Machine Learning in Spine Surgery: A Narrative Review

Introduction Preoperative neurosurgical planning is a keen step to avoiding surgical complications, reducing morbidity, and improving patient safety. The incursion of machine learning (ML) in this domain has recently gained attention, given the notable advantages in processing large data sets and potentially generating efficient and accurate algorithms in patient care. Objective To evaluate the evolving applications of ML algorithms in the preoperative planning of brain and spine surgery. Methods In accordance with the Arksey and O'Malley framework, a scoping review was conducted using three databases (Pubmed, Embase, and Web of Science). Articles that described the use of ML for preoperative planning in brain and spine surgery were included. Relevant data were collected regarding the neurosurgical field of application, patient baseline features, disease description, type of ML technology, study's aim, preoperative ML algorithm description, and advantages and limitations of ML algorithms. Results Our search strategy yielded 7,407 articles, of which 8 studies (5 retrospective, 2 prospective, and 1 experimental study) satisfied the inclusion criteria. Clinical information from 518 patients (62.7% female; mean age: 44.8 years) was used for generating ML algorithms, including convolutional neural network (14.3%), logistic regression (14.3%), random forest (14.3%), and other algorithms (Table 1). Neurosurgical fields of applications included functional neurosurgery (37.5%), tumor surgery (37.5%), and spine surgery (25%). The main advantages of ML included automated processing of clinical and imaging information, selection of an individualized patient surgical approach and data-driven support for treatment decision-making. All studies reported technical limitations, such as long processing time, algorithmic bias, limited generalizability, and the need for database updating and maintenance. Conclusion ML algorithms for preoperative neurosurgical planning are being developed for efficient, automated, and safe treatment decision-making. Enhancing the robustness, transparency, and understanding of ML applications will be crucial for their successful integration into neurosurgical practice.

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Integrating Augmented Reality in Spine Surgery: Redefining Precision with New Technologies

De Jesus Encarnacion Ramirez,

Chmutin,

Nurmukhametov

et al. 2024

Brain Sciences

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Introduction: The integration of augmented reality (AR) in spine surgery marks a significant advancement, enhancing surgical precision and patient outcomes. AR provides immersive, three-dimensional visualizations of anatomical structures, facilitating meticulous planning and execution of spine surgeries. This technology not only improves spatial understanding and real-time navigation during procedures but also aims to reduce surgical invasiveness and operative times. Despite its potential, challenges such as model accuracy, user interface design, and the learning curve for new technology must be addressed. AR’s application extends beyond the operating room, offering valuable tools for medical education and improving patient communication and satisfaction. Material and methods: A literature review was conducted by searching PubMed and Scopus databases using keywords related to augmented reality in spine surgery, covering publications from January 2020 to January 2024. Results: In total, 319 articles were identified through the initial search of the databases. After screening titles and abstracts, 11 articles in total were included in the qualitative synthesis. Conclusion: Augmented reality (AR) is becoming a transformative force in spine surgery, enhancing precision, education, and outcomes despite hurdles like technical limitations and integration challenges. AR’s immersive visualizations and educational innovations, coupled with its potential synergy with AI and machine learning, indicate a bright future for surgical care. Despite the existing obstacles, AR’s impact on improving surgical accuracy and safety marks a significant leap forward in patient treatment and care.

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Machine Learning in Spine Surgery: A Narrative Review

Cited by 3 publications

References 140 publications

Text-to-video generative artificial intelligence: sora in neurosurgery

Text-to-video generative artificial intelligence: sora in neurosurgery

Machine Learning Algorithms for Neurosurgical Preoperative Planning: A Comprehensive Scoping Review of the Literature

Integrating Augmented Reality in Spine Surgery: Redefining Precision with New Technologies

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