Browsing by Author "Emre, Sukru"

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Evaluation of Donor Exclusions for Living Donor Liver Transplantation in a Tertiary Center
(Elsevier Science Inc, 2026) Gumus, Tufan; Umman, Veysel; Sertoz, Berk; Guler, Ezgi; Uguz, Alper; Sertoz, Ozen Onen; Emre, Sukru
Introduction. The success of living donor liver transplantation is closely related to donor selection. Living donor liver transplantation (LDLT) plays a crucial role in saving lives, especially where cadaveric donations are limited. Donor selection is pivotal for the success of LDLT, emphasizing donor rights, minimizing complications, and ensuring donor survival. The main purpose for donor evaluation is to provide a suitable graft for the recipient while assuring a safe operation for the donor. This study aims to identify our center's donor exclusion reasons, assess limitations in donor pool utilization, and enhance its effectiveness. Methods. We retrospectively analyzed data from 680 healthy individuals who applied as liver donor candidates to our center between November 2016 and November 2021. Of these, 170 underwent donor hepatectomy, while 510 candidates deemed unsuitable were investigated. Results. A total of 170 (25%) candidates became liver donors (group A), and 510 (75%) candidates were found unsuitable (group B). Recipient-related reasons (179, 35.09%) made up the leading exclusion cause. Psychiatric problems (105, 20%) ranked second among the reasons for rejection of donor candidates, and hepatosteatosis was the third most common reason. Conclusion. The critical factor determining the success of living donor liver transplantation is the precise selection of the donor. Achieving optimal donor selection is feasible through a comprehensive multidisciplinary liver transplant team and clearly defined criteria. By employing appropriate selection standards and a skilled transplant team, it is feasible to enhance the pool of liver donors and conduct more living donor liver transplants with reduced morbidity and mortality rates.
Evaluation of the Effectiveness, Safety, and Patient Satisfaction of Artificial Intelligence-Based Patient Education and Counseling for Both Recipients and Donors in the Preoperative and Postoperative Phases of Organ Transplantation
(Elsevier Science Inc, 2025) Umman, Veysel; Tosun, Busra; Uygur, Abdulkerim; Emre, Sukru
Introduction Effective patient education is critical in organ transplantation, particularly for living donor groups, where informed decision-making impacts both donors and recipients. AI-driven solutions, such as OpenAI's ChatGPT, can enhance education by providing real-time responses. This study assessed the effectiveness, safety, and satisfaction of an AI-supported patient education system integrated with WhatsApp and a Customer Relationship Management (CRM) system. Methods A prospective observational study was conducted at our transplant center between October 1, 2023, and July 31, 2024. Eligible participants included adults (18-65 years) who were either recipients or living donor candidates for kidney and liver transplantation. AI-generated responses were retrospectively evaluated by transplant physicians for accuracy and safety using a 5-point Likert scale. Patient satisfaction was assessed using the validated Turkish Short Assessment of Patient Satisfaction (SAPS) form. Results A total of 196 patients submitted 1281 questions, categorized into nine thematic groups. The most common inquiries pertained to post-transplant social life and work resumption (25.2%). AI responses demonstrated high safety (92% scoring >= 4) and accuracy (80% scoring >= 4). The highest accuracy was for surgical technique-related questions (4.7/5), while general questions had the lowest (4.2/5). Patient satisfaction was overwhelmingly positive, with 99.5% expressing satisfaction. Conclusion The AI-supported system provided accurate and safe preoperative and postoperative education for transplant patients, demonstrating high satisfaction. AI integration into clinical workflows presents a promising advancement, though challenges related to accuracy and ethics remain. Future research should explore AI's role in image recognition and triage to optimize transplant patient care.