Resul Arjin Öksüz
2009- 2013 Diyarbakır Science High School, Turkey2013 - 2019 Akdeniz University Faculty of Medicine, Turkey (Formal Education)2019-2020 Selahattin-I Eyyubi Hospital (Practicioner)2020-Present Ankara University ORL-HNS DepartmentORL-HNS Speciality Education(5 years)
2024- European Board written Exam passed.
2025 October to January Fellowship programme accepted from Luzern University ENT Department
2024-05-13 to 2024-05-30 | Experimental animal use certificate for the animal group “ rodents ( mouse, rat , guinea pig and rabbit) (Ankara university medicine faculty)
2022-06-25 to 2022-06-26 | Hand on dissection (15. Turkish otorhinolaryngology temporal bone dissection course)
2025-10-05 to 2025-10-06 | Hands on Dissection Laterall Skull Base Course
Sessions
Introduction: The traditional Halstedian apprenticeship model ("See one, do one, teach one") is increasingly challenged by ethical constraints, reduced working hours, and significant global disparities in training resources. While young otolaryngologists in major academic centers benefit from high-volume robotic and endoscopic exposure, peers in developing regions often lack access to specialized mentorship. This presentation proposes a paradigm shift: leveraging Artificial Intelligence (AI) and Deep Learning (DL) not merely as diagnostic tools, but as the great equalizer in surgical education.
Methods & Analysis: We analyzed scientific studies exploring the growing role of Computer Vision (CV) and Motion Analysis algorithms in Otolaryngology training. Specifically, we examined DL models trained to objectively assess surgical videos of Endoscopic Sinus Surgery (ESS) and temporal bone dissection. These systems were evaluated on their ability to act as "Virtual Mentors," providing automated, granular feedback on instrument handling, tissue respect, and operative flow compared to standard OSATS (Objective Structured Assessment of Technical Skills) scores.
Results: Current Deep Learning models demonstrate the capacity to segment surgical phases and identify unsafe maneuvers with accuracy comparable to expert consensus. By integrating these AI-driven feedback loops into cloud-based simulation platforms, residents can achieve proficiency benchmarks independently of their geographic location or local faculty availability. This technology effectively decouples high-quality surgical feedback from the physical presence of a master surgeon.
Conclusion: For the Young IFOS community, AI represents a critical opportunity to bridge the global training gap. Transitioning from subjective apprenticeship to data-driven, objective competence assessment fosters true global diversity. By embracing these "Augmented Intelligence" tools, we can ensure that the next generation of otolaryngologists achieves a standardized level of excellence, ensuring that patient safety and surgical skill are defined by dedication, not geography.