Development of the Korean construction job exposure matrix (KoConJEM) based on experts' judgment using the 60 consolidated occupations for construction workers.

BACKGROUND: This study was conducted as an effort to develop a Korean construction job exposure matrix (KoConJEM) based on 60 occupations recently consolidated by the construction workers mutual aid association for use by the construction industry. METHODS: The probability, intensity, and prevalence of exposure to 26 hazardous agents for 60 consolidated occupations were evaluated as binary (Yes/No) or four categories (1 to 4) by 30 industrial hygiene experts. The score for risk was calculated by multiplying the exposure intensity by the prevalence of exposure. Fleiss' kappa for each hazardous agent and occupation was used to determine agreement among the 30 experts. The JEM was expressed on a heatmap and a web-based dashboard to facilitate comparison of factors affecting exposure according to each occupation and hazardous agent. RESULTS: Awkward posture, heat/cold, heavy lifting, and noise were hazardous agents regarded as exposure is probable by at least one or more experts in all occupations, while exposure to asphalt fumes was considered hazardous in the smallest number of occupations (n = 5). Based on the degree of agreement among experts, more than half of the harmful factors and most occupations showed fair to good results. The highest risk value was 16 for awkward posture for most occupations other than safety officer. CONCLUSIONS: The KoConJEM provides information on the probability, intensity, and prevalence of exposure to harmful factors, including most occupations employing construction workers; therefore, it may be useful in the conduct of epidemiological studies on assessment of health risk for construction workers.

Treatment outcome and long-term stability of orthognathic surgery for facial asymmetry: A systematic review and meta-analysis.

Objective: : This systematic review aimed to provide a comparative analysis of the treatment outcomes, including hard and soft tissues, postoperative stability, temporomandibular disorders (TMD), and quality of life (QoL), in patients with facial asymmetry who underwent orthognathic surgery. Methods: : The primary objective was to address the question, "How do different factors related to surgery affect the outcomes and stability of orthognathic surgery in the correction of facial asymmetry?" A meta-analysis was conducted on the outcome parameters, such as skeletal, dental, and soft tissue symmetry, TMD, QoL, and relapse, using the Hartung-Knapp-Sidik-Jonkman method for random-effects models. Subgroup analyses were conducted considering surgery-related factors such as surgical techniques (one-jaw vs. two-jaw), use of the surgery-first approach, utilization of computer simulation, and analytical methods employed to evaluate asymmetry (2D vs. 3D). Results: : Forty-nine articles met the inclusion criteria. The meta-analysis demonstrated a significant improvement in the symmetry of hard and soft tissues. The subgroup analysis indicated that the treatment outcomes showed significant improvement, regardless of the factors related to surgery. Changes in TMD signs and symptoms varied according to the surgical technique used. Quality of life improved in the facial, oral, and social domains. Skeletal relapse was observed during the follow-up. Conclusions: : Our findings support the positive outcomes of orthognathic surgery in the treatment of facial asymmetry in terms of skeletal and soft tissue improvements, stability, relief of TMD symptoms, and enhancement of QoL. However, most of the included studies showed a low certainty of evidence and high heterogeneity.

Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks.

BACKGROUND: This study aimed to assess the error range of cephalometric measurements based on the landmarks detected using cascaded CNNs and determine how horizontal and vertical positional errors of individual landmarks affect lateral cephalometric measurements. METHODS: In total, 120 lateral cephalograms were obtained consecutively from patients (mean age, 32.5 ± 11.6) who visited the Asan Medical Center, Seoul, Korea, for orthodontic treatment between 2019 and 2021. An automated lateral cephalometric analysis model previously developed from a nationwide multi-centre database was used to digitize the lateral cephalograms. The horizontal and vertical landmark position error attributable to the AI model was defined as the distance between the landmark identified by the human and that identified by the AI model on the x- and y-axes. The differences between the cephalometric measurements based on the landmarks identified by the AI model vs those identified by the human examiner were assessed. The association between the lateral cephalometric measurements and the positioning errors in the landmarks comprising the cephalometric measurement was assessed. RESULTS: The mean difference in the angular and linear measurements based on AI vs human landmark localization was .99 ± 1.05°, and .80 ± .82 mm, respectively. Significant differences between the measurements derived from AI-based and human localization were observed for all cephalometric variables except SNA, pog-Nperp, facial angle, SN-GoGn, FMA, Bjork sum, U1-SN, U1-FH, IMPA, L1-NB (angular) and interincisal angle. CONCLUSIONS: The errors in landmark positions, especially those that define reference planes, may significantly affect cephalometric measurements. The possibility of errors generated by automated lateral cephalometric analysis systems should be considered when using such systems for orthodontic diagnoses.

