Expert System Design in Hematology Diagnosis

A relational database for diagnosis of hematopoietic neoplasms using immunophenotyping by flow cytometry

A relational database was developed to facilitate the diagnosis of hematopoietic neoplasms using results of immunophenotyping by flow cytometry. This database runs on personal computers and uses backward-chaining search to arrive at conclusions. Results of immunologic marker studies are processed by the database to obtain a set of differential diagnoses. The current version of this database includes diagnostic immunophenotyping pattern for 33 hematopoietic neoplasms. We tested this database using 92 clinical cases from 2 tertiary care medical centers. The database ranked the actual diagnosis as 1 of the top 5 differential diagnoses in 93% of the cases tested. The user can modify the database contents to suit individual needs. This database has been posted on the World Wide Web for direct access. We propose that this user-friendly database is a potential tool for computer-assisted diagnosis of hematopoietic neoplasms.

Haematological validation of a computer-based bone marrow reporting system

AIMS

To prove the safety and effectiveness of "Professor Belmonte", a knowledge-based system for bone marrow reporting, a formal evaluation of the reports generated by the system was performed.

METHODS

Three haematologists (a consultant, a senior registrar, and a junior registrar), none of whom were involved in the development of the software, compared the unedited reports generated by Professor Belmonte with the original bone marrow reports in 785 unselected cases. Each haematologist independently graded the quality of Belmonte's reports using one of four categories: (a) better than the original report (more informative, containing useful information missing in the original report); (b) equivalent to the original report; (c) satisfactory, but missing information that should have been included; and (d) unsatisfactory.

RESULTS

The consultant graded 64 reports as more informative than the original, 687 as equivalent to the original, 32 as satisfactory, and two as unsatisfactory. The senior registrar considered 29 reports to be better than the original, 739 to be equivalent to the original, 15 to be satisfactory, and two to be unsatisfactory. The junior registrar found that 88 reports were better than the original, 681 were equivalent to the original, 14 were satisfactory, and two were unsatisfactory. Each judge found two different reports to be unsatisfactory according to their criteria. All 785 reports generated by the computer system received at least two scores of satisfactory or better.

CONCLUSIONS

In this representative study, Professor Belmonte generated bone marrow reports that proved to be as accurate as the original reports in a large university hospital. The haematology knowledge contained within the system, the reasoning process, and the function of the software are safe and effective for assisting haematologists in generating high quality bone marrow reports.

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 leukaemias 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 disorders. The semantic networks reflect the hierarchy of cells and their occurrence in diseases, the normal and pathological antigen expression patterns of cells, cell maturation, and the frequency of cell populations in normal blood and bone marrow. Using these semantic networks, the diagnosis algorithm compares the characteristic antigen expression pattern of a disease with the actual findings in the blood or bone marrow sample. The algorithm can separate mixed populations by taking double staining findings into account. Finally, a diagnosis text is generated that describes all identified cell populations and the resulting diagnosis. The validation of the program showed a correct diagnosis (disease group and subclassification) in 97% of the cases (n = 633) with slight differences between the disease groups (e.g., B-NHL: 99%, B-cell ALL: 84%).

Multiparameter case studies using knowledge-based systems in hematology

Knowledge-based systems in diagnostic medicine are often used for teaching medical decision making. We have extended the educational value of our suite of hematology decision support systems (peripheral blood analysis, flow cytometry immunophenotyping and DNA analysis, and bone marrow morphology) by creating case studies in a hypertext format. The case studies use the knowledge-based system screens as a background. Digitized images of blood and bone marrow smears from the teaching cases can be accessed on-line. The case studies, which are designed primarily for technologists and physicians, emphasize the proper multiparameter approach to hematopathology diagnosis.

Multiparameter interpretative reporting in diagnostic laboratory hematology

Accurate diagnosis and classification of hematologic malignancies (acute leukemias and chronic lymphoproliferative disorders) requires a multiparameter approach including peripheral blood analysis, bone marrow examination, and immunophenotyping. We have designed knowledge-based computer systems for interpretation of the hemogram and peripheral blood smears, analysis of flow cytometric immunophenotyping panels, and morphologic assessment of bone marrow specimens. The 3 modules share a relational database which includes pertinent clinical history in addition to the laboratory results. The bone marrow module automatically writes a complete interpretative report with a final diagnosis by searching all of the databases for appropriate clinical, peripheral blood, and immunophenotyping information. The ability of the 3 modules to interact, and the quality of the interpretative reports were tested on 100 consecutive patients with leukemia. The final diagnosis made by the bone marrow system agreed with the hospital diagnosis in 94 cases and the authors' interpretation in 99 of 100 cases.