Citations

AbstractBlood blister-like aneurysms (BBA) are rare vascular lesions with challenging treatment. While direct surgery has been the primary technique for treating BBA, there has been a shift toward endovascular methods in recent years. This article presents a retrospective case series of patients treated with direct surgery at our center over a 6-year period.Patients who underwent surgery for BBA from 2014 to 2019 were evaluated. Data on surgical procedures, complications, and clinical outcomes were obtained from patients' medical records. Additionally, patients were contacted to provide updates on their treatment outcomes.A total of 17 patients were treated for BBA during the study period. All cases presented with subarachnoid hemorrhage and were treated using the clipping method. Intraoperative rupture occurred in 11 patients. Additionally, internal carotid artery sacrificing was done in five patients due to avulsion during surgery. Of these 17 patients, 11 (64.7%) had favorable outcomes following surgery, while the mortality rate was 29.4% (5 patients).The rate of favorable outcomes and mortality associated with the direct surgery technique at our center was comparable to results from other centers. Further studies are required to determine the optimal method for treating BBA.

Background The digital transformation in medical education is reshaping how clinical skills, such as point-of-care ultrasound (POCUS), are taught. In nephrology fellowship programs, POCUS is essential for enhancing diagnostic accuracy, guiding procedures, and optimizing patient management. To address these evolving demands, we developed an artificial intelligence (AI)-driven POCUS curriculum using a multiagent approach that integrates human expertise with advanced AI models, thereby elevating educational standards and better preparing fellows for contemporary clinical practice. Methods In April 2024, the Mayo Clinic Minnesota Nephrology Fellowship Program initiated a novel AI-assisted process to design a comprehensive POCUS curriculum. This process integrated multiple advanced AI models—including GPT-4.0, Claude 3.0 Opus, Gemini Advanced, and Meta AI with Llama 3—to generate initial drafts and iteratively refine content. A panel of blinded nephrology POCUS experts subsequently reviewed and modified the AI-generated material to ensure both clinical relevance and educational rigor. Results The curriculum underwent 12 iterative revisions, incorporating feedback from 29 communications across AI models. Key features of the final curriculum included expanded core topics, diversified teaching methods, enhanced assessment tools, and integration into inpatient and outpatient nephrology rotations. The curriculum emphasized quality assurance, POCUS limitations, and essential clinical applications, such as fistula/graft evaluation and software integration. Alignment with certification standards further strengthened its utility. AI models contributed significantly to the curriculum's foundational structure, while human experts provided critical clinical insights. Conclusion This curriculum, enhanced through a multiagent approach that combines AI and human collaboration, exemplifies the transformative potential of digital tools in nephrology education. The innovative framework seamlessly integrates advanced AI models with expert clinical insights, providing a scalable model for medical curriculum development that is responsive to evolving educational demands. The synergy between technological innovation and human expertise holds promising implications for advancing fellowship training. Future studies should evaluate its impact on clinical competencies and patient outcomes across diverse practice environments.