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RISS 인기검색어
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- 저자 : Christopher M. Mikhail (검색결과 2 건)
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Biomaterials in Spinal Implants: A Review
Andrew Warburton,Steven J. Girdler,Christopher M. Mikhail,Amy Ahn,Samuel K. Cho 대한척추신경외과학회 2020 Neurospine Vol.17 No.1
The aim to find the perfect biomaterial for spinal implant has been the focus of spinal research since the 1800s. Spinal surgery and the devices used therein have undergone a constant evolution in order to meet the needs of surgeons who have continued to further understand the biomechanical principles of spinal stability and have improved as new technologies and materials are available for production use. The perfect biomaterial would be one that is biologically inert/compatible, has a Young’s modulus similar to that of the bone where it is implanted, high tensile strength, stiffness, fatigue strength, and low artifacts on imaging. Today, the materials that have been most commonly used include stainless steel, titanium, cobalt chrome, nitinol (a nickel titanium alloy), tantalum, and polyetheretherketone in rods, screws, cages, and plates. Current advancements such as 3-dimensional printing, the ProDisc-L and ProDisc-C, the ApiFix, and the Mobi-C which all aim to improve range of motion, reduce pain, and improve patient satisfaction. Spine surgeons should remain vigilant regarding the current literature and technological advancements in spinal materials and procedures. The progression of spinal implant materials for cages, rods, screws and plates with advantages and disadvantages for each material will be discussed.
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Applications of Machine Learning Using Electronic Medical Records in Spine Surgery
John T. Schwartz,Michael Gao,Eric A. Geng,Kush S. Mody,Christopher M. Mikhail,Samuel K. Cho 대한척추신경외과학회 2019 Neurospine Vol.16 No.4
Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly, and the field of spine surgery is no exception. Electronic medical records are a key source of medical data that can be leveraged for the creation of clinically valuable machine learning algorithms. This review examines the current state of machine learning using electronic medical records as it applies to spine surgery. Studies across the electronic medical record data domains of imaging, text, and structured data are reviewed. Discussed applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance, among others. The limitations and future challenges for machine learning research using electronic medical records are also discussed.
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