidence Gap Maps. She has created evidence gap maps to support the development of guidelines and inform research priorities for Epilepsy Action and NIHR.Information retrieval: Has published a large number of systematic reviews, she is the author of 12 Cochran reviews.Anthea SuttonInformation retrieval: Information Resources Group Manager; Director of Professional LearningDr. Parveen AliContent experience: is a public health researcher with academic and research experience of over 20 years.Methodology expertise: Dr. Ali is a mixed methods researcher and has extensive experience conducting systematic reviews and evidence synthesis of various topics and especially those related to elder abuse, gender‐based violence and domestic violence. She has extensive experience of creating face to face online courses that have used maps and other graphics to explain the extent and impact of abuse on victims.Information retrieval: Published widely in the field of gender‐based violence and nursing.Dr. David BurnesContent experience: Has authored many peer‐reviewed publications and been awarded multiple external and federal level grants on the topic of elder abuse He received an NIH grant to conduct a longitudinal, population‐based elder abuse study on elder abuse risk factors and consequences. He led a paper to identify the prevalence and risk factors of elder abuse based on the New York State Elder Mistreatment Prevalence Study. Dr. Burnes has conducted several gerontology‐related systematic, meta‐analysis, or scoping reviews including on elder financial fraud and elder abuse intervention outcomes. Dr. Burnes has successfully contributed to projects with the WHO on topics related to elder abuse and ageism.Methodology expertise: Systematic reviews, data synthesisInformation retrieval: Has published in leading journals on research relating to elder abuse, risk factors and consequences.Bridget PenhaleContent experience: Is a registered social worker and academic. Her experience in elder abuse dates back to early work as a social worker in the 1980s. She published one of the first academic papers on elder abuse in the UK in 1993. Recognized nationally in the UK for her work on adult safeguarding/adult protection and internationally for her work on elder abuse. In 2010, she received the International Rosalie Wolf Award for her work in the field. She has undertaken research on adult safeguarding and elder abuse in the UK and Europe. Bridget has also acted as a consultant/advisor to the WHO, the Department of Health (in England and Scotland) and the European Commission in relation to elder abuse. She was also a member of an Expert Working Group on Violence Against Older Women convened by the UN.Methodology expertise: Contributed to a number of gerontology‐related systematic and scoping reviews, including several related to elder abuse.Information retrieval: Has published in leading journals on research relating to elder abuse.Professor Tova Band‐WintersteinContent experience: Research various aspects of elder abuse and neglect including intimate partner violence (IPV) in late life and elder self‐neglect. Recently, she has expanded her work on aging women who are sexually abused; older women who experienced incest in their early life; and IPV among multicultural groups. She is the chair of The Minerva Center on Intersectionality in Aging, and added research on special populations that are marginalized in a variety of ways, including abuse and neglect, but also reaching beyond this .Methodology expertise:Information retrieval: She coordinated the first national survey on elder abuse and neglect in Israel and published two books on the topic: Like a wounded pigeon: Life stories of old battered women (2008) and Intimate violence across the lifespan: Interpersonal, familial and cross‐generational perspectives (2014).Professor Mark LachsContent experience: Is a chronic disease epidemiologist who has conducted some of the largest longitudinal studies of elder abuse. Specifically, his work with the NIA EPEPE cohort demonstrated that being an elder abuse victim is associated with a threefold risk of death after adjustment for comorbidity and other factors. Subsequent work found that elder abuse was an independent predictor of nursing home placement. More recently his research has involved risk factors and outcomes for elder abuse occurring within long term care facilities between residents.Methodology expertise: Is a trained quantitative researcher with expertise in longitudinal research.Information retrieval: Has published in leading journals on research relating to public health including elder abuse.Professor Karl PillemerContent experience: Is the most highly‐cited researcher in the field of elder abuse, having contributed to theory, research and intervention in the field over several decades. He conducted the first prevalence studies of the topic in family and institutional settings and the first large‐scale epidemiological survey of elder abuse and neglect, which established the benchmark prevalence rate for elder mistreatment. Since that time, he has conducted ground breaking work on risk factors for and consequences of elder abuse. Dr. Pillemer is also involved actively in intervention research and in translational research on elder abuse, exploring ways to speed the transfer of findings from basic research into scientifically tested interventions to prevent and treat elder abuse.Methodology expertise: Has been involved in multiple systematic review projects involving elder abuse and related topics.Information retrieval: Has published in leading journals on research relating to elder abuse and ageism.Kelly Marnfeldt will be assisting with screening and data extraction. She is a doctoral student at the University of Southern California's Leonard Davis School