Decision support tools for maxillofacial tumors diagnostics

The article presents originally developed intelligent decision support tools designed for precancerous lesions and tumors of the oral mucosa diagnostics.

Background. There is well recognized need for better primary health care in patients with suspected maxillofacial tumors.

Aim: develop intelligent decision support tools for tumors of the oral mucosa diagnostics.

Methods. Developed method was based on generalization of the practicing doctors experience. Diagnostic parameters of the disease were studied during the first stage of the development. All diagnostic parameters were divided into three large groups: patients complaints, patient’s work-up, risk factors and patients lifestyle. At the next stage of analysis each data group was presented as a parameter set. Each parameter set and each singular parameter were assigned a weight coefficient by expert evaluation method. The sum of the weighting coefficients for each group of parameters and for each singular parameter was equal to one for convenience. Use of these coefficients enabled transition made to indicators that allow assessing the expectation of confirmation of the expected prognosis.

Results. Using weight data we developed production knowledge models and implemented the fuzzy set technique, which resulted in models that allow assessing the degree of confidence in the diagnosis. This approach will ensure that doctors get informed decisions summarizing the collective knowledge of medical experts. Such intelligent solutions can only be considered as a kind of hint to a specialist, instead of the only, uncontested option. As the system functions, the models will be refined, which will increase the efficiency of the expert system.

Conclusions. The results of the study propose a new approach to classification, as well as highlight the structure of the parameters allowing suspicion of a malignancy in patients. We also developed new formal diagnostic method of maxillofacial cancer which suggest further patient management. Production knowledge base for automated diagnosis of the oral mucosa pathologies was created.

Practical application. This research results could be used as an additional tool that allows the doctor to verify diagnosis or treatment strategy of patients.

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