Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study
Fatima Badahman,
Mashael Alsobhi,
Almaha Alzahrani,
Mohamed Faisal Chevidikunnan,
Ziyad Neamatallah,
Abdullah Alqarni,
Umar Alabasi,
Ahmed Abduljabbar,
Reem Basuodan,
Fayaz Khan
Affiliations
Fatima Badahman
Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah 22252, Saudi Arabia
Mashael Alsobhi
Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah 22252, Saudi Arabia
Almaha Alzahrani
Department of Physical Therapy, King Faisal Hospital, Makkah 24236, Saudi Arabia
Mohamed Faisal Chevidikunnan
Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah 22252, Saudi Arabia
Ziyad Neamatallah
Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah 22252, Saudi Arabia
Abdullah Alqarni
Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah 22252, Saudi Arabia
Umar Alabasi
Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah 22252, Saudi Arabia
Ahmed Abduljabbar
Department of Radiology, Faculty of Medicine, King Abdulaziz University, Jeddah 22252, Saudi Arabia
Reem Basuodan
Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
Fayaz Khan
Department of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah 22252, Saudi Arabia
Background: Low back pain (LBP) is a major cause of disability globally, and the diagnosis of LBP is challenging for clinicians. Objective: Using new software called Therapha, this study aimed to assess the accuracy level of artificial intelligence as a Clinical Decision Support System (CDSS) compared to MRI in predicting lumbar disc herniated patients. Methods: One hundred low back pain patients aged ≥18 years old were included in the study. The study was conducted in three stages. Firstly, a case series was conducted by matching MRI and Therapha diagnosis for 10 patients. Subsequently, Delphi methodology was employed to establish a clinical consensus. Finally, to determine the accuracy of the newly developed software, a cross-sectional study was undertaken involving 100 patients. Results: The software showed a significant diagnostic accuracy with the area under the curve in the ROC analysis determined as 0.84 with a sensitivity of 88% and a specificity of 80%. Conclusions: The study’s findings revealed that CDSS using Therapha has a reasonable level of efficacy, and this can be utilized clinically to acquire a faster and more accurate screening of patients with lumbar disc herniation.
