Artificial intelligence and treatment algorithms in spine surgery

Charles, Yann Philippe; Lamas, Vincent; Ntilikina, Yves

doi:10.1016/j.otsr.2022.103456

Cited by 16 publications

(9 citation statements)

References 32 publications

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“…8,9 Applications of ML have also identified patients likely to develop postsurgical complications and readmission to the hospital. 3,[10][11][12] This review will serve as an overview of current ML modalities, limitations in its implementation, and future directions in spine surgery (Figure).…”

Section: History Of Machine Learning In Spine Surgerymentioning

confidence: 99%

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Machine Learning in Spine Surgery: A Narrative Review

Adida,

Legarreta,

Hudson

et al. 2023

Neurosurgery

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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.

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Section: History Of Machine Learning In Spine Surgerymentioning

confidence: 99%

“…Surgeons are continually incorporating ML into their practice. 2,10,12,13,122 A novel future direction is the development of mixed reality (MR). MR uses complex image overlay projected on the natural visual field.…”

Section: Future Applications Of Machine Learning In Spinementioning

confidence: 99%

Machine Learning in Spine Surgery: A Narrative Review

Adida,

Legarreta,

Hudson

et al. 2023

Neurosurgery

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“…Arti cial intelligence (AI) has shown potential in providing simple, fast, and accurate measurement methods [10,11]. Recently, many studies have utilised deep learning (DL) techniques to measure sagittal parameters.…”

Section: Introductionmentioning

confidence: 99%

“…They are closely related to pain, function, and health-related quality of life outcomes [3][4][5][6]. However, two signi cant challenges exist in measuring pelvic parameters in clinical practice, as follows: 1) novice physicians lack measurement experience and are prone to making errors; 2) the process is time-consuming[7-9].Arti cial intelligence (AI) has shown potential in providing simple, fast, and accurate measurement methods [10,11]. Recently, many studies have utilised deep learning (DL) techniques to measure sagittal parameters.…”

mentioning

confidence: 99%

Accurate automatic measurement of spinopelvic parameters with a one-stage deep learning technique

Meng,

Liu,

feng

et al. 2024

Preprint

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Background: The current method of measuring parameters in spinal imaging manually is time-consuming and prone to inconsistencies. This study proposed and validated a novel method to automate the measurement of pelvic parameters using a one-stage deep learning (DL) model. Methods: Spinopelvic parameters, including pelvic incidence (PI), sacral slope (SS), and pelvic tilt (PT), were measured from full body radiographs of patients by three evaluators and by using our proposed method. Our proposed one-stage DL model was based on keypoint localisation. Landmark localisation error was used to evaluate the performance of landmark localisation. To evaluate the agreement between our method and the human evaluators, the analysis of average error, standard deviation, and intra- and inter-evaluator reliability was conducted using the intraclass correlation coefficient (ICC) and Pearson's correlation coefficient (R). Results:The method achieved excellent measurement performance for spinopelvic parameters. The distribution of the landmark localisation errors was within a reasonable range (median error, 2.28–4.01 mm). ICC values for the assessment of the intra- (range: 0.941–0.996) and inter-evaluator (0.994–0.998) reliability of human evaluators were excellent. The method was able to determine spinopelvic parameters with excellent ICC values (0.919-0.997) and R value (R >0.899, p<0.001, all). Meanwhile, the detection speed of the algorithm was approximately 30 times faster than that of manual measurements of spinopelvic parameters. Conclusions:This one-step automated measurement method is less time-consuming and has excellent reliability and agreement with human evaluators.

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“…Es visible como la aplicación de estrategias de aprendizaje automático se han convertido en una poderosa rama de la inteligencia artificial que se representa es un conjunto de teorías y técnicas para simular la inteligencia humana utilizando máquinas que utilizan programas informáticos complejos (Charles et al, 2022) y que en muchos de los casos viene siendo utilizado con éxito en distintas industrias (Álvarez Vega et al, 2020). Es por ello que el empleo de mecanismos asociados a tareas de inteligencia artificial podría dar resultados aceptables teniendo en cuenta que este tipo de alternativas se encuentra vigente desde el año 1950 con la responsabilidad de descubrir reglas del pensamiento que logren representar un enfoque más complejo sobre lo que se pensaba o imaginaba.…”

Section: Introductionunclassified

Modelo de red neuronal artificial para la predicción del fenómeno “el Niño” en la región de Piura (Perú)

Cáceres

Nima

et al. 2023

revTECHNO

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El Fenómeno El Niño es un evento de origen climático. En el año 2017 este evento impacto la región Piura, Perú. Las fuertes precipitaciones pluviales generaron el desborde el río Piura que afecto los distritos de Piura, Castilla, Catacaos y Cura Mori. El objetivo del estudio es identificar las variables más influyentes y disponer de un modelo que prediga la ocurrencia del Fenómeno El Niño en la región Piura, mediante el uso de Redes Neuronales Artificiales. Los resultados indican que la Temperatura Superficial del Mar es la variable más influyente. El modelo tiene una precisión del 82% de precisión.

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Artificial intelligence and treatment algorithms in spine surgery

Cited by 16 publications

References 32 publications

Machine Learning in Spine Surgery: A Narrative Review

Machine Learning in Spine Surgery: A Narrative Review

Accurate automatic measurement of spinopelvic parameters with a one-stage deep learning technique

Modelo de red neuronal artificial para la predicción del fenómeno “el Niño” en la región de Piura (Perú)

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