Guba, Markus

ABSTRACT Background Living kidney donation is a crucial option for addressing the global organ shortage and providing kidney transplantation for patients suffering from end‐stage kidney disease. Ensuring donor safety necessitates a comprehensive preoperative assessment of kidney anatomy and function. This study evaluates the relationship between kidney volumes derived from deep learning‐based MRI volumetry, intraoperative kidney volume measurements, split renal function measured by renal scintigraphy, and post‐donation eGFR. Deep learning‐based MRI volumetry is hypothesized to be a reliable method with good correlation. Methods This retrospective study analyzed 178 living kidney donors. Deep learning MRI volumetry‐based kidney volumes were compared with intraoperative volumes of the explanted donor kidneys obtained using the water displacement method. Additionally, MRI‐based volume ratios were compared with scintigraphy‐based split renal function ratios to determine their ability to predict the kidney with poorer renal function and post‐donation eGFR. Results Deep learning‐based MRI volumetry strongly correlated with intraoperatively measured kidney volumes (Pearson's correlation; r = 0.7671; p < 0.0001), confirming its precision in volume estimation. Although MRI‐based kidney volume ratios demonstrated only a moderate correlation with scintigraphy‐based split renal function ratios ( r = 0.4798), MRI volumetry correlated with 1‐year post‐donation eGFR. It tended to be better than renal scintigraphy ( r = 0.6829 versus r = 0.6191). Conclusion Deep learning‐based MRI volumetry is a reliable, non‐invasive tool for estimating kidney volumes in living donors, offering a radiation‐free alternative for preoperative assessment. While it differs from renal scintigraphy in evaluating split renal function ratios, its correlation with post‐donation eGFR tends to be better, supporting its potential role in living kidney donor assessment.

Objectives: Every year, around 300 million surgeries are conducted worldwide, with an estimated 4.2 million deaths occurring within 30 days after surgery. Adequate patient education is crucial, but often falls short due to the stress patients experience before surgery. Large language models (LLMs) can significantly enhance this process by delivering thorough information and addressing patient concerns that might otherwise go unnoticed. Material and methods: This cross-sectional study evaluated Chat Generative Pretrained Transformer-4o’s audio-based responses to frequently asked questions (FAQs) regarding six general surgical procedures. Three experienced surgeons and two senior residents formulated seven general and three procedure-specific FAQs for both preoperative and postoperative situations, covering six surgical scenarios (major: pancreatic head resection, rectal resection, total gastrectomy; minor: cholecystectomy, Lichtenstein procedure, hemithyroidectomy). In total, 120 audio responses were generated, transcribed, and assessed by 11 surgeons from 6 different German university hospitals. Results: ChatGPT-4o demonstrated strong performance, achieving an average score of 4.12/5 for accuracy, 4.46/5 for relevance, and 0.22/5 for potential harm across 120 questions. Postoperative responses surpassed preoperative ones in both accuracy and relevance, while also exhibiting lower potential for harm. Additionally, responses related to minor surgeries were minimal, but significantly more accurate compared to those for major surgeries. Conclusions: This study underscores GPT-4o’s potential to enhance patient education both before and after surgery by delivering accurate and relevant responses to FAQs about various surgical procedures. Responses regarding the postoperative course proved to be more accurate and less harmful than those addressing preoperative ones. Although a few responses carried moderate risks, the overall performance was robust, indicating GPT-4o’s value in patient education. The study suggests the development of hospital-specific applications or the integration of GPT-4o into interactive robotic systems to provide patients with reliable, immediate answers, thereby improving patient satisfaction and informed decision-making.