of Gerontology.Content expertise: Has conducted primary research on the prevention of mistreatment and neglect of older adults, and interventions to alleviate caregiver burden.Methodological expertise: Trained in qualitative and mixed methods research. She has conducted literature reviews on the topics of caregiving, social isolation and loneliness, and elder mistreatment.Lilly Estenson will be assisting with screening and data extraction. She is a doctoral student at the University of Southern California's Leonard Davis School of Gerontology.Content expertise: Has a background in gerontological social work research and practice.Methodological expertise: Trained in qualitative and mixed methods research with experience conducting systematic reviews.Dr. Mark Byrne will be assisting with screening and data extraction. He is a research fellow at the School of Nursing and Midwifery, Trinity College Dublin.Content expertise: Has experience in both qualitative and quantitative methods research. Conducted primary research (mixed methods) in the area of mental health.Information retrieval: Has conducted literature reviews on the topics of inner conflict and depression.Yongjie Yon and Christopher Mikton have published several systematic reviews on the global prevalence of elder abuse .Marie Beaulieu has published several reports, articles, books chapters and books on the state of knowledge of elder abuse and neglect. She has also developed several practice tools. She has published original research on practices to prevent and respond to elder abuse and neglect. The Government of Quebec finances her Chair without a right to view and control her scientific production.Julien Cadieux Genesse and Kevin St‐Martin, as co‐authors, published some reports, articles and books chapter with Marie Beaulieu.Contributors Dr. Jennifer Storey, Dr. Michaela Rogers, Dr. Parveen Ali, Dr. David Burnes, Bridget Penhale, Professor Tova Band‐Winterstein, Professor Mark Lachs and Professor Karl Pillemer have published reports, articles and books chapters on the state of knowledge of elder abuse and neglect. This includes original research and reviews.None. 1World Health Organization, Switzerland. Financial supportUniversity of Sherbrooke, Canada. Financial support2None.Supporting information.Click here for additional data file.


By comparison, similar scores on 30 new elements in L2 (upper-body) are 7.73 and 7.40, respectively. Therefore, the proposed intelligent robot-imagined choreography model can help the dancing robot choreograph more finely and improve the choreography efficiency. The research results have significant practical value for dance teaching.To study the influence of Artificial Intelligence (AI) on dancing robots in choreography, this paper introduces the biped-humanoid robot-imagined choreography model alongside the Psychological Space Construction  and Human–Computer Interaction (HCI). The proposed model is based on deep learning and imitating human thinking and is capable of imagining new dance elements. Finally, simulation experiments are designed to verify the model's effectiveness. Dance professionals are invited to evaluate the robot-imagined dance posture. The results show that the proposed model can vividly imitate human dancers and imagine and create new dance movements. The average basic feature retention and innovation scores of 30 new dance elements imagined on the L Dance, as an art, conveys people's emotional and ideal aesthetics through the relationship between body and spirit Li, . In the The technical level of biped robot gait research is not mature. Its stable gait and balance ability are still the research focus. Control and planning the robot's gait so that the robot's walking motion is always stable is of great practical importance. Barnes et al.  observedThe main contribution is to study the impact of Artificial Intelligence (AI) on dancing robots in choreography by introducing robot choreography and Psychological Construct. Most of the existing research focuses on robot dance's action design and research direction. Compared with the existing research, the innovation lies in establishing a choreography model that enables a bipedal humanoid robot to choreograph by imitating human thinking actively. Being able to choreograph beautiful dance moves using robots opens up new ideas for future research on dance creation.Dance experts often use familiar dance postures in choreography. Even similar dance postures exert different artistic tastes due to human dancers' individual charisma. Nevertheless, those uniformly programmed dancing robots' monotonous movements might easily get people disinterested after a few tryouts  constructs are both essential for art. Like other art forms, choreography generates a series of psychological behaviors with complex interactions and relations, where the subject's imagination exerts the most active psychological function  member, and share their joys and pains. Overall, the dance can well reflect ordinary people's lives and tendering emotions, thus infecting the audience. The two actors accurately convey the characters' psychology through aesthetic dance moves, such as modesty, heartache, helplessness, comfort, coquetry, marital love, and other real-life scenes. Meanwhile, the director has consolidated the characters' emotions into each move and embeds their psychological rhythm into the dance connection. The process of the Psychological Construct is demonstrated in From the perspective of art, the Psychological Construct is mainly affected by choreographers' experiences in real life. In the art of dance, to pursue a better theme, the artistic works must appeal to choreographers' thoughts and emotions through a vague mental state in the psychological space. For example, in the dance A Couple Guarding