Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Background and Objectives: The Fractional Flow Reserve and Intravascular UltrasoundGuided Intervention Strategy for Clinical Outcomes in Patients with Intermediate Stenosis (FLAVOUR) trial demonstrated non-inferiority of fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) compared with intravascular ultrasound (IVUS)-guided PCI. We sought to investigate the cost-effectiveness of FFR-guided PCI compared to IVUS-guided PCI in Korea. Methods: A 2-part cost-effectiveness model, composed of a short-term decision tree model and a long-term Markov model, was developed for patients who underwent PCI to treat intermediate stenosis (40% to 70% stenosis by visual estimation on coronary angiography).The lifetime healthcare costs and quality-adjusted life-years (QALYs) were estimated from the healthcare system perspective. Transition probabilities were mainly referred from the FLAVOUR trial, and healthcare costs were mainly obtained through analysis of Korean National Health Insurance claims data. Health utilities were mainly obtained from the Seattle Angina Questionnaire responses of FLAVOUR trial participants mapped to EQ-5D. Results: From the Korean healthcare system perspective, the base-case analysis showed that FFR-guided PCI was 2,451 U.S. dollar lower in lifetime healthcare costs and 0.178 higher in QALYs compared to IVUS-guided PCI. FFR-guided PCI remained more likely to be cost-effective over a wide range of willingness-to-pay thresholds in the probabilistic sensitivity analysis. Conclusions Based on the results from the FLAVOUR trial, FFR-guided PCI is projected to decrease lifetime healthcare costs and increase QALYs compared with IVUS-guided PCI in intermediate coronary lesion, and it is a dominant strategy in Korea.

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.

Lichen amyloidosis is characterized by coalescent hyperpigmented papules with a predilection for the extremities and is accompanied by severe chronic pruritus. Various therapies have been attempted; however, there is no uniformly recognized effective treatment. In the first case, a 60-year-old man with a 37-year history of intractable itching presented with hyperkeratotic brown papules and coalescing plaques on the trunk and extensor surfaces of the extremities. The second case involved a 31-year-old man who presented with brownish macules and papules with a rippled pattern on the upper back and lower extremities. Histological examination of the lesions from both patients revealed epidermal hyperplasia and amorphous material deposited in the papillary dermis, which tested positive on Congo red staining. Although both patients were diagnosed with lichen amyloidosis, several treatment modalities showed limited efficacy. Subsequently, dupilumab, a treatment for severe pruritus, was administered to both patients, resulting in significant improvements.

A 34-year-old male with acne was presented with a 1-year history of multiple tiny subcutaneous nodules on his face, particularly on both cheeks. Physical examination revealed asymptomatic, palpable, and hard nodules with an acne scar. The skin lesions were more obvious when the cheeks were inflated. A skin biopsy revealed multiple focal bony trabeculae with osteoblasts in the dermis. A diagnosis of multiple miliary osteomas cutis was done. Multiple miliary osteoma cutis is a rare benign extraskeletal ossification that is considered relatively common and under-reported because of its asymptomatic behavior. Osteoma cutis is generally observed in middle-aged females with a history of acne. Male involvement rarely occurs, especially at a young age, as observed in this patient. Topical retinoids were prescribed, but the lesions did not abate at 3 months follow-up.

Traumatic tattoos are abnormal pigmentations caused by embedded foreign particles due to various injuries, including explosions, abrasions, or traffic accidents. Traumatic tattoos caused by pencils are common and considered trivial because they are usually confined to the injured site without any complications. A 4-year-old girl presented with a 5-month history of a grey-to-bluish patch on her left cheek. Histopathological examination revealed many pigment granules surrounding the blood vessels and appendages diffused from the mid to the lower dermis. Various staining methods were employed to verify that colorants caused the tattoo. To our knowledge, there have been no reports of traumatic tattoos caused by colored pencils that spread rapidly beyond the injured areas. In particular, various unexpected traumas can occur frequently in children; therefore, an accurate diagnosis is essential by differentiating it from other acquired pigmentary disorders

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.