ABSTRACT
Screening for osteoporosis is crucial for early detection and prevention, yet it faces challenges due to the low accuracy of calcaneal quantitative ultrasound (QUS) and limited access to dual-energy X-ray absorptiometry (DXA) scans. Recent advances in AI offer a promising solution through opportunistic screening using existing medical images. This study aims to utilize deep learning techniques to develop a model that analyzes chest X-ray (CXR) images for osteoporosis screening. This study included the AI model development stage and the clinical validation stage. In the AI model development stage, the combined dataset of 5122 paired CXR images and DXA reports from the patients aged 20 to 98 years at a medical center was collected. The images were enhanced and filtered for hardware retention such as pedicle screws, bone cement, artificial intervertebral discs or severe deformity in target level of T12 and L1. The dataset was then separated into training, validating, and testing datasets for model training and performance validation. In the clinical validation stage, we collected 440 paired CXR images and DXA reports from both the TCVGH and Joy Clinic, including 304 pared data from TCVGH and 136 paired data from Joy Clinic. The pre-clinical test yielded an area under the curve (AUC) of 0.940, while the clinical validation showed an AUC of 0.946. Pearson's correlation coefficient was 0.88. The model demonstrated an overall accuracy, sensitivity, and specificity of 89.0%, 88.7%, and 89.4%, respectively. This study proposes an AI model for opportunistic osteoporosis screening through CXR, demonstrating good performance and suggesting its potential for broad adoption in preliminary screening among high-risk populations.
ABSTRACT
PURPOSE: To report a case of corneal injury by swim goggle anti-misting agent. METHODS: Case report. RESULTS: A 47-year-old man presented with ocular injury caused by swim goggle anti-misting agent. Two weeks previously, the patient felt sudden ocular pain and grit after pool water flooded his goggles while swimming. Before the patient's swim, a copious amount of anti-misting agent was sprayed inside his goggles. On examination, severe corneal epithelial defect and stroma edema of the right eye were noted. Best-corrected visual acuity (BCVA) was 20/40. Right eye corneal thickness was 625 microm. Topical corticosteroids, antibiotic eye drops, and oral tetracycline were started. After 1 week of treatment, the epithelial defect of his right cornea improved slightly. Stromal edema decreased mildly. Epithelial defect persisted at 1 month follow-up. Right eye BCVA remained 20/40. Three months after treatment, the right eye epithelial defect healed completely. Mild diffuse superficial stromal opacity was noted. BCVA improved to 20/20. At 1-year follow-up, right eye corneal thickness was 547 microm, which was thinner than the left eye. Right eye corneal endothelial cell density measured approximately 1500 cells/mm2, which was less than the left eye at 2300 cells/mm2. The faint, diffuse stromal opacity disappeared; however, white subepithelial plaques were noted in the cornea centers. After 1 month of treatment with topical corticosteroids, the plaques subsided completely. The corneas remained clear at 2-year follow-up. CONCLUSION: Anti-misting agent can cause severe corneal injuries, including persistent epithelial defects, stromal edema and opacity, endothelial cell loss, and subepithelial plaque.
