Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Purpose: This study developed a thermoelectric element (TEE) tourniquet integrating a tourniquet with a temperature control device capable of delivering heat or cold therapy. A randomized controlled trial was conducted to evaluate the effects of the TEE tourniquet on pain, stress, and satisfaction during venipuncture. Methods: In total, 118 hospitalized adults were randomly assigned to heat therapy (40~45°C), cold therapy (0~10°C), thermal grill illusion therapy (alternating heat and cold), or the control group. The TEE tourniquet was applied 10 cm above the puncture site. A temperature intervention began 5 seconds before cannulation and was maintained during the procedure, typically lasting 10 to 30 seconds. The control group received the TEE tourniquet without temperature activation. Outcomes included perceived pain and stress (numerical rating scale), observed pain (Wong-Baker FACES), SpO2, stress index, and participant satisfaction. Results: Significant differences were found among groups in perceived pain (F = 4.82, p = .003), observed pain (F = 5.50, p = .001), and perceived stress (F = 4.72, p = .004). The heat therapy group reported significantly lower pain and stress than the control group. No significant differences were found in SpO₂, the stress index, or satisfaction. Conclusion Heat therapy via the TEE tourniquet significantly reduced venipuncture-related pain and stress. Given its short application time and usability, this device may serve as a clinically useful nursing intervention to improve comfort during invasive procedures.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Purpose: This study developed a thermoelectric element (TEE) tourniquet integrating a tourniquet with a temperature control device capable of delivering heat or cold therapy. A randomized controlled trial was conducted to evaluate the effects of the TEE tourniquet on pain, stress, and satisfaction during venipuncture. Methods: In total, 118 hospitalized adults were randomly assigned to heat therapy (40~45°C), cold therapy (0~10°C), thermal grill illusion therapy (alternating heat and cold), or the control group. The TEE tourniquet was applied 10 cm above the puncture site. A temperature intervention began 5 seconds before cannulation and was maintained during the procedure, typically lasting 10 to 30 seconds. The control group received the TEE tourniquet without temperature activation. Outcomes included perceived pain and stress (numerical rating scale), observed pain (Wong-Baker FACES), SpO2, stress index, and participant satisfaction. Results: Significant differences were found among groups in perceived pain (F = 4.82, p = .003), observed pain (F = 5.50, p = .001), and perceived stress (F = 4.72, p = .004). The heat therapy group reported significantly lower pain and stress than the control group. No significant differences were found in SpO₂, the stress index, or satisfaction. Conclusion Heat therapy via the TEE tourniquet significantly reduced venipuncture-related pain and stress. Given its short application time and usability, this device may serve as a clinically useful nursing intervention to improve comfort during invasive procedures.

Purpose: This study developed a thermoelectric element (TEE) tourniquet integrating a tourniquet with a temperature control device capable of delivering heat or cold therapy. A randomized controlled trial was conducted to evaluate the effects of the TEE tourniquet on pain, stress, and satisfaction during venipuncture. Methods: In total, 118 hospitalized adults were randomly assigned to heat therapy (40~45°C), cold therapy (0~10°C), thermal grill illusion therapy (alternating heat and cold), or the control group. The TEE tourniquet was applied 10 cm above the puncture site. A temperature intervention began 5 seconds before cannulation and was maintained during the procedure, typically lasting 10 to 30 seconds. The control group received the TEE tourniquet without temperature activation. Outcomes included perceived pain and stress (numerical rating scale), observed pain (Wong-Baker FACES), SpO2, stress index, and participant satisfaction. Results: Significant differences were found among groups in perceived pain (F = 4.82, p = .003), observed pain (F = 5.50, p = .001), and perceived stress (F = 4.72, p = .004). The heat therapy group reported significantly lower pain and stress than the control group. No significant differences were found in SpO₂, the stress index, or satisfaction. Conclusion Heat therapy via the TEE tourniquet significantly reduced venipuncture-related pain and stress. Given its short application time and usability, this device may serve as a clinically useful nursing intervention to improve comfort during invasive procedures.

