COMPUTER-ASSISTED INTERPRETATION OF FLOW-CYTOMETRY DATA IN HEMATOLOGY

A computer program has been developed for computer-assisted diagnosis (including subclassification) of flow cytometry data of acute leukaemi as and non-Hodgkin lymphomas by means of artificial intelligence. The knowledge base for the system has been formulated as semantic networks that describe physiological hematopoiesis as well as the pathological situation (e.g., aberrant antigen expression) of hematological disord ers. The semantic networks reflect the hierarchy of cells and their oc currence in diseases, the normal and pathological antigen expression p atterns of cells, cell maturation, and the frequency of cell populatio ns in normal blood and bone marrow. Using these semantic networks, the diagnosis algorithm compares the characteristic antigen expression pa ttern of a disease with the actual findings in the blood or bone marro w sample, The algorithm can separate mixed populations by taking doubl e staining findings into account. Finally, a diagnosis text is generat ed that describes all identified cell populations and the resulting di agnosis, The validation of the program showed a correct diagnosis (dis ease group and subclassification) in 97% of the cases (n = 633) with s light differences between the disease groups (e.g., B-NHL: 99%, B-cell ALL:84%). (C) 1996 Wiley-Liss, Inc.