A Sentry Box. Obviously, the dancers have successfully expressed the characters' emotions and psychology through dance moves. Such is the process of Psychological Construct.Based on the achievements of DL in human keypoint recognition, the Three-dimensional (3D) position coordinates of human key points are obtained in the image by analyzing RGB images. BPNN is a multilayer Artificial Neural Network (ANN), a new research direction in the DL field. It is introduced into a broader concept of Machine Learning (ML) to help realize true AI. In particular, AI aims to understand the essence of intelligence and fabricate an intelligent machine with human intelligence. It involves multiple disciplines, such as robotics, language recognition, Image Recognition (IR), Natural Language Processing (NLP), and Expert Systems (ES). BPNN features error back-propagation and signals forward transmission. As detailed in In addition to adopting the idea of choreography, choreographers also follow the following creative rules. (1) Learn and master the core elements of some dance forms, such as the shape and position of hand and feet shape, position, and speed. New elements lay the foundation of spontaneous imagination. (2) All these elements are used as the basic dance, and the dance movement is innovated, conforming to human body aesthetics and design. (3) These dance movements are associated together with different combinations, fabricating an intact dance work. Based on the above BPNN research and the objective rules of human choreography, the biped robot can imitate human thinking when choreographing the independent dance. The specific flow is as follows. Learning → Memorizing → Imagining → Processing → Combining. In order to efficiently complete choreography independent of human thought on the biped robot platform, the model is implemented, as given in The joint angle is given as the action command of the robot. The robot's inverse kinematics calculation and forward kinematics calculation involves the mutual conversion between the joint coordinates and the joint angle. Moreover, the angle must be adjusted in multiple steps. This complex angle adjustment is unnecessary when the robot cannot imitate some delicate motions due to the freedom and mechanical structure constraints.The dance representation space of the humanoid robot can be represented by HRDES = {K, B, L, D, DA, DN, DC}. The meaning and expression of each part are listed in L) before independent robot choreography. Then, the robot will learn all the dance elements demonstrated by human expert dancers alongside various dance postures. Different dance elements and postures combinations will be generated and stored in the robot dance memory space.Before independent choreography, the robot should first determine what dance postures to choreograph , referring to literature :Every imagined dance element on limb R, and the greater the difference between dance elements is, and vice versa. Therefore, an appropriate neighborhood di should be determined. All the neighborhood points (di) are similar, yet each is a unique dance element point. In R, two spatially distanced points and no less than 2di are deemed distinguishable dance elements. In Equations (2) and (3), DAi, u and DAi, v represent two dance elements on the same limb Li, their PSI [S] is calculated by Equation (2), and Equation (3) judge whether the two elements are identical.The larger the neighborhood is, the fewer dance elements are in Li. As shown in Next, a new dance element imagination algorithm is given based on the change of single joint value on limb Subsequently, this section takes Tibetan tap dance as an experimental subject for independent humanoid robot-imagined choreography. The Tibetan tap dance features regular knee swings and footsteps  and Webots 7.0 4.1 environments. The “memory dance space” is the Windows file system, where all instantiated objects are stored. The visual C++ platform is chosen for coding , with the constraints D = {D1, D2, D3), as unfolded in L3 (lower-body) plays an essential role in body balance and reflects the features of Tibetan tap dance. Thus, constraints are imposed on limb L3 to maintain the dance features, and D3 = l. In other words, it is not allowed to imagine dance postures on L3 limbs. In Tibetan tap dance, human dancers often keep their hands relaxed. Accordingly, the NAO robot's left arm and right arm are modeled as the upper body L2 (Upper-body), not independently. Compared with L3, L2, and Ll (head) are more flexible, so D1 = 0 and D2 = 0. Hence, L2 and Ll can be imagined with new dance elements based on the learned dance elements on L3. The joint set corresponding to limb L1 is {HeadYaw, HeadPitch}. The joint set corresponding to limb L2 is {LShoulderPitch, LShoulderRoll, LEbowYaw, LElbowRoll, LWristYaw, LHand, RShoulderPitch, RShoulderRoll, RElbowYaw, RElbowRoll, RWristYaw, RHand}. The joint set corresponding to limb L3 is {LHipYawPitch, LHipRoll, LHipPitch, LKneePitch, LAnkiePitch, LAnkleRoll, RHipYawPitch, RHipRoll, RHipPitch, RKneePitch, RAnklePitch, RAnkleRoll}.First, the dance representation space of the humanoid robot must be instantiated  are collected and stored in the dance space. Specifically, seven basic dance elements are learned on the limb L2, as given in L1, as shown in L3, as depicted in L1 and L2, the parameters are set as d1 = 1 and d2 = 1.Based on the dance representation space of the instantiated humanoid robot, the PSI-based simulation learning method is adopted to learn the basic dance elements of human dancers. Thus, the robot's basic dance elements on three limbs  for the publication of any potentially identifiable images or data included in this article.The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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