Purpose: Recently, deep learning techniques have been introduced for age estimation, with automated methods based on radiographic analysis demonstrating high accuracy. In this study, we applied convolutional neural network (CNN) techniques to the lower dentition area on orthopantomograms (OPGs) of children to develop an automated age estimation model and evaluate its accuracy for use in forensic dentistry. Methods: In this study, OPGs of 2,856 subjects aged 3-14 years were analyzed. The You Only Look Once (YOLO) V8 object detection technique was applied to extract the mandibular dentition area on OPGs, designating it as the region of interest (ROI). First, 200 radiographs were randomly selected, and were used to train a model for extracting the ROI. The trained model was then applied to the entire dataset. For the CNN image classification task, 80% of OPGs were allocated to the training set, while the remaining 20% were used as the test set. A transfer learning approach was employed using the ResNet50 and VGG19 backbone models, with an ensemble technique combining these models to improve performance. The mean absolute error (MAE) on the test set was used as the validation metric, and the model with the lowest MAE was selected. Results: In this study, the age estimation model developed using mandibular dentition region from OPGs achieved MAE and root mean squared error (RMSE) values of 0.501 and 0.742, respectively, on the test set, and MAE and RMSE values of 0.273 and 0.354, respectively, on the training set. Conclusions The automated age estimation model developed in this study demonstrated accuracy comparable to that of previous research and shows potential for applications in forensic investigations. Increasing the sample size and incorporating diverse deep learning techniques are expected to further enhance the accuracy of future age estimation models.

In children and adolescents, dental age estimation is performed with the development of the teeth. Various statistical analysis methods have been used to determine the relationship between age and dental maturity and develop an accurate method of age calculation. This study attempted to apply a neural network model for the statistical analysis of dental age estimation in children and evaluated its applicability. This study used 1196 panoramic radiographs of patients aged 3–16 years, and 996 and 200 were randomly classified into training and test sets, respectively. The dental maturity of the mandibular left teeth was evaluated using Demirjian's method, the neural network model using the backpropagation algorithm was derived using training sets, and the errors were evaluated using 100 radiographs of each male and female as test sets. In addition, multiple linear regression analysis was conducted on the same training set, and the error was calculated by applying it to the test set and comparing it with the error of the neural network model. In the neural network model, the mean absolute error (MAE) and root mean squared error (RMSE) were 0.589 and 0.783 in male subjects and 0.529 and 0.760 in female subjects, respectively. In the multiple linear regression model, the MAE and RMSE were 0.600 and 0.748 in male subjects and 0.566 and 0.789 in female subjects, respectively. When applying the neural network model to the statistical analysis of the dental developmental stage, the results were as accurate as those of conventional statistical analysis methods. This study’s approach is expected to be useful for estimating the ages of children.

Background: Chronic kidney disease (CKD) has a negative impact on growth and development in children and is a risk factor for neurocognitive impairment; however, there is limited research on the cognitive function of children and adolescents with CKD. This study therefore aimed to investigate the mean intelligence and risk factors for low intelligence in children and adolescents with CKD. Methods: Eighty-one patients with CKD under 18 years old were included in the KoreaN cohort study for Outcomes in patients With Pediatric Chronic Kidney Disease (KNOW-Ped CKD). Participants completed either the Wechsler Intelligence Scale for Children (6–16 years), or Wechsler Adult Intelligence Scale (> 16 years). Results: The mean full-scale intelligence quotient (IQ) was 91 ± 19; 24.7% of participants scored a full-scale IQ below 80. Participants with a short stature (height Z scores < −1.88), failure to thrive (weight Z scores < −1.65), more severe CKD stage (≥ IIIb), longer duration of CKD (≥ 5 years), and those who were Medicare or Medicaid beneficiaries, had significantly lower mean full-scale IQs. Conclusion On linear regression analysis, the association between the full-scale IQ, and longer duration of CKD and growth failure, remained significant after controlling for demographic and clinical variables. It is therefore necessary to investigate cognitive impairment in pediatric patients with CKD who exhibit growth failure or for a longer postmorbid period. It is believed that early interventions, such as kidney transplantation, will have a positive effect on IQ in children with CKD, as the disease negatively affects IQ due to poor glomerular filtration rate over time.

