ABSTRACT
BACKGROUND: The optimal sequence of microvascular clamping during free flap transfer is yet to be established. Many surgeons are reluctant to perform temporary declamping and subsequent reclamping during microvascular anastomosis; however, we generally anastomose the artery first and temporarily declamp it before performing venous anastomosis to confirm arterial patency and ensure proper alignment of the flap veins. Herein, we aimed to retrospectively investigate the efficacy and safety of this temporary revascularization method in 126 patients who underwent microvascular head and neck reconstruction. METHODS: A total of 127 free flaps were transferred, with the anterolateral thigh flap (49 flaps) being the most frequently used. The internal jugular vein was the most frequently used recipient vein and end-to-side anastomoses to it were performed in 112 patients. RESULTS: Intraoperative reanastomosis was required because of arterial thrombosis in 5 cases (4.0%), arterial and venous thrombosis in 1 case (0.8%), injury to the flap artery distal to the anastomotic site in 1 case (0.8%), and venous twisting in 1 case (0.8%). Postoperatively, all the flaps survived without microvascular compromise. CONCLUSIONS: Vascular kinking or twisting of the vascular pedicle is a major cause of free flap failure. However, it is difficult to place empty vessels accurately during clamping. Nonetheless, temporary revascularization engorges the flap vein before venous anastomosis and minimizes the risk of venous kinking and twisting. According to our results, reclamping did not increase the risk of arterial thrombosis.
ABSTRACT
BACKGROUND: Lasting scars such as keloids and hypertrophic scars adversely affect a patient's quality of life. However, these scars are frequently underdiagnosed because of the complexity of the current diagnostic criteria and classification systems. This study aimed to explore the application of Large Language Models (LLMs) such as ChatGPT in diagnosing scar conditions and to propose a more accessible and straightforward diagnostic approach. METHODS: In this study, five artificial intelligence (AI) chatbots, including ChatGPT-4 (GPT-4), Bing Chat (Precise, Balanced, and Creative modes), and Bard, were evaluated for their ability to interpret clinical scar images using a standardized set of prompts. Thirty mock images of various scar types were analyzed, and each chatbot was queried five times to assess the diagnostic accuracy. RESULTS: GPT-4 had a significantly higher accuracy rate in diagnosing scars than Bing Chat. The overall accuracy rates of GPT-4 and Bing Chat were 36.0% and 22.0%, respectively (P = 0.027), with GPT-4 showing better performance in terms of specificity for keloids (0.6 vs. 0.006) and hypertrophic scars (0.72 vs. 0.0) than Bing Chat. CONCLUSIONS: Although currently available LLMs show potential for use in scar diagnostics, the current technology is still under development and is not yet sufficient for clinical application standards, highlighting the need for further advancements in AI for more accurate medical diagnostics. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online instructions to authors www.springer.com/00266 .
ABSTRACT
To evaluate the accuracy of AI chatbots in staging pressure injuries according to the National Pressure Injury Advisory Panel (NPIAP) Staging through clinical image interpretation, a cross-sectional design was conducted to assess five leading publicly available AI chatbots. As a result, three chatbots were unable to interpret the clinical images, whereas GPT-4 Turbo achieved a high accuracy rate (83.0%) in staging pressure injuries, notably outperforming BingAI Creative mode (24.0%) with statistical significance (p < 0.001). GPT-4 Turbo accurately identified Stages 1 (p < 0.001), 3 (p = 0.001), and 4 (p < 0.001) pressure injuries, and suspected deep tissue injuries (p < 0.001), while BingAI demonstrated significantly lower accuracy across all stages. The findings highlight the potential of AI chatbots, especially GPT-4 Turbo, in accurately diagnosing images and aiding the subsequent management of pressure injuries.