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Life
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Recent Advances and Future Directions in Complex Spinal Surgery
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Recent Advances and Future Directions in Complex Spinal Surgery

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".

Deadline for manuscript submissions: closed (19 May 2023) | Viewed by 6588

Special Issue Editor

Dr. Lee A. Tan

E-Mail Website
Guest Editor
School of Medicine, University of California, San Francisco, CA, USA
Interests: spinal biomechanics; cervical spine; spinal deformity; minimally invasive; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Life is committed to presenting original research that underscores the advancements in each discipline of medicine. Now more than ever, attention should be dedicated to headways in the exciting practice of spine surgery. Over the past decade, spine surgery has leaped in incredible strides that span from minimally invasive techniques to robotic to innovative technologies such as augmented reality and machine learning/artificial intelligence. This remarkable progress has evolved the diagnoses, surgical plannings, and operative skills in spine surgery. Surgeons, patients, and supporting specialties have all reaped the fruits of novel expansions in spine.

This Special Issue, “Recent Advances in Spine Surgery”, aims to highlight the key developments that have shaped the field in recent years. Experts in each topic are asked to weigh in on how their nascent areas of interest have impacted surgical approaches, patient-reported outcomes, life expectancies, and radiographic corrections in spine. Pioneers within each subject comprise both neurosurgeons and orthopedic surgeons, who are invited to speak from their years of experience to ensure each publication makes a meaningful contribution to the literature. The intended audience includes spine surgeons, physicians, and healthcare professionals alike, whose understanding of spine surgery will certainly broaden.

Dr. Lee A. Tan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spinal biomechanics
  • cervical spondylotic myelopathy
  • thoracic disc herniation
  • spinal deformity
  • spondylolisthesis
  • minimally invasive
  • robotic
  • spinal tumor
  • spinal trauma
  • complication avoidance
 

Published Papers (2 papers)

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Review

13 pages, 2853 KiB  
Review
The Role of Magnetic Resonance Imaging and Computed Tomography in Spinal Cord Injury
by Omar Hussain, Mayank Kaushal, Nitin Agarwal, Shekar Kurpad and Saman Shabani
Life 2023, 13(8), 1680; https://doi.org/10.3390/life13081680 - 3 Aug 2023
Viewed by 2839
Abstract
Traumatic injuries of the spine are associated with long-term morbidity and mortality. Timely diagnosis and appropriate management of mechanical instability and spinal cord injury are important to prevent further neurologic deterioration. Spine surgeons require an understanding of the essential imaging techniques concerning the [...] Read more.
Traumatic injuries of the spine are associated with long-term morbidity and mortality. Timely diagnosis and appropriate management of mechanical instability and spinal cord injury are important to prevent further neurologic deterioration. Spine surgeons require an understanding of the essential imaging techniques concerning the diagnosis, management, and prognosis of spinal cord injury. We present a review in the role of computed tomography (CT) including advancements in multidetector CT (MDCT), dual energy CT (DECT), and photon counting CT, and how it relates to spinal trauma. We also review magnetic resonance imaging (MRI) and some of the developed MRI based classifications for prognosticating the severity and outcome of spinal cord injury, such as diffusion weighted imaging (DWI), diffusion tractography (DTI), functional MRI (fMRI), and perfusion MRI. Full article
(This article belongs to the Special Issue Recent Advances and Future Directions in Complex Spinal Surgery)
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13 pages, 410 KiB  
Review
Current Applications of Machine Learning for Spinal Cord Tumors
by Konstantinos Katsos, Sarah E. Johnson, Sufyan Ibrahim and Mohamad Bydon
Life 2023, 13(2), 520; https://doi.org/10.3390/life13020520 - 14 Feb 2023
Cited by 4 | Viewed by 3353
Abstract
Spinal cord tumors constitute a diverse group of rare neoplasms associated with significant mortality and morbidity that pose unique clinical and surgical challenges. Diagnostic accuracy and outcome prediction are critical for informed decision making and can promote personalized medicine and facilitate optimal patient [...] Read more.
Spinal cord tumors constitute a diverse group of rare neoplasms associated with significant mortality and morbidity that pose unique clinical and surgical challenges. Diagnostic accuracy and outcome prediction are critical for informed decision making and can promote personalized medicine and facilitate optimal patient management. Machine learning has the ability to analyze and combine vast amounts of data, allowing the identification of patterns and the establishment of clinical associations, which can ultimately enhance patient care. Although artificial intelligence techniques have been explored in other areas of spine surgery, such as spinal deformity surgery, precise machine learning models for spinal tumors are lagging behind. Current applications of machine learning in spinal cord tumors include algorithms that improve diagnostic precision by predicting genetic, molecular, and histopathological profiles. Furthermore, artificial intelligence-based systems can assist surgeons with preoperative planning and surgical resection, potentially reducing the risk of recurrence and consequently improving clinical outcomes. Machine learning algorithms promote personalized medicine by enabling prognostication and risk stratification based on accurate predictions of treatment response, survival, and postoperative complications. Despite their promising potential, machine learning models require extensive validation processes and quality assessments to ensure safe and effective translation to clinical practice. Full article
(This article belongs to the Special Issue Recent Advances and Future Directions in Complex Spinal Surgery)
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