Background: Chronic kidney disease (CKD) has a negative impact on growth and development in children and is a risk factor for neurocognitive impairment; however, there is limited research on the cognitive function of children and adolescents with CKD. This study therefore aimed to investigate the mean intelligence and risk factors for low intelligence in children and adolescents with CKD. Methods: Eighty-one patients with CKD under 18 years old were included in the KoreaN cohort study for Outcomes in patients With Pediatric Chronic Kidney Disease (KNOW-Ped CKD). Participants completed either the Wechsler Intelligence Scale for Children (6–16 years), or Wechsler Adult Intelligence Scale (> 16 years). Results: The mean full-scale intelligence quotient (IQ) was 91 ± 19; 24.7% of participants scored a full-scale IQ below 80. Participants with a short stature (height Z scores < −1.88), failure to thrive (weight Z scores < −1.65), more severe CKD stage (≥ IIIb), longer duration of CKD (≥ 5 years), and those who were Medicare or Medicaid beneficiaries, had significantly lower mean full-scale IQs. Conclusion On linear regression analysis, the association between the full-scale IQ, and longer duration of CKD and growth failure, remained significant after controlling for demographic and clinical variables. It is therefore necessary to investigate cognitive impairment in pediatric patients with CKD who exhibit growth failure or for a longer postmorbid period. It is believed that early interventions, such as kidney transplantation, will have a positive effect on IQ in children with CKD, as the disease negatively affects IQ due to poor glomerular filtration rate over time.

Background: and Purpose We aimed to develop a model predicting early recanalization after intravenous tissue plasminogen activator (t-PA) treatment in large-vessel occlusion. Methods: Using data from two different multicenter prospective cohorts, we determined the factors associated with early recanalization immediately after t-PA in stroke patients with large-vessel occlusion, and developed and validated a prediction model for early recanalization. Clot volume was semiautomatically measured on thin-section computed tomography using software, and the degree of collaterals was determined using the Tan score. Follow-up angiographic studies were performed immediately after t-PA treatment to assess early recanalization. Results: Early recanalization, assessed 61.0±44.7 minutes after t-PA bolus, was achieved in 15.5% (15/97) in the derivation cohort and in 10.5% (8/76) in the validation cohort. Clot volume (odds ratio [OR], 0.979; 95% confidence interval [CI], 0.961 to 0.997; P=0.020) and good collaterals (OR, 6.129; 95% CI, 1.592 to 23.594; P=0.008) were significant factors associated with early recanalization. The area under the curve (AUC) of the model including clot volume was 0.819 (95% CI, 0.720 to 0.917) and 0.842 (95% CI, 0.746 to 0.938) in the derivation and validation cohorts, respectively. The AUC improved when good collaterals were added (derivation cohort: AUC, 0.876; 95% CI, 0.802 to 0.950; P=0.164; validation cohort: AUC, 0.949; 95% CI, 0.886 to 1.000; P=0.036). The integrated discrimination improvement also showed significantly improved prediction (0.097; 95% CI, 0.009 to 0.185; P=0.032). Conclusions The model using clot volume and collaterals predicted early recanalization after intravenous t-PA and had a high performance. This model may aid in determining the recanalization treatment strategy in stroke patients with large-vessel occlusion.