Purpose: This study developed a thermoelectric element (TEE) tourniquet integrating a tourniquet with a temperature control device capable of delivering heat or cold therapy. A randomized controlled trial was conducted to evaluate the effects of the TEE tourniquet on pain, stress, and satisfaction during venipuncture. Methods: In total, 118 hospitalized adults were randomly assigned to heat therapy (40~45°C), cold therapy (0~10°C), thermal grill illusion therapy (alternating heat and cold), or the control group. The TEE tourniquet was applied 10 cm above the puncture site. A temperature intervention began 5 seconds before cannulation and was maintained during the procedure, typically lasting 10 to 30 seconds. The control group received the TEE tourniquet without temperature activation. Outcomes included perceived pain and stress (numerical rating scale), observed pain (Wong-Baker FACES), SpO2, stress index, and participant satisfaction. Results: Significant differences were found among groups in perceived pain (F = 4.82, p = .003), observed pain (F = 5.50, p = .001), and perceived stress (F = 4.72, p = .004). The heat therapy group reported significantly lower pain and stress than the control group. No significant differences were found in SpO₂, the stress index, or satisfaction. Conclusion Heat therapy via the TEE tourniquet significantly reduced venipuncture-related pain and stress. Given its short application time and usability, this device may serve as a clinically useful nursing intervention to improve comfort during invasive procedures.

Postmortem computed tomography (PMCT) is used in forensic medicine worldwide due to its ability to non-invasively visualize injuries, hemorrhage, and estimate volume. In the autopsy of infants, assessing nutritional conditions such as weight is crucial for identifying neglect. This study aims to evaluate the usefulness of retrospectively estimating the weight of Korean infants using PMCT-based volume and multiplication factors, even when the body has been cremated. A total of 44 cases of infant death (under 12 months) were analyzed. PMCT images were obtained before autopsy. Autopsy records and documentation provided by the police at the time of autopsy were reviewed to determine the weight (g) of the infant. PMCT-based infant volumes (mL) were estimated using a three-dimensional semi-automatic segmentation method. Multiplication factors (g/mL) were calculated by dividing the weight recorded at autopsy by the PMCT-based volume, yielding a mean of 1.047 g/mL, ranging from 1.014 g/mL to 1.085 g/mL. The mean absolute error compared to weights recorded at autopsy was 95 g. Significant discrepancies were observed between weights recorded at the scene or medical center and those measured at autopsy. This study demonstrates that PMCT-based weight estimation for Korean infants is a reliable method and has the potential for retrospectively validating incorrect weight measurements and addressing inconsistencies in recorded weight data.

Detecting sphenoid sinus fluid (SSF) is an additional finding in autopsies for diagnosing drowning. SSF can provide additional forensic evidence through laboratory tests such as diatom and electrolyte analyses. If drowning is suspected, accurately assessing the presence and volume of SSF during an autopsy is crucial. Utilizing postmortem computed tomography (PMCT) images could aid in accurately sampling SSF. Accurately segmenting the region of interest is essential for volume analysis using computed tomography images. However, manual segmentation techniques are labor-intensive and time-consuming, and their success depends on the experience of the observer. Therefore, this study aimed to develop a U-Net–based deep learning model for the automatic segmentation of SSF in drowning cases using PMCT images and to evaluate the performance of the model. We retrospectively reviewed 34 drowning cases in which both PMCT scans and forensic autopsies were performed at our institution. The U-Net architecture of deep learning was used for automatic segmentation. The proposed model achieved the Dice similarity coefficient (DSC) and Intersection over Union (IoU) of a maximum of 95.85% and 92.03%, a minimum of 0% and 0%, and an average of 77.15% and 67.18%, respectively. Although the average DSC and IoU did not show high similarity, this study showed that PMCT images can be used for automatic segmentation of SSF in drowning cases, which could improve the performance with sufficient dataset acquisition and further model training.