Accuracy of posteroanterior cephalogram landmarks and measurements identification using a cascaded convolutional neural network algorithm: A multicenter study.

Objective: : To quantify the effects of midline-related landmark identification on midline deviation measurements in posteroanterior (PA) cephalograms using a cascaded convolutional neural network (CNN). Methods: : A total of 2,903 PA cephalogram images obtained from 9 university hospitals were divided into training, internal validation, and test sets (n = 2,150, 376, and 377). As the gold standard, 2 orthodontic professors marked the bilateral landmarks, including the frontozygomatic suture point and latero-orbitale (LO), and the midline landmarks, including the crista galli, anterior nasal spine (ANS), upper dental midpoint (UDM), lower dental midpoint (LDM), and menton (Me). For the test, Examiner-1 and Examiner-2 (3-year and 1-year orthodontic residents) and the Cascaded-CNN models marked the landmarks. After point-to-point errors of landmark identification, the successful detection rate (SDR) and distance and direction of the midline landmark deviation from the midsagittal line (ANS-mid, UDM-mid, LDM-mid, and Me-mid) were measured, and statistical analysis was performed. Results: : The cascaded-CNN algorithm showed a clinically acceptable level of point-to-point error (1.26 mm vs. 1.57 mm in Examiner-1 and 1.75 mm in Examiner-2). The average SDR within the 2 mm range was 83.2%, with high accuracy at the LO (right, 96.9%; left, 97.1%), and UDM (96.9%). The absolute measurement errors were less than 1 mm for ANS-mid, UDM-mid, and LDM-mid compared with the gold standard. Conclusions: : The cascaded-CNN model may be considered an effective tool for the auto-identification of midline landmarks and quantification of midline deviation in PA cephalograms of adult patients, regardless of variations in the image acquisition method.

Novel Procedure for Automatic Registration between Cone-Beam Computed Tomography and Intraoral Scan Data Supported with 3D Segmentation.

In contemporary practice, intraoral scans and cone-beam computed tomography (CBCT) are widely adopted techniques for tooth localization and the acquisition of comprehensive three-dimensional models. Despite their utility, each dataset presents inherent merits and limitations, prompting the pursuit of an amalgamated solution for optimization. Thus, this research introduces a novel 3D registration approach aimed at harmonizing these distinct datasets to offer a holistic perspective. In the pre-processing phase, a retrained Mask-RCNN is deployed on both sagittal and panoramic projections to partition upper and lower teeth from the encompassing CBCT raw data. Simultaneously, a chromatic classification model is proposed for segregating gingival tissue from tooth structures in intraoral scan data. Subsequently, the segregated datasets are aligned based on dental crowns, employing the robust RANSAC and ICP algorithms. To assess the proposed methodology's efficacy, the Euclidean distance between corresponding points is statistically evaluated. Additionally, dental experts, including two orthodontists and an experienced general dentist, evaluate the clinical potential by measuring distances between landmarks on tooth surfaces. The computed error in corresponding point distances between intraoral scan data and CBCT data in the automatically registered datasets utilizing the proposed technique is quantified at 0.234 ± 0.019 mm, which is significantly below the 0.3 mm CBCT voxel size. Moreover, the average measurement discrepancy among expert-identified landmarks ranges from 0.368 to 1.079 mm, underscoring the promise of the proposed method.

