Anninou AP, Poulios P, Groumpos PP and Gliatis I
Objective: The main objective of this study is the construction of a tool to diagnose a specific type of knee injuries, the meniscus injury, without the aid of imaging modalities. This tool exploits physician’s knowledge and experience and extracts all information needed for a diagnostic procedure.
Methods: A specific type of Fuzzy Cognitive Maps, which is named Dynamic Fuzzy Cognitive Networks, is used for the implementation of diagnostic process. This method simulates the physician’s way of thinking and making decisions in order to conclude to a final diagnosis and will constitute the background of a user interface platform, named “KneeD,” designed especially as a supportive tool for the physician. It exploits information about patient history and clinical examination and the arisen symptoms and risk factors constitute the main attributes that contribute to injury identification.
Results: A pilot sample of 17 patients with knee injuries who arrived at the University Hospital of Patras, in Greece, was examined by the orthopedic clinician and the diagnoses confirmed with MRI were compared to our platform’