Version 1
: Received: 17 July 2024 / Approved: 17 July 2024 / Online: 17 July 2024 (10:59:34 CEST)
How to cite:
Badahman, F.; Alsobhi, M.; Alzahrani, A.; Chevidikunnan, M. F.; Neamatallah, Z.; Alqarni, A.; Alabasi, U.; Abduljabbar, A.; Basuodan, R.; Khan, F. Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study. Preprints2024, 2024071412. https://doi.org/10.20944/preprints202407.1412.v1
Badahman, F.; Alsobhi, M.; Alzahrani, A.; Chevidikunnan, M. F.; Neamatallah, Z.; Alqarni, A.; Alabasi, U.; Abduljabbar, A.; Basuodan, R.; Khan, F. Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study. Preprints 2024, 2024071412. https://doi.org/10.20944/preprints202407.1412.v1
Badahman, F.; Alsobhi, M.; Alzahrani, A.; Chevidikunnan, M. F.; Neamatallah, Z.; Alqarni, A.; Alabasi, U.; Abduljabbar, A.; Basuodan, R.; Khan, F. Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study. Preprints2024, 2024071412. https://doi.org/10.20944/preprints202407.1412.v1
APA Style
Badahman, F., Alsobhi, M., Alzahrani, A., Chevidikunnan, M. F., Neamatallah, Z., Alqarni, A., Alabasi, U., Abduljabbar, A., Basuodan, R., & Khan, F. (2024). Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study. Preprints. https://doi.org/10.20944/preprints202407.1412.v1
Chicago/Turabian Style
Badahman, F., Reem Basuodan and Fayaz Khan. 2024 "Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study" Preprints. https://doi.org/10.20944/preprints202407.1412.v1
Abstract
Background: Low back pain (LBP) is a major cause of disability globally, and the diagnosis of LBP is challenging for clinicians. Objective: This study aimed to assess the accuracy level of arti-ficial intelligence as a Clinical Decision Support System (CDSS) called Therapa 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 3 stages, at first, a case se-ries was conducted by matching MRI and Therapha diagnosis for 10 patients. Subsequently, Delphi methodology was employed to establish a clinical consensus. Lastly, to determine the ac-curacy 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%. Conclusion: The study’s findings revealed that CDSS using Therapha has a reasonable level of ef-ficacy, and this can be utilized clinically to acquire faster and more accurate screening of patients with lumbar disc herniation.
Keywords
Artificial Intelligence; Machine Learning; Back Pain; Clinical Decision Support System; Lumbar; Disc Herniation.
Subject
Medicine and Pharmacology, Orthopedics and Sports Medicine
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