Association with Combined Occupational Hazards Exposure and Risk of Metabolic Syndrome: A Workers' Health Examination Cohort 2012-2021.

Background: This study aimed to evaluate the association between exposure to occupational hazards and the metabolic syndrome. A secondary objective was to analyze the additive and multiplicative effects of exposure to risk factors. Methods: This retrospective cohort was based on 31,615 health examinees at the Pusan National University Yangsan Hospital in Republic of Korea from 2012-2021. Demographic and behavior-related risk factors were treated as confounding factors, whereas three physical factors, 19 organic solvents and aerosols, and 13 metals and dust were considered occupational risk factors. Time-dependent Cox regression analysis was used to calculate hazard ratios. Results: The risk of metabolic syndrome was significantly higher in night shift workers (hazard ratio = 1.45: 95% confidence interval = 1.36-1.54) and workers who were exposed to noise (1.15:1.07-1.24). Exposure to some other risk factors was also significantly associated with a higher risk of metabolic syndrome. They were dimethylformamide, acetonitrile, trichloroethylene, xylene, styrene, toluene, dichloromethane, copper, antimony, lead, copper, iron, welding fume, and manganese. Among the 28 significant pairs, 19 exhibited both positive additive and multiplicative effects. Conclusions: Exposure to single or combined occupational risk factors may increase the risk of developing metabolic syndrome. Working conditions should be monitored and improved to reduce exposure to occupational hazards and prevent the development of the metabolic syndrome.

The prediction of sagittal chin point relapse following two-jaw surgery using machine learning.

The study aimed to identify critical factors associated with the surgical stability of pogonion (Pog) by applying machine learning (ML) to predict relapse following two-jaw orthognathic surgery (2 J-OGJ). The sample set comprised 227 patients (110 males and 117 females, 207 training and 20 test sets). Using lateral cephalograms taken at the initial evaluation (T0), pretreatment (T1), after (T2) 2 J-OGS, and post treatment (T3), 55 linear and angular skeletal and dental surgical movements (T2-T1) were measured. Six ML modes were utilized, including classification and regression trees (CART), conditional inference tree (CTREE), and random forest (RF). The training samples were classified into three groups; highly significant (HS) (≥ 4), significant (S) (≥ 2 and < 4), and insignificant (N), depending on Pog relapse. RF indicated that the most important variable that affected relapse rank prediction was ramus inclination (RI), CTREE and CART revealed that a clockwise rotation of more than 3.7 and 1.8 degrees of RI was a risk factor for HS and S groups, respectively. RF, CTREE, and CART were practical tools for predicting surgical stability. More than 1.8 degrees of CW rotation of the ramus during surgery would lead to significant Pog relapse.

Orthognathic surgical planning using graph CNN with dual embedding module: External validations with multi-hospital datasets.

BACKGROUND AND OBJECTIVE: Despite recent development of AI, prediction of the surgical movement in the maxilla and mandible by OGS might be more difficult than that of tooth movement by orthodontic treatment. To evaluate the prediction accuracy of the surgical movement using pairs of pre-(T0) and post-surgical (T1) lateral cephalograms (lat-ceph) of orthognathic surgery (OGS) patients and dual embedding module-graph convolution neural network (DEM-GCNN) model. METHODS: 599 pairs from 3 institutions were used as training, internal validation, and internal test sets and 201 pairs from other 6 institutions were used as external test set. DEM-GCNN model (IEM, learning the lat-ceph images; LTEM, learning the landmarks) was developed to predict the amount and direction of surgical movement of ANS and PNS in the maxilla and B-point and Md1crown in the mandible. The distance between T1 landmark coordinates actually moved by OGS (ground truth) and predicted by DEM-GCNN model and pre-existed CNN-based Model-C (learning the lat-ceph images) was compared. RESULTS: In both internal and external tests, DEM-GCNN did not exhibit significant difference from ground truth in all landmarks (ANS, PNS, B-point, Md1crown, all P > 0.05). When the accumulated successful detection rate for each landmark was compared, DEM-GCNN showed higher values than Model-C in both the internal and external tests. In violin plots exhibiting the error distribution of the prediction results, both internal and external tests showed that DEM-GCNN had significant performance improvement in PNS, ANS, B-point, Md1crown than Model-C. DEM-GCNN showed significantly lower prediction error values than Model-C (one-jaw surgery, B-point, Md1crown, all P < 0.005; two-jaw surgery, PNS, ANS, all P < 0.05; B point, Md1crown, all P < 0.005). CONCLUSION: We developed a robust OGS planning model with maximized generalizability despite diverse qualities of lat-cephs from 9 institutions.

