Current Applications of Machine Learning for Spinal Cord Tumors
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 facili...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/13/2/520 |
| _version_ | 1797619829452046336 |
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| author | Konstantinos Katsos Sarah E. Johnson Sufyan Ibrahim Mohamad Bydon |
| author_facet | Konstantinos Katsos Sarah E. Johnson Sufyan Ibrahim Mohamad Bydon |
| author_sort | Konstantinos Katsos |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T08:32:24Z |
| format | Article |
| id | doaj.art-58af71dc9657417db46e795afa9861db |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-11T08:32:24Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-58af71dc9657417db46e795afa9861db2023-11-16T21:42:24ZengMDPI AGLife2075-17292023-02-0113252010.3390/life13020520Current Applications of Machine Learning for Spinal Cord TumorsKonstantinos Katsos0Sarah E. Johnson1Sufyan Ibrahim2Mohamad Bydon3Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USADepartment of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USADepartment of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USADepartment of Neurologic Surgery, Mayo Clinic, Rochester, MN 55902, USASpinal 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.https://www.mdpi.com/2075-1729/13/2/520machine learningartificial intelligencedeep learningspinal cord tumors |
| spellingShingle | Konstantinos Katsos Sarah E. Johnson Sufyan Ibrahim Mohamad Bydon Current Applications of Machine Learning for Spinal Cord Tumors Life machine learning artificial intelligence deep learning spinal cord tumors |
| title | Current Applications of Machine Learning for Spinal Cord Tumors |
| title_full | Current Applications of Machine Learning for Spinal Cord Tumors |
| title_fullStr | Current Applications of Machine Learning for Spinal Cord Tumors |
| title_full_unstemmed | Current Applications of Machine Learning for Spinal Cord Tumors |
| title_short | Current Applications of Machine Learning for Spinal Cord Tumors |
| title_sort | current applications of machine learning for spinal cord tumors |
| topic | machine learning artificial intelligence deep learning spinal cord tumors |
| url | https://www.mdpi.com/2075-1729/13/2/520 |
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