Development of Severity Index for Oral Epithelial Dysplasia Using Fuzzy Group Decision Making Algorithm

BackgroundOral epithelial dysplasia (OED) grading suffers from several levels of uncertainties and imprecision. An index is preferred in this context to improve reliability and reduce subjectivity in diagnostic decision making. In this study, a fuzzy logic-based disease severity index (SI) is formulated considering standard histopathological features used in OED grading. MethodsOral onco-pathologists were asked to independently provide weights of different features, according to their clinical significance in the context of dysplasia. Aggregated weight of each feature was calculated from individual assessment of different onco-pathologists by a fuzzy logic-based group decision making algorithm. Confidence levels of experts were also included to improve robustness of the method. SI was generated by integrating abnormality score of each feature with its weight. Abnormality degree of each feature was expressed in linguistic terms by onco-pathologists which were subsequently represented by a triangular fuzzy number. Fuzzy membership function was used as it can capture the ambiguity of experts opinion regarding abnormality of individual feature. Finally, defuzzification was used to get a crisp index from weighted sum of all features. ResultSI was found to be statistically different (p<0.01) for different grades of dysplasia i.e mild, moderate and severe with added advantage of stratifying each grade in low and high subcategory. ConclusionKey contribution of our work is that we have developed a fuzzy logic-based group consensus process regarding weights of histopathological features for OED grading. Present methodology of developing SI can be applied to other medical decision-making problems as well. HighlightsO_LIA severity index is proposed to improve reproducibility of oral epithelial dysplasia grading. C_LIO_LIClinical significance of each histopathological and cytopathological feature in the context of dysplasia is reflected by its weight. C_LIO_LIA fuzzy logic-based group decision making algorithm is used to reduce subjectivity of features weights. C_LIO_LIConfidence level of oral onco-pathologists are given due importance in derivation of aggregated weight of each feature. C_LIO_LIAbnormality score of each feature is evaluated in fuzzy scale to capture clinicians ambiguity. C_LIO_LIPresent methodology may be useful for developing indices of other diseases. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/633806v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@5a5e28org.highwire.dtl.DTLVardef@dba3a9org.highwire.dtl.DTLVardef@1d5cc7corg.highwire.dtl.DTLVardef@16404b5_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG