: Injecting soft-tissue fillers, such as hyaluronic acid, has become an extremely popular method of facial augmentation. Although rare, adverse effects, ranging from cosmetically dissatisfactory to dangerous, may occur. The most severe adverse effect of these is vascular occlusion of the central retinal artery, resulting in vision loss. Protocols for the treatment of filler-induced blindness have not been well established, but there is evidence to suggest that hyperbaric oxygen therapy (HBOT) may aid in the therapeutic algorithm for filler-induced blindness. We present a clinical case of filler-induced blindness successfully treated with prompt administration of HBOT. A 38-year-old healthy female presented to the emergency room after immediate pain and complete vision loss following an at-home injection of mail-order filler into the left glabella and medial eyebrow. After treatment with hyaluronidase and ocular massage, neither of which relieved her symptoms, she received HBOT within 10 h of the injury, after which her vision improved significantly. After 2 additional sessions, the patient had complete vision recovery. This case report contributes to the very sparse literature documenting successful treatment of filler-induced blindness using HBOT, advocating for further study, and possible incorporation into the treatment algorithm for filler-induced blindness. Improper soft-tissue filler administration possesses a potential risk of severe adverse effects. It is crucial that the medical community is aware of treatments that offer the highest chance of visual recovery and sustained benefit for patients. Level of Evidence: 5:

Introduction: Wound care is an essential discipline in plastic surgery, especially as the prevalence of chronic wounds, such as pressure injuries, is increasing. The escalating volume of patient data and the numerous variables influencing wound outcomes are making traditional manual chart reviews in wound care and research increasingly complex and burdensome. The emergence of Natural Language Processing (NLP) software based on large language models (LLMs) such as ChatGPT presents an opportunity to automate the data extraction process. This study harnesses the capabilities of ChatGPT, hosted by our medical center's secure, private Azure OpenAI service, to automatically extract and process variables from patient charts following sacral wound visits. We assess ChatGPT's potential to revolutionize chart review through improved data retrieval accuracy and efficiency. Methods: We evaluated the use of the medical center's internal ChatGPT in chart review. ChatGPT and a Python script were integrated into the existing chart review process for patients with sacral wounds from 2 hospital cohorts to extract and format variables related to wound care. Metrics include time taken for review, accuracy of extracted information, and assessment of ChatGPT-generated insights. Results: ChatGPT reduced the average time per chart review from 7.56 minutes with the manual method to 1.03 minutes using ChatGPT. Furthermore, it achieved a 0.957 overall accuracy rate compared to manual chart review, ranging from 0.747 to 0.986 across extracted data elements. ChatGPT was also able to synthesize accurate narrative descriptions of patient wounds. Conclusions: We highlight ChatGPT's potential to enhance speed and precision of chart review in the context of both clinical care and wound care research, offering valuable implications for integration of artificial intelligence in healthcare workflows.

This case series describes the successful use of indocyanine green dye and near infrared fluorescence imaging in primary peripheral nerve repair.