Occupational asthma related to indoor air pollution in a worker at an indoor air gun shooting range: a case report.

Background: Indoor air pollution can cause and exacerbate asthma. We report a previously undescribed case of occupational asthma related to indoor air pollution in a worker at an indoor air gun shooting range and highlight the potential risk of developing occupational asthma in this environment. Case presentation: A 31-year-old man presented with dyspnea, cough, and sputum and was diagnosed with asthma complicated by pneumonia. Objective evidence of asthma was obtained by performing a methacholine bronchial provocation test. It was suspected that the patient had occupational asthma, which began one month after changing jobs to work within the indoor air gun shooting range. The highest peak expiratory flow (PEF) diurnal variability on working days was 15%, but the highest variation was 24%, with 4 days out of 4 weeks having a variation of over 20% related to workplace exposure. Conversely, the diurnal variability on the rest days was 7%, and no day showed a variation exceeding 20%. The difference in the average PEF between working and rest days was 52 L/min. PEF deterioration during working days and improvement on rest days were noted. Conclusions: The results obtained from the in-depth analysis of the PEF were adequate to diagnose the patient with occupational asthma. Exposure to indoor air pollution and lead and the patient's atopy and allergic rhinitis may have contributed to the development of occupational asthma.

Incidence rates of injury, musculoskeletal, skin, pulmonary and chronic diseases among construction workers by classification of occupations in South Korea: a 1,027 subject-based cohort of the Korean Construction Worker's Cohort (KCWC).

Background: The objective of this study is to investigate the differences in incidence rates of targeted diseases by classification of occupations among construction workers in Korea. Methods: In a subject-based cohort of the Korean Construction Worker's Cohort, we surveyed a total of 1,027 construction workers. As occupational exposure, the classification of occupations was developed using two axes: construction business and job type. To analyze disease incidence, we linked survey data with National Health Insurance Service data. Eleven target disease categories with high prevalence or estimated work-relatedness among construction workers were evaluated in our study. The average incidence rates were calculated as cases per 1,000 person-years (PY). Results: Injury, poisoning, and certain other consequences of external causes had the highest incidence rate of 344.08 per 1,000 PY, followed by disease of the musculoskeletal system and connective tissue for 208.64 and diseases of the skin and subcutaneous tissue for 197.87 in our cohort. We especially found that chronic obstructive pulmonary disease was more common in construction painters, civil engineering welders, and civil engineering frame mold carpenters, asthma in construction painters, landscape, and construction water proofers, interstitial lung diseases in construction water proofers. Conclusions: This is the first study to systematically classify complex construction occupations in order to analyze occupational diseases in Korean construction workers. There were differences in disease incidences among construction workers based on the classification of occupations. It is necessary to develop customized occupational safety and health policies for high-risk occupations for each disease in the construction industry.

Three-Dimensional Quantitative Assessment of Condylar Displacement and Adaptive Remodeling in Asymmetrical Mandibular Prognathism Patients After Sagittal Split Ramus Osteotomy.

