Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

The introduction of anti-amyloid therapies for Alzheimer’s disease (AD), such as lecanemab (Lequembi®), which was recently approved in Korea, necessitates careful monitoring for amyloid-related imaging abnormalities (ARIA) using brain MRI. To optimize ARIA monitoring in Korean clinical settings, the Korean Society of Neuroradiology (KSNR) and the Age and Neurodegeneration Imaging (ANDI) Study Group proposed MRI protocol recommendations on essential MR sequences, MRI acquisition parameters, timing and condition of MRI examinations, and essential details to provide a scientific basis for maximizing the safety and efficacy of AD treatment. A customized, standardized MRI protocol focusing on Korea’s healthcare environment can improve ARIA management and ensure patient safety through early detection of potential anti-amyloid therapy side effects, thereby enhancing treatment quality.

Darier’s disease is characterized by greasy and scaly papules that primarily affect seborrheic and intertriginous areas which is caused by a mutation in the ATP2A2 gene. Histopathologically, the disease is characterized by acantholysis and dyskeratosis. Among the diverse presentations, the segmental type follows a linear distribution along the lines of Blaschko. Herein, we present a case of a 54-year-old male with generalized erythematous papules that had been linearly distributed across his body for two decades. Lesions on his trunk and extremities were confined to the right side, whereas those on the scalp and face exhibited multiple segmental presentations. Histopathological examination revealed acantholysis and dyskeratosis in the epidermis, confirming the diagnosis of type 1 segmental Darier’s disease. This case underscores the rarity of type 1 segmental Darier’s disease, particularly with multiple segmental involvement and highlights the complexity and variability of this dermatological condition.

The introduction of anti-amyloid therapies for Alzheimer’s disease (AD), such as lecanemab (Lequembi®), which was recently approved in Korea, necessitates careful monitoring for amyloid-related imaging abnormalities (ARIA) using brain MRI. To optimize ARIA monitoring in Korean clinical settings, the Korean Society of Neuroradiology (KSNR) and the Age and Neurodegeneration Imaging (ANDI) Study Group proposed MRI protocol recommendations on essential MR sequences, MRI acquisition parameters, timing and condition of MRI examinations, and essential details to provide a scientific basis for maximizing the safety and efficacy of AD treatment. A customized, standardized MRI protocol focusing on Korea’s healthcare environment can improve ARIA management and ensure patient safety through early detection of potential anti-amyloid therapy side effects, thereby enhancing treatment quality.

Darier’s disease is characterized by greasy and scaly papules that primarily affect seborrheic and intertriginous areas which is caused by a mutation in the ATP2A2 gene. Histopathologically, the disease is characterized by acantholysis and dyskeratosis. Among the diverse presentations, the segmental type follows a linear distribution along the lines of Blaschko. Herein, we present a case of a 54-year-old male with generalized erythematous papules that had been linearly distributed across his body for two decades. Lesions on his trunk and extremities were confined to the right side, whereas those on the scalp and face exhibited multiple segmental presentations. Histopathological examination revealed acantholysis and dyskeratosis in the epidermis, confirming the diagnosis of type 1 segmental Darier’s disease. This case underscores the rarity of type 1 segmental Darier’s disease, particularly with multiple segmental involvement and highlights the complexity and variability of this dermatological condition.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Darier’s disease is characterized by greasy and scaly papules that primarily affect seborrheic and intertriginous areas which is caused by a mutation in the ATP2A2 gene. Histopathologically, the disease is characterized by acantholysis and dyskeratosis. Among the diverse presentations, the segmental type follows a linear distribution along the lines of Blaschko. Herein, we present a case of a 54-year-old male with generalized erythematous papules that had been linearly distributed across his body for two decades. Lesions on his trunk and extremities were confined to the right side, whereas those on the scalp and face exhibited multiple segmental presentations. Histopathological examination revealed acantholysis and dyskeratosis in the epidermis, confirming the diagnosis of type 1 segmental Darier’s disease. This case underscores the rarity of type 1 segmental Darier’s disease, particularly with multiple segmental involvement and highlights the complexity and variability of this dermatological condition.

The introduction of anti-amyloid therapies for Alzheimer’s disease (AD), such as lecanemab (Lequembi®), which was recently approved in Korea, necessitates careful monitoring for amyloid-related imaging abnormalities (ARIA) using brain MRI. To optimize ARIA monitoring in Korean clinical settings, the Korean Society of Neuroradiology (KSNR) and the Age and Neurodegeneration Imaging (ANDI) Study Group proposed MRI protocol recommendations on essential MR sequences, MRI acquisition parameters, timing and condition of MRI examinations, and essential details to provide a scientific basis for maximizing the safety and efficacy of AD treatment. A customized, standardized MRI protocol focusing on Korea’s healthcare environment can improve ARIA management and ensure patient safety through early detection of potential anti-amyloid therapy side effects, thereby enhancing treatment quality.

Darier’s disease is characterized by greasy and scaly papules that primarily affect seborrheic and intertriginous areas which is caused by a mutation in the ATP2A2 gene. Histopathologically, the disease is characterized by acantholysis and dyskeratosis. Among the diverse presentations, the segmental type follows a linear distribution along the lines of Blaschko. Herein, we present a case of a 54-year-old male with generalized erythematous papules that had been linearly distributed across his body for two decades. Lesions on his trunk and extremities were confined to the right side, whereas those on the scalp and face exhibited multiple segmental presentations. Histopathological examination revealed acantholysis and dyskeratosis in the epidermis, confirming the diagnosis of type 1 segmental Darier’s disease. This case underscores the rarity of type 1 segmental Darier’s disease, particularly with multiple segmental involvement and highlights the complexity and variability of this dermatological condition.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.