Evaluation of diagnostic tests using information theory for multi-class diagnostic problems and its application for the detection of occlusal caries lesions.

Background: Several methods are available to evaluate the performance of the tests when the purpose of the diagnostic test is to discriminate between two possible disease states. However multi-class diagnostic problems frequently appear in many areas of medical science. Hence, there is a need for methods which will enable us to characterize the accuracy of diagnostic tests when there are more than two possible disease states.

Aims: To show that two information theory measures, information content (IC) and proportional reduction in diagnostic uncertainty (PRDU), can be used for the evaluation of the performance of diagnostic tests for multi-class diagnostic problems that may appear in different areas of medical science.

Study Design: Diagnostic accuracy study.

Methods: Sixty freshly extracted permanent human molar and pre-molar teeth suspected to have occlusal caries lesions were selected for the study and were assessed by two experienced examiners. Each examiner performed two evaluations. Histological examination was used as the gold standard. The scores of the histological examination were defined as sound (n=11), enamel caries (n=22) and dentin caries (n=27). Diagnostic performance of i) visual inspection, ii) radiography, iii) laser fluorescence (LF) and iv) micro-computed tomography (M-CT) caries detection methods was evaluated by calculating IC and PRDU.

Results: Micro-computed tomography examination was the best method among the diagnostic techniques for the diagnosis of occlusal caries in terms of both IC and PRDU. M-CT examination supplied the maximum diagnostic information about the diagnosis of occlusal caries in the first (IC: 1.056; p<0.05), (PRDU: 70.5%) and second evaluation (IC: 1.105; p<0.05), (PRDU: 73.8%) for the first examiner. M-CT examination was the best method among the diagnostic techniques for the second examiner in both the first (IC:1.105; p<0.05), (PRDU:73.8%) and second evaluation (IC:1.061; p<0.05), (PRDU:70.8%). IC and PRDU were lowest for visual inspection.

Conclusion: The present study demonstrates that IC and PRDU can be used to evaluate diagnostic test performance when multiple disease states are being evaluated.