This study was performed to evaluate the condylar displacement and associated condylar remodeling in class III patients following mandibular setback surgery via sagittal split ramus osteotomy (SSRO). The sample comprised of 26 condyles of 13 subjects (mean age of 21.2±2.6 y). We evaluated patients with mandibular prognathism and facial asymmetry who had undergone SSRO for mandibular setback at Korea University Hospital between January 2016 and December 2018. Three-dimensional segmentation of the mandibular condyles was done using the initial cone-beam computed tomography scan and scan taken 12 months postoperatively or later. Quantitative assessments of the 3-dimensional condylar displacement from T0 to T1 and bony remodeling of 8 regions of the condylar head were performed. The correlation between the condylar displacement and condylar head remodeling on the deviated (D) and nondeviated (ND) sides was analyzed. Significant correlations between condylar displacement and surface remodeling were observed in both D and ND condyles. The anteroposterior condylar displacement was significantly different between the D and ND sides (P=0.007). There was no significant difference in condylar remodeling between the 2 sides. Condylar displacement and adaptive remodeling after SSRO varied greatly among individuals. Compared with displacement in the ND condyle, displacement in the D condyle has a greater association with condylar remodeling in both D and ND condyles. There is no significant difference in condylar head remodeling between D and ND condyles.

Epidemiology of occupational injuries in construction workers between 2009 and 2018 in South Korea.

BACKGROUND: This study aimed to investigate the characteristics of occupational injuries based on fatality, sex, and classification of occupations among construction workers using workers' compensation (WC) insurance data in South Korea. METHODS: We collected WC insurance data from the Korea Workers' Compensation & Welfare Service for all construction workers between 2009 and 2018. Data from 158,947 accepted claims for occupational injury were extracted, and the demographic features, occupational injury types, and annual trends were analyzed for fatal and nonfatal cases. The annual incidence and mortality trends of occupational injury were estimated using negative binomial regression and Poisson regression models, for injury incidence and mortality respectively. RESULTS: Among a total of 158,947 occupational injury cases, there were 155,772 (98%) nonfatal injuries and 3175 (2%) fatal injuries. For all occupational injuries, Construction Elementary Workers (6th Korean Standard Classification of Occupations (KSCO) 910; 45.7%) was the most frequent occupation, followed by Construction-Related Technical Workers (6th KSCO 772; 39.2%). The most frequent injury type was a fracture, followed by ruptures or lacerations and contusions. The incidence of all occupational injuries increased from 700.36 per 100,000 persons in 2009 to 1,195.98 per 100,000 persons in 2018. Further, deaths from injuries at work followed a significantly increasing annual trend [mortality rate ratio 1.04 (95% CI: 1.03-1.05)] from 2009 to 2018. CONCLUSION: The over two-thirds increased incidence of occupational injuries and significantly increasing mortality trends for occupational injuries during the last 10 years indicate the need for aggressive intervention in occupational safety and health management within the Korean construction industry.

Genetic Characteristics of Canine Adenovirus Type 2 Detected in Wild Raccoon Dogs (Nyctereutes procyonoides) in Korea (2017-2020).

Adenovirus has been detected in a wide range of hosts like dogs, foxes, horses, bats, avian animals, and raccoon dogs. Canine adenoviruses with two serotypes host mammals and are members of the mastadenovirus family. Canine adenovirus type 1 (CAdV-1) and canine adenovirus type 2 (CAdV-2) cause infectious canine hepatitis and infectious bronchial disease, respectively. In this study, we investigated the prevalence of CAdV-1 and 2 in wild Nyctereutes procyonoides in Korea in 2017-2020 from 414 tissue samples, including the liver, kidney, lung, and intestine, collected from 105 raccoon dog carcasses. Only CAdV-2 was detected in two raccoon dogs, whereas CAdV-1 was not detected. Tissue samples from raccoon dogs were screened for CAdV-1 and CAdV-2 using conventional PCR. Adenovirus was successfully isolated from PCR positive samples using the Vero cell line, and the full-length gene sequence of the isolated viruses was obtained through 5' and 3' rapid amplification of cDNA ends (RACE). The major genes of the isolated CAdV-2/18Ra54 and CAdV-2/18Ra-65 strains showed the closest relationship with that of the CAdV-2 Toronto A26/61 strain isolated from Canada in 1976. There is no large mutation between CAdV-2, which is prevalent worldwide, and CAdV-2, which is prevalent in wild animals in Korea. In addition, it is still spreading and causing infections. The Toronto A26/61 strain, which showed the most similarity to CAdV-2/18Ra-54, was likely transmitted to wild animals through vaccinated companion animals, suggesting that further research is needed on safety measures surrounding animal vaccination. This study provides information on the genetic characteristics and prevalence of canine adenovirus in domestic wild animals and provides a better understanding of canine adenovirus.

