Artificial intelligence- and computer-assisted navigation for shoulder surgery
| Autor: | Kang-San Lee, Seung Ho Jung, Dong-Hyun Kim, Seok Won Chung, Jong Pil Yoon |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Journal of Orthopaedic Surgery, Vol 32 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2309-4990 10225536 |
| DOI: | 10.1177/10225536241243166 |
| Popis: | Background: Over the last few decades, shoulder surgery has undergone rapid advancements, with ongoing exploration and the development of innovative technological approaches. In the coming years, technologies such as robot-assisted surgeries, virtual reality, artificial intelligence, patient-specific instrumentation, and different innovative perioperative and preoperative planning tools will continue to fuel a revolution in the medical field, thereby pushing it toward new frontiers and unprecedented advancements. In relation to this, shoulder surgery will experience significant breakthroughs. Main body: Recent advancements and technological innovations in the field were comprehensively analyzed. We aimed to provide a detailed overview of the current landscape, emphasizing the roles of technologies. Computer-assisted surgery utilizing robotic- or image-guided technologies is widely adopted in various orthopedic specialties. The most advanced components of computer-assisted surgery are navigation and robotic systems, with functions and applications that are continuously expanding. Surgical navigation requires a visual system that presents real-time positional data on surgical instruments or implants in relation to the target bone, displayed on a computer monitor. There are three primary categories of surgical planning that utilize navigation systems. The initial category involves volumetric images, such as ultrasound echogram, computed tomography, and magnetic resonance images. The second type is based on intraoperative fluoroscopic images, and the third type incorporates kinetic information about joints or morphometric data about the target bones acquired intraoperatively. Conclusion: The rapid integration of artificial intelligence and deep learning into the medical domain has a significant and transformative influence. Numerous studies utilizing deep learning-based diagnostics in orthopedics have remarkable achievements and performance. |
| Databáze: | Directory of Open Access Journals |
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