Purpose: The aim of this study was to evaluate the risk factors associated with glaucoma or ocular hypertension (OHT) in patients taking oral corticosteroids for extended periods, and to aid in managing intraocular pressure (IOP) in patients with these risk factors. Methods: A cross-sectional study was performed involving 690 patients who visited a tertiary referral hospital and had been using oral corticosteroids for more than six months. Patients' demographics, tonometry results, drug type, dosage, duration, ophthalmic history, and the use of glaucoma eye drops were analyzed to determine the risk factors associated with glaucoma or OHT. Results: In a generalized linear model analysis comparing patients' eyes diagnosed with glaucoma or ocular hypertension to those without such diagnoses, no statistical difference was observed between the two groups in terms of drug type, age, and duration of oral corticosteroid use. However, the dosage was found to be statistically significant (odds ratio 1.09, p = 0.0294). Conclusions No difference in the incidence of glaucoma or OHT was found based on the type of oral steroid, age, or duration of use. However, a higher incidence of glaucoma and OHT was observed among patients taking higher doses of oral steroids. Therefore, it is suggested that using lower doses of oral steroids may be more beneficial for managing IOP.

Background: The forehead is a region connected to the scalp and is accompanied by various structures. In some tumors, the pattern of development may differ from that of other anatomical sites. When a noninflammatory skin-colored tumor develops on the forehead, it is difficult to diagnose accurately. Objective: This study aimed to identify the epidemiologic data and clinical features of noninflammatory skin-colored tumors of the forehead. Methods: We retrospectively reviewed the medical records of 200 patients with noninflammatory, skin-colored tumors diagnosed after skin biopsy over a period of 11 years. We evaluated tumor prevalence, clinical features, and differences according to sex and age. If the tumor was large and deeply located, a radiologic study was performed. Results: Of the 12 different histopathologic results, lipoma (52.0%) was the most frequent, followed by epidermal cyst (17.0%), osteoma (13.5%), steatocystoma (6.0%), and pilomatricoma (3.5%). Statistical analysis showed that females were dominant in the osteoma group. For an accurate diagnosis, 25 of the 52 patients who underwent computed tomography were diagnosed with lipoma, and 19 (76.0%) of them were identified as deep-seated lipoma. Conclusion The most common tumor among noninflammatory, skin-colored tumors of the forehead was lipoma.When they occur on the forehead, the proportion of deep-seated lipomas is higher than that at other sites. In the case of a solid and fixed tumor, a deep-seated lipoma should be considered. Computed tomography should be performed in addition to ultrasonography because the sensitivity of ultrasonography for the diagnosis of deep-seated lipoma is unsatisfactory.

Background: Liver fibrosis is an early stage of liver cirrhosis. As a reversible lesion before cirrhosis, liver failure, and liver cancer, it has been a target for drug discovery. Many antifibrotic candidates have shown promising results in experimental animal models; however, due to adverse clinical reactions, most antifibrotic agents are still preclinical. Therefore, rodent models have been used to examine the histopathological differences between the control and treatment groups to evaluate the efficacy of anti-fibrotic agents in non-clinical research. In addition, with improvements in digital image analysis incorporating artificial intelligence (AI), a few researchers have developed an automated quantification of fibrosis. However, the performance of multiple deep learning algorithms for the optimal quantification of hepatic fibrosis has not been evaluated. Here, we investigated three different localization algorithms, mask R-CNN, DeepLabV3+, and SSD, to detect hepatic fibrosis. Results: 5750 images with 7503 annotations were trained using the three algorithms, and the model performance was evaluated in large-scale images and compared to the training images. The results showed that the precision values were comparable among the algorithms. However, there was a gap in the recall, leading to a difference in model accuracy. The mask R-CNN outperformed the recall value (0.93) and showed the closest prediction results to the annotation for detecting hepatic fibrosis among the algorithms. DeepLabV3+ also showed good performance; however, it had limitations in the misprediction of hepatic fibrosis as inflammatory cells and connective tissue. The trained SSD showed the lowest performance and was limited in predicting hepatic fibrosis compared to the other algorithms because of its low recall value (0.75). Conclusions We suggest it would be a more useful tool to apply segmentation algorithms in implementing AI algorithms to predict hepatic fibrosis in non-clinical studies.