Background: and Purpose We aimed to develop a model predicting early recanalization after intravenous tissue plasminogen activator (t-PA) treatment in large-vessel occlusion. Methods: Using data from two different multicenter prospective cohorts, we determined the factors associated with early recanalization immediately after t-PA in stroke patients with large-vessel occlusion, and developed and validated a prediction model for early recanalization. Clot volume was semiautomatically measured on thin-section computed tomography using software, and the degree of collaterals was determined using the Tan score. Follow-up angiographic studies were performed immediately after t-PA treatment to assess early recanalization. Results: Early recanalization, assessed 61.0±44.7 minutes after t-PA bolus, was achieved in 15.5% (15/97) in the derivation cohort and in 10.5% (8/76) in the validation cohort. Clot volume (odds ratio [OR], 0.979; 95% confidence interval [CI], 0.961 to 0.997; P=0.020) and good collaterals (OR, 6.129; 95% CI, 1.592 to 23.594; P=0.008) were significant factors associated with early recanalization. The area under the curve (AUC) of the model including clot volume was 0.819 (95% CI, 0.720 to 0.917) and 0.842 (95% CI, 0.746 to 0.938) in the derivation and validation cohorts, respectively. The AUC improved when good collaterals were added (derivation cohort: AUC, 0.876; 95% CI, 0.802 to 0.950; P=0.164; validation cohort: AUC, 0.949; 95% CI, 0.886 to 1.000; P=0.036). The integrated discrimination improvement also showed significantly improved prediction (0.097; 95% CI, 0.009 to 0.185; P=0.032). Conclusions The model using clot volume and collaterals predicted early recanalization after intravenous t-PA and had a high performance. This model may aid in determining the recanalization treatment strategy in stroke patients with large-vessel occlusion.

PURPOSE: This study was conducted to discover the clinical factors that can predict pathologically complete remission (pCR) after neoadjuvant chemoradiotherapy (CRT), so that those factors may help in deciding on a treatment program for patients with locally advanced rectal cancer. METHODS: A total of 137 patients with locally advanced rectal cancer were retrospectively enrolled in this study, and data were collected retrospectively. The patients had undergone a total mesorectal excision after neoadjuvant CRT. Histologic response was categorized as pCR vs. non-pCR. The tumor area was defined as (tumor length) × (maximum tumor depth). The difference in tumor area was defined as pre-CRT tumor area – post-CRT tumor area. Univariate and multivariate logistic regression analyses were conducted to find the factors affecting pCR. A P-value < 0.05 was considered significant. RESULTS: Twenty-three patients (16.8%) achieved pCR. On the univariate analysis, endoscopic tumor circumferential rate <50%, low pre-CRT T & N stage, low post-CRT T & N stage, small pretreatment tumor area, and large difference in tumor area before and after neoadjuvant CRT were predictive factors of pCR. A multivariate analysis found that only the difference in tumor area before and after neoadjuvant CRT was an independent predictor of pCR (P < 0.001). CONCLUSION: The difference in tumor area, as determined using radiologic tools, before and after neoadjuvant CRT may be important predictor of pCR. This clinical factor may help surgeons to determine which patients who received neoadjuvant CRT for locally advanced rectal cancer should undergo surgery.
Chemoradiotherapy ; Humans ; Logistic Models ; Multivariate Analysis ; Polymerase Chain Reaction ; Rectal Neoplasms* ; Retrospective Studies ; Surgeons

BACKGROUND AND PURPOSE: To compare the efficacy and safety of antiplatelet agents for the secondary prevention of ischemic stroke based on cytochrome P450 2C19 (CYP2C19) polymorphisms. METHODS: This study was a prospective, multicenter, randomized, parallel-group, open-label, blind genotype trial. First time non-cardiogenic ischemic stroke patients were enrolled and screened within 30 days. Participants were randomized to receive either triflusal or clopidogrel for secondary stroke prevention. The primary outcome was the time from randomization to first recurrent ischemic stroke or hemorrhagic stroke. RESULTS: The required sample size was 1,080 but only 784 (73%) participants were recruited. In patients with a poor CYP2C19 genotype for clopidogrel metabolism (n=484), the risk of recurrent stroke among those who received triflusal treatment was 2.9% per year, which was not significantly different from those who received clopidogrel treatment (2.2% per year; hazard ratio [HR], 1.23; 95% confidence interval [CI], 0.60–2.53). In the clopidogrel treatment group (n=393), 38% had good genotypes and 62% poor genotypes for clopidogrel metabolism. The risk of recurrent stroke in patients with a good CYP2C19 genotype was 1.6% per year, which was not significantly different from those with a poor genotype (2.2% per year; HR, 0.69; 95% CI, 0.26–1.79). CONCLUSIONS: Whilst there were no significant differences between the treatment groups in the rates of stroke recurrence, major vascular events, or coronary revascularization, the efficacy of antiplatelet agents for the secondary prevention of stroke according to CYP2C19 genotype status remains unclear.
Cytochrome P-450 CYP2C19 ; Cytochrome P-450 Enzyme System* ; Cytochromes* ; Genotype ; Humans ; Metabolism ; Platelet Aggregation Inhibitors ; Prospective Studies ; Random Allocation ; Recurrence ; Sample Size ; Secondary Prevention* ; Stroke*