Fully automated condyle segmentation using 3D convolutional neural networks.

The aim of this study was to develop an auto-segmentation algorithm for mandibular condyle using the 3D U-Net and perform a stress test to determine the optimal dataset size for achieving clinically acceptable accuracy. 234 cone-beam computed tomography images of mandibular condyles were acquired from 117 subjects from two institutions, which were manually segmented to generate the ground truth. Semantic segmentation was performed using basic 3D U-Net and a cascaded 3D U-Net. A stress test was performed using different sets of condylar images as the training, validation, and test datasets. Relative accuracy was evaluated using dice similarity coefficients (DSCs) and Hausdorff distance (HD). In the five stages, the DSC ranged 0.886-0.922 and 0.912-0.932 for basic 3D U-Net and cascaded 3D U-Net, respectively; the HD ranged 2.557-3.099 and 2.452-2.600 for basic 3D U-Net and cascaded 3D U-Net, respectively. Stage V (largest data from two institutions) exhibited the highest DSC of 0.922 ± 0.021 and 0.932 ± 0.023 for basic 3D U-Net and cascaded 3D U-Net, respectively. Stage IV (200 samples from two institutions) had a lower performance than stage III (162 samples from one institution). Our results show that fully automated segmentation of mandibular condyles is possible using 3D U-Net algorithms, and the segmentation accuracy increases as training data increases.

Automated analysis of three-dimensional CBCT images taken in natural head position that combines facial profile processing and multiple deep-learning models.

BACKGROUND AND OBJECTIVES: Analyzing three-dimensional cone beam computed tomography (CBCT) images has become an indispensable procedure for diagnosis and treatment planning of orthodontic patients. Artificial intelligence, especially deep-learning techniques for analyzing image data, shows great potential for medical and dental image analysis and diagnosis. To explore the feasibility of automating measurement of 13 geometric parameters from three-dimensional cone beam computed tomography images taken in natural head position (NHP), this study proposed a smart system that combined a facial profile analysis algorithm with deep-learning models. MATERIALS AND METHODS: Using multiple views extracted from the cone beam computed tomography data of 170 cases as a dataset, our proposed method automatically calculated 13 dental parameters by partitioning, detecting regions of interest, and extracting the facial profile. Subsequently, Mask-RCNN, a trained decentralized convolutional neural network was applied to detect 23 landmarks. All the techniques were integrated into a software application with a graphical user interface designed for user convenience. To demonstrate the system's ability to replace human experts, 30 CBCT data were selected for validation. Two orthodontists and one advanced general dentist located required landmarks by using a commercial dental program. The differences between manual and developed methods were calculated and reported as the errors. RESULTS: The intraclass correlation coefficients (ICCs) and 95% confidence interval (95% CI) for intra-observer reliability were 0.98 (0.97-0.99) for observer 1; 0.95 (0.93-0.97) for observer 2; 0.98 (0.97-0.99) for observer 3 after measuring 13 parameters two times at two weeks interval. The combined ICC for intra-observer reliability was 0.97. The ICCs and 95% CI for inter-observer reliability were 0.94 (0.91-0.97). The mean absolute value of deviation was around 1 mm for the length parameters, and smaller than 2° for angle parameters. Furthermore, ANOVA test demonstrated the consistency between the measurements of the proposed method and those of human experts statistically (Fdis=2.68, ɑ=0.05). CONCLUSIONS: The proposed system demonstrated the high consistency with the manual measurements of human experts and its applicability. This method aimed to help human experts save time and efforts for analyzing three-dimensional CBCT images of orthodontic patients.

Relationship between of working hours, weekend work, and shift work and work-family conflicts among Korean manufacturers.

