Objective: The purpose of this study is to identify the types of pain changes that affect older Koreans, as well as their effects on depressive symptom. Methods: We analyzed the Korean Longitudinal Study of Aging data collected from 2010 to 2018. A data of total of 1,359 participants, aged 65 or older were used to estimate the change in pain. A latent growth model and growth mixture modeling were performed to estimate the overall change in pain and to categorize the types of pain changes. Results: The pain changes of older adults were classified into two categories: low-stable and high increasing. The depressive symptom showed a stronger relationship among the high-increasing type of pain than the low-stable type. The high-increasing type had a higher percentage of females, lower income, relatively low educational attainment, and a higher percentage of rural residents than the low-stable type. Conclusion The significance of this study is that it reiterated the importance of early pain diagnosis and intervention by identifying the types of pain changes in older adults and analyzing their effects on depressive symptoms. Therefore, it is especially important to pay attention to interventions that are designed to help vulnerable groups with a high risk of pain obtain effective pain management.

Objective: The purpose of this study is to identify the types of pain changes that affect older Koreans, as well as their effects on depressive symptom. Methods: We analyzed the Korean Longitudinal Study of Aging data collected from 2010 to 2018. A data of total of 1,359 participants, aged 65 or older were used to estimate the change in pain. A latent growth model and growth mixture modeling were performed to estimate the overall change in pain and to categorize the types of pain changes. Results: The pain changes of older adults were classified into two categories: low-stable and high increasing. The depressive symptom showed a stronger relationship among the high-increasing type of pain than the low-stable type. The high-increasing type had a higher percentage of females, lower income, relatively low educational attainment, and a higher percentage of rural residents than the low-stable type. Conclusion The significance of this study is that it reiterated the importance of early pain diagnosis and intervention by identifying the types of pain changes in older adults and analyzing their effects on depressive symptoms. Therefore, it is especially important to pay attention to interventions that are designed to help vulnerable groups with a high risk of pain obtain effective pain management.

Objective: The purpose of this study is to identify the types of pain changes that affect older Koreans, as well as their effects on depressive symptom. Methods: We analyzed the Korean Longitudinal Study of Aging data collected from 2010 to 2018. A data of total of 1,359 participants, aged 65 or older were used to estimate the change in pain. A latent growth model and growth mixture modeling were performed to estimate the overall change in pain and to categorize the types of pain changes. Results: The pain changes of older adults were classified into two categories: low-stable and high increasing. The depressive symptom showed a stronger relationship among the high-increasing type of pain than the low-stable type. The high-increasing type had a higher percentage of females, lower income, relatively low educational attainment, and a higher percentage of rural residents than the low-stable type. Conclusion The significance of this study is that it reiterated the importance of early pain diagnosis and intervention by identifying the types of pain changes in older adults and analyzing their effects on depressive symptoms. Therefore, it is especially important to pay attention to interventions that are designed to help vulnerable groups with a high risk of pain obtain effective pain management.

Objective: The purpose of this study is to identify the types of pain changes that affect older Koreans, as well as their effects on depressive symptom. Methods: We analyzed the Korean Longitudinal Study of Aging data collected from 2010 to 2018. A data of total of 1,359 participants, aged 65 or older were used to estimate the change in pain. A latent growth model and growth mixture modeling were performed to estimate the overall change in pain and to categorize the types of pain changes. Results: The pain changes of older adults were classified into two categories: low-stable and high increasing. The depressive symptom showed a stronger relationship among the high-increasing type of pain than the low-stable type. The high-increasing type had a higher percentage of females, lower income, relatively low educational attainment, and a higher percentage of rural residents than the low-stable type. Conclusion The significance of this study is that it reiterated the importance of early pain diagnosis and intervention by identifying the types of pain changes in older adults and analyzing their effects on depressive symptoms. Therefore, it is especially important to pay attention to interventions that are designed to help vulnerable groups with a high risk of pain obtain effective pain management.

Objective: The purpose of this study is to identify the types of pain changes that affect older Koreans, as well as their effects on depressive symptom. Methods: We analyzed the Korean Longitudinal Study of Aging data collected from 2010 to 2018. A data of total of 1,359 participants, aged 65 or older were used to estimate the change in pain. A latent growth model and growth mixture modeling were performed to estimate the overall change in pain and to categorize the types of pain changes. Results: The pain changes of older adults were classified into two categories: low-stable and high increasing. The depressive symptom showed a stronger relationship among the high-increasing type of pain than the low-stable type. The high-increasing type had a higher percentage of females, lower income, relatively low educational attainment, and a higher percentage of rural residents than the low-stable type. Conclusion The significance of this study is that it reiterated the importance of early pain diagnosis and intervention by identifying the types of pain changes in older adults and analyzing their effects on depressive symptoms. Therefore, it is especially important to pay attention to interventions that are designed to help vulnerable groups with a high risk of pain obtain effective pain management.

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.