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Document Type |
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Thesis |
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Document Title |
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The Accuracy of Artificial Intelligence as Clinical Decision Support System in Diagnosing Cervical Radiculopathy due to Disc Herniation and Spondylosis قياس دقة خوارزمية الذكاء الاصطناعي كنظام دعم للقرار السريري في تشخيص الانزلاق الغضروفي وداء الفقار في الفقرات العنقية |
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Subject |
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Faculty of Medical Rehabilitation Sciences |
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Document Language |
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Arabic |
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Abstract |
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Background & Objective: Neck pain is one of the most prevalent musculoskeletal conditions worldwide, even in Saudi Arabia, and cervical radiculopathy is one of the most important causes of these problems. MRI is used worldwide as the ideal diagnostic tool to diagnose these problems, but they are expensive for many patients. Meanwhile, others may suffer from other health diseases that prevent them from undergoing these radiations, which prevents them from diagnosing the condition more appropriately. Considering these factors, artificial intelligence could be an appropriate, accurate, and suitable model for diagnosing cervical radiculopathy. Therefore, the objective of this study was to compare the accuracy of an AI-enabled platform and an Algorithm as a Clinical Decision Support System (CDSS) versus MRI in triaging and diagnosing patients affected with cervical disc herniation and spondylosis.
Methodology: Ninety-two male and female patients above 18 years of age who suffer from neck pain were included in the study. The personal and clinical history was taken using the Therapha™ software on the same day or 2 to 3 days before the patient undergoes an MRI. First, the Delphi method was used for ten cases to define expert consensus for software. Then, the diagnostic accuracy of AI was determined in terms of sensitivity and specificity compared with MRI.
Results: The results of the Delphi method showed that the Therapha™ software had a 100% agreement for nine cases and 80% agreement for one case by the experts with software. The software showed a high sensitivity (89.5%) and specificity (62.5%) in triaging and diagnosing cervical radiculopathy compared with MRI.
Conclusion & Recommendation: The study results conclude that the Therapha™ software showed high sensitivity and specificity in diagnosing cervical radiculopathy. So thereby, the AI could be used to triage and diagnose cervical radiculopathy, which could be highly recommended in rehabilitation centers where highly sophisticated radio-diagnostic facilities are unavailable.
Keywords: Artificial Intelligence (AI); Diagnostic Accuracy; Delphi Method; Cervical Radiculopathy; Disc Herniation; Spondylosis. |
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Supervisor |
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Dr. Mohamed Faisal Chevidikunnan |
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Thesis Type |
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Master Thesis |
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Publishing Year |
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1445 AH
2023 AD |
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Co-Supervisor |
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Dr. Umar Alabasi |
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Added Date |
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Monday, October 16, 2023 |
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Researchers
| المها خالد الزهراني | Alzahrani, Almaha Khalid | Researcher | Master | |
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