Background: In the manufacturing industry, work-family conflict (WFC) is related to working hour characteristics. Earlier studies on the relationship between working hour characteristics and WFC in the manufacturing industry have been limited to some regions in Korea. No study has addressed the data on a national scale. Thus, this study investigated the impact of weekly working hours, weekend work, and shift work on WFC using national-scale data. Methods: This study was based on the fifth Korean Working Conditions Survey of 5,432 manufacturers. WFC consists of 5 variables; WFC1 "kept worrying about work"; WFC2 "felt too tired after work"; WFC3 "work prevented time for family"; WFC4 "difficult to concentrate on work"; WFC5 "family responsibilities prevented time for work". As WFC refers to the inter-role conflict between the need for paid work and family work, WFC has been measured in two directions, work to family conflict (WTFC: WFC1, 2, 3) and family to work conflict (FTWC: WFC4, 5). With these WFC variables, we conducted multiple logistic analyses to study how working hours, weekend work, and shift work impact WFC. Results: Korean manufacturers' prolonged working hours increased all aspects of WFCs. Odds ratios (ORs) of WFCs based on working hours (reference of under 40 hours) of 41-52, 53-60, over 61 were 1.247, 1.611, 2.279 (WFC1); 1.111, 2.561, 6.442 (WFC2); 1.219, 3.495, 8.327 (WFC3); 1.076, 2.019, 2.656 (WFC4); and 1.166, 1.592, 1.946 (WFC5), respectively. Shift-work in the WFC2 model showed a significantly higher OR of 1.390. Weekend work 'only on Saturday' had significant ORs with WFC2 (1.323) and WFC3 (1.552). Conclusions: An increase in working hours leads to the spending of less time attending to problems between work and family, causing both WTFC and FTWC to increase. As weekends, evenings, and nighttime are considered to be family-friendly to people, working on weekends and shift-work were highly correlated to WTFC.

Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis.

BACKGROUND: Artificial intelligence (AI) algorithms have been applied to diagnose temporomandibular disorders (TMDs). However, studies have used different patient selection criteria, disease subtypes, input data, and outcome measures. Resultantly, the performance of the AI models varies. OBJECTIVE: This study aimed to systematically summarize the current literature on the application of AI technologies for diagnosis of different TMD subtypes, evaluate the quality of these studies, and assess the diagnostic accuracy of existing AI models. MATERIALS AND METHODS: The study protocol was carried out based on the preferred reporting items for systematic review and meta-analysis protocols (PRISMA). The PubMed, Embase, and Web of Science databases were searched to find relevant articles from database inception to June 2022. Studies that used AI algorithms to diagnose at least one subtype of TMD and those that assessed the performance of AI algorithms were included. We excluded studies on orofacial pain that were not directly related to the TMD, such as studies on atypical facial pain and neuropathic pain, editorials, book chapters, and excerpts without detailed empirical data. The risk of bias was assessed using the QUADAS-2 tool. We used Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) to provide certainty of evidence. RESULTS: A total of 17 articles for automated diagnosis of masticatory muscle disorders, TMJ osteoarthrosis, internal derangement, and disc perforation were included; they were retrospective studies, case-control studies, cohort studies, and a pilot study. Seven studies were subjected to a meta-analysis for diagnostic accuracy. According to the GRADE, the certainty of evidence was very low. The performance of the AI models had accuracy and specificity ranging from 84% to 99.9% and 73% to 100%, respectively. The pooled accuracy was 0.91 (95% CI 0.76-0.99), I2 = 97% (95% CI 0.96-0.98), p < 0.001. CONCLUSIONS: Various AI algorithms developed for diagnosing TMDs may provide additional clinical expertise to increase diagnostic accuracy. However, it should be noted that a high risk of bias was present in the included studies. Also, certainty of evidence was very low. Future research of higher quality is strongly recommended.

Accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent orthodontic treatment and two-jaw orthognathic surgery.

Objective: To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery. Methods: A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed. Results: The total mean error was 1.17 mm without significant difference among the four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups. Conclusions: The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.