The shocking implications of Bayes’ theorem for diagnosing herniated nucleus pulposus based on MRI scans

Abstract

We obtain the input data for Bayes Theorem, and use the theorem to determine the probability of a patient having a lumbar HNP, given only a positive MRI. We also enumerate the potential consequences that the clinician must keep in mind when making the diagnosis of lumbar HNP. We used the theorem by Bayes, in conjunction with well-established results in the orthopedic literature, to calculate the probability of lumbar HNP given only a positive MRI finding. The necessary information provided by the orthopedic literature includes the prevalence of lumbar HNP, the probability of a positive MRI finding given that there is no HNP, and the probability of a positive MRI finding given that there is HNP. We found that the probability of lumbar HNP given only a positive MRI finding was 8%. The probability that there is no lumbar HNP, even when there is a positive MRI finding, is 92%. Clearly, MRI scans cannot be trusted as the sole source of diagnostic information.

Keywords

• bayes’ theorem
• herniated nucleus pulposus
• mri scans