Spine literature: Machine Learning in Spine Surgery: A Narrative Review.

Summary (English)

Educational summary of peer-reviewed work on spinal disorders (not medical advice).

Article: Machine Learning in Spine Surgery: A Narrative Review.

Authors (excerpt): Samuel Adida, Andrew D Legarreta, Joseph S Hudson, David McCarthy, Edward Andrews

Overview: Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.

Full citation: Machine Learning in Spine Surgery: A Narrative Review.. Neurosurgery. PubMed: PMID 37930259; DOI: 10.1227/neu.0000000000002660.

Consult the original paper for methods, statistics, and clinical applicability.

Summary (Bulgarian)

Това е обобщение за пациенти и специалисти, базирано на официалното резюме в PubMed. Публикацията е в списание Neurosurgery и засяга теми, свързани с гръбначния стълб (диагностика, лечение или епидемиология — според съдържанието на оригиналната статия). Не замества очен преглед при лекар и не представлява персонализиран медицински съвет.

Заглавие: Spine literature: Machine Learning in Spine Surgery: A Narrative Review.

Оригинално резюме (английски, съкратено): Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in…

Source: Machine Learning in Spine Surgery: A Narrative Review.. Neurosurgery. PubMed: PMID 37930259; DOI: 10.1227/neu.0000000000002660.