  Objective: Until now, traditional invasive approaches have been the only
means being leveraged to diagnose spinal disorders. Traditional manual
diagnostics require a high workload, and diagnostic errors are likely to occur
due to the prolonged work of physicians. In this research, we develop an expert
system based on a hybrid inference algorithm and comprehensive integrated
knowledge for assisting the experts in the fast and high-quality diagnosis of
spinal disorders.
  Methods: First, for each spinal anomaly, the accurate and integrated
knowledge was acquired from related experts and resources. Second, based on
probability distributions and dependencies between symptoms of each anomaly, a
unique numerical value known as certainty effect value was assigned to each
symptom. Third, a new hybrid inference algorithm was designed to obtain
excellent performance, which was an incorporation of the Backward Chaining
Inference and Theory of Uncertainty.
  Results: The proposed expert system was evaluated in two different phases,
real-world samples, and medical records evaluation. Evaluations show that in
terms of real-world samples analysis, the system achieved excellent accuracy.
Application of the system on the sample with anomalies revealed the degree of
severity of disorders and the risk of development of abnormalities in unhealthy
and healthy patients. In the case of medical records analysis, our expert
system proved to have promising performance, which was very close to those of
experts.
  Conclusion: Evaluations suggest that the proposed expert system provides
promising performance, helping specialists to validate the accuracy and
integrity of their diagnosis. It can also serve as an intelligent educational
software for medical students to gain familiarity with spinal disorder
diagnosis process, and related symptoms.
