Facial pain classification and outcome measurement

The taxonomies of pain disorders are challenging, and the classification of facial pain is no exception. Simplistic schemes designating broad categories such as trigeminal neuralgia (TN), atypical trigeminal neuralgia, and atypical facial pain suffered from having no objective category boundaries or grounding in patient history. On the other hand, complex categorization of craniofacial pains yields myriad subtypes and small subgroups, making statistical analysis more difficult. Fifteen years ago, we proposed a facial pain classification scheme based solely on patient history. This classification scheme is implemented on a free, web-based application using an artificial neural network expert system. The application allows anyone seeking to establish a facial pain diagnosis to anonymously answer questions related to the nature of their facial pain. The system has proven to have a high specificity and sensitivity for all diagnostic categories, particularly for classic TN. The goal of such a diagnostic patient-driven system is to arm patients with a diagnosis, to allow them to seek out proper care, and to avoid ineffective or ill-advised medical and or surgical therapy. Medical treatment should be carefully and individually managed and is a first-line therapy. However, TN is a progressive condition, and medical intractability is inevitable in most patients. TN is unique, in that surgical therapy is highly successful in most cases. However, surgical treatment of TN should be appropriate to patient age, medical status, and imaging results. Facial pain is also unique, in that the diagnosis has strong correlation with the outcome of surgery, and successful pain relief.