Nephronophthisis-related ciliopathy (NPHP-RC) is a common genetic cause of end-stage renal failure during childhood and adolescence and exhibits an autosomal recessive pattern of inheritance. Genetic diagnosis is quite limited owing to genetic heterogeneity in NPHP-RC. We designed a novel approach involving the step-wise screening of Sanger sequencing and targeted exome sequencing for the genetic diagnosis of 55 patients with NPHP-RC. First, five NPHP-RC genes were analyzed by Sanger sequencing in phenotypically classified patients. Known pathogenic mutations were identified in 12 patients (21.8%); homozygous deletions of NPHP1 in 4 juvenile nephronophthisis patients, IQCB1/NPHP5 mutations in 3 Senior–Løken syndrome patients, a CEP290/NPHP6 mutation in 1 Joubert syndrome patient, and TMEM67/MKS3 mutations in 4 Joubert syndrome patients with liver involvement. In the remaining undiagnosed patients, we applied targeted exome sequencing of 34 ciliopathy-related genes to detect known pathogenic mutations in 7 (16.3%) of 43 patients. Another 18 likely damaging heterozygous variants were identified in 13 NPHP-RC genes in 18 patients. In this study, we report a variety of pathogenic and candidate mutations identified in 55 patients with NPHP-RC in Korea using a step-wise application of two genetic tests. These results support the clinical utility of targeted exome sequencing to resolve the issue of allelic and genetic heterogeneity in NPHP-RC.
Adolescent ; Diagnosis* ; Exome* ; Genetic Heterogeneity* ; Humans ; Kidney Failure, Chronic ; Korea ; Liver ; Mass Screening ; Wills

PURPOSE: In this study, we investigated the stroke mechanism and the factors associated with ischemic stroke in patients with nonvalvular atrial fibrillation (NVAF) who were on optimal oral anticoagulation with warfarin. MATERIALS AND METHODS: This was a multicenter case-control study. The cases were consecutive patients with NVAF who developed cerebral infarction or transient ischemic attack (TIA) while on warfarin therapy with an international normalized ratio (INR) > or =2 between January 2007 and December 2011. The controls were patients with NVAF without ischemic stroke who were on warfarin therapy for more than 1 year with a mean INR > or =2 during the same time period. We also determined etiologic mechanisms of stroke in cases. RESULTS: Among 3569 consecutive patients with cerebral infarction or TIA who had NVAF, 55 (1.5%) patients had INR > or =2 at admission. The most common stroke mechanism was cardioembolism (76.0%). Multivariate analysis demonstrated that smoking and history of previous ischemic stroke were independently associated with cases. High CHADS2 score (> or =3) or CHA2DS2-VASc score (> or =5), in particular, with previous ischemic stroke along with > or =1 point of other components of CHADS2 score or > or =3 points of other components of CHA2DS2-VASc score was a significant predictor for development of ischemic stroke. CONCLUSION: NVAF patients with high CHADS2/CHA2DS2-VASc scores and a previous ischemic stroke or smoking history are at high risk of stroke despite optimal warfarin treatment. Some other measures to reduce the risk of stroke would be necessary in those specific groups of patients.
Aged ; Aged, 80 and over ; Anticoagulants/adverse effects/*therapeutic use ; Atrial Fibrillation/*complications ; Cardiovascular Diseases ; Case-Control Studies ; Cerebral Infarction/complications ; Female ; Humans ; Male ; Middle Aged ; Multivariate Analysis ; Risk Factors ; Stroke/etiology/*prevention & control ; Warfarin/adverse effects/*therapeutic use