Predicting postoperative outcomes in lumbar spinal fusion: development of a machine learning model

Schönnagel, Lukas; Caffard, Thomas; Vu-Han, Tu-Lan; Zhu, Jiaqi; Nathoo, Isaac; Finos, Kyle; Camino-Willhuber, Gaston; Tani, Soji; Guven, Ali. E.; Haffer, Henryk; Muellner, Maximilian; Arzani, Artine; Chiapparelli, Erika; Amoroso, Krizia; Shue, Jennifer; Duculan, Roland; Pumberger, Matthias; Zippelius, Timo; Sama, Andrew A.; Cammisa, Frank P.; Girardi, Federico P.; Mancuso, Carol A.; Hughes, Alexander P.

doi:10.1016/j.spinee.2023.09.029

Cited by 5 publications

(6 citation statements)

References 51 publications

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“…By leveraging machine learning algorithms to analyze and learn from clinical data, healthcare providers can enhance quality assurance, performance monitoring, and outcome measurement in spine surgeries, leading to improved patient safety, reduced variability in care delivery, and enhanced overall quality of care. This data-driven approach to quality improvement and innovation empowers healthcare providers to continuously optimize care delivery, enhance patient safety, and drive advancements in spine surgery practices through evidence-based decision-making and continuous learning [ 19 – 21 ].…”

Section: Leveraging Machine Learning For Enhanced Patient Safety In S...mentioning

confidence: 99%

“…By integrating data from various sources, such as electronic health records, imaging studies, genetic information, and patient-reported outcomes, machine learning algorithms can generate personalized treatment recommendations that take into account the unique characteristics of each patient. This personalized approach enables healthcare providers to tailor surgical interventions, rehabilitation plans, and follow-up care to meet the specific needs and preferences of individual patients, leading to improved outcomes and enhanced patient satisfaction [ 18 , 19 ].…”

Section: Personalized Care Through Machine Learning: Transforming Spi...mentioning

confidence: 99%

“…By analyzing extensive patient data encompassing medical histories, imaging results, surgical details, and outcomes, AI algorithms can detect risk factors, forecast potential complications, and stratify patients based on their distinct risk profiles. This predictive ability equips healthcare providers to preemptively address risks, tailor treatments, and deploy targeted interventions to curtail complications and optimize results in spine surgeries [ 19 , 21 ].…”

Section: Mitigating Risks In Spine Surgeries: the Role Of Artificial ...mentioning

confidence: 99%

“…Through the utilization of predictive modeling and risk stratification algorithms, AI can assist healthcare providers in identifying patients at higher risk of complications, customizing treatment plans to individual patient profiles, and implementing targeted interventions to bolster patient safety and enhance outcomes in spine procedures. This personalized approach to surgical decision-making equips healthcare providers to deliver patient-centered care that is tailored to each patient’s distinct needs, characteristics, and risk factors, ultimately enhancing safety and care quality in spine surgeries [ 17 – 19 , 31 ].…”

Section: Enhancing Surgical Decision-making With Ai: a Focus On Patie...mentioning

confidence: 99%

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The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review

Arjmandnia,

Alimohammadi

2024

Patient Saf Surg

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Machine learning algorithms have the potential to significantly improve patient safety in spine surgeries by providing healthcare professionals with valuable insights and predictive analytics. These algorithms can analyze preoperative data, such as patient demographics, medical history, and imaging studies, to identify potential risk factors and predict postoperative complications. By leveraging machine learning, surgeons can make more informed decisions, personalize treatment plans, and optimize surgical techniques to minimize risks and enhance patient outcomes. Moreover, by harnessing the power of machine learning, healthcare providers can make data-driven decisions, personalize treatment plans, and optimize surgical interventions, ultimately enhancing the quality of care in spine surgery. The findings highlight the potential of integrating artificial intelligence in healthcare settings to mitigate risks and enhance patient safety in surgical practices. The integration of machine learning holds immense potential for enhancing patient safety in spine surgeries. By leveraging advanced algorithms and predictive analytics, healthcare providers can optimize surgical decision-making, mitigate risks, and personalize treatment strategies to improve outcomes and ensure the highest standard of care for patients undergoing spine procedures. As technology continues to evolve, the future of spine surgery lies in harnessing the power of machine learning to transform patient safety and revolutionize surgical practices. The present review article was designed to discuss the available literature in the field of machine learning techniques to enhance patient safety in spine surgery.

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Section: Leveraging Machine Learning For Enhanced Patient Safety In S...mentioning

confidence: 99%

Section: Personalized Care Through Machine Learning: Transforming Spi...mentioning

confidence: 99%

Section: Mitigating Risks In Spine Surgeries: the Role Of Artificial ...mentioning

confidence: 99%

Section: Enhancing Surgical Decision-making With Ai: a Focus On Patie...mentioning

confidence: 99%

See 2 more Smart Citations

The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review

Arjmandnia,

Alimohammadi

2024

Patient Saf Surg

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“…5 The assessment of PMA can be helpful for understanding the disease status of the surgical spine patients, aiding in predicting surgical outcomes. 6,7 It is of great relevance for nonsurgical patients with chronic back pain as well, by monitoring how therapeutic interventions such as muscle stimulating implants, both aimed at tackling back pain by restoring the stabilizing function of the paraspinal muscles. 8,9 Though PMA can be assessed on CT and MRI images, 10 such imaging is reserved for patients with a specific spinal indication, due to the cost and radiation exposure.…”

Section: Introductionmentioning

confidence: 99%

Assessing paraspinal muscle atrophy with electrical impedance myography: Limitations and insights

Guven,

Chiapparelli,

Camino‐Willhuber

et al. 2024

Journal Orthopaedic Research

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Paraspinal muscle atrophy is gaining attention in spine surgery due to its link to back pain, spinal degeneration and worse postoperative outcomes. Electrical impedance myography (EIM) is a noninvasive diagnostic tool for muscle quality assessment, primarily utilized for patients with neuromuscular diseases. However, EIM's accuracy for paraspinal muscle assessment remains understudied. In this study, we investigated the correlation between EIM readings and MRI‐derived muscle parameters, as well as the influence of dermal and subcutaneous parameters on these readings. We retrospectively analyzed patients with lumbar spinal degeneration who underwent paraspinal EIM assessment between May 2023 to July 2023. Paraspinal muscle fatty infiltration (FI) and functional cross‐sectional area (fCSA), as well as the subcutaneous thickness were assessed on MRI scans. Skin ultrasound imaging was assessed for dermal thickness and the echogenicities of the dermal and subcutaneous layers. All measurements were performed on the bilaterally. The correlation between EIM readings were compared with ultrasound and MRI parameters using Spearman's correlation analyses. A total of 20 patients (65.0% female) with a median age of 69.5 years (IQR, 61.3–73.8) were analyzed. The fCSA and FI did not significantly correlate with the EIM readings, regardless of frequency. All EIM readings across frequencies correlated with subcutaneous thickness, echogenicity, or dermal thickness. With the current methodology, paraspinal EIM is not a valid alternative to MRI assessment of muscle quality, as it is strongly influenced by the dermal and subcutaneous layers. Further studies are required for refining the methodology and confirming our results.

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Moving towards the use of artificial intelligence in pain management

Antel,

Whitelaw,

Gore

et al. 2024

European Journal of Pain

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Background and ObjectiveWhile the development of artificial intelligence (AI) technologies in medicine has been significant, their application to acute and chronic pain management has not been well characterized. This systematic review aims to provide an overview of the current state of AI in acute and chronic pain management.Databases and Data TreatmentThis review was registered with PROSPERO (ID# CRD42022307017), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in four electronic databases (Embase, Medline, Central, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for acute and chronic pain management.ResultsFrom the 17,601 records identified in the initial search, 197 were included in this review. Identified applications of AI were described for treatment planning as well as treatment delivery. Described uses include prediction of pain, forecasting of individualized responses to treatment, treatment regimen tailoring, image‐guidance for procedural interventions and self‐management tools. Multiple domains of AI were used including machine learning, computer vision, fuzzy logic, natural language processing and expert systems.ConclusionThere is growing literature regarding applications of AI for pain management, and their clinical use holds potential for improving patient outcomes. However, multiple barriers to their clinical integration remain including lack validation of such applications in diverse patient populations, missing infrastructure to support these tools and limited provider understanding of AI.SignificanceThis review characterizes current applications of AI for pain management and discusses barriers to their clinical integration. Our findings support continuing efforts directed towards establishing comprehensive systems that integrate AI throughout the patient care continuum.

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Predicting postoperative outcomes in lumbar spinal fusion: development of a machine learning model

Cited by 5 publications

References 51 publications

The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review

The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review

Assessing paraspinal muscle atrophy with electrical impedance myography: Limitations and insights

Moving towards the use of artificial intelligence in pain management

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