Abstract: The invention provides methods for determining the probability that a given region of a tissue sample contains tissue of a given category, such as CIN 1 (cervical intraepithelial neoplasia, grade 1), CIN 2/3 (cervical intraepithelial neoplasia grades 2 and/or 3), normal squamous, normal columnar, and metaplasia, for example. The invention provides increased diagnostic accuracy by combining a plurality of statistical classification techniques. Furthermore, in one embodiment, the invention comprises combining one or more statistical techniques with one or more non-statistical classification techniques.

Abstract: The invention provides methods for processing tissue-derived optical data for use in a classification algorithm. Methods of the invention comprise application of image masks for automatically identifying ambiguous or unclassifiable optical data. The optical data may comprise, for example, spectral data and/or acetowhitening kinetic data used in a tissue classification scheme. The invention improves the accuracy of tissue classification, in part, by properly identifying and accounting for image data from tissue regions that are affected by an obstruction and/or regions that lie outside a diagnostic zone of interest.

Abstract: The invention provides a system and methods for in-situ identification of one or more regions of tissue at which there is a likelihood of disease. The invention generally relates to methods and devices for acquiring, analyzing, processing, and displaying optical data and diagnostic results from a patient sample. For example, methods of the invention comprise obtaining spectral and visual data from a patient sample, calibrating the data, compensating for sample motion, arbitrating between redundant data sets, identifying potentially non-representative data, analyzing the data, and displaying the diagnostic results. The invention provides the option of real-time data processing and diagnosis.

Abstract: The invention provides methods for determining the probability that a given region of a tissue sample contains tissue of a given category, such as CIN 1 (cervical intraepithelial neoplasia, grade 1), CIN 2/3 (cervical intraepithelial neoplasia grades 2 and/or 3), normal squamous, normal columnar, and metaplasia, for example. The invention provides increased diagnostic accuracy by combining a plurality of statistical classification techniques. Furthermore, in one embodiment, the invention comprises combining one or more statistical techniques with one or more non-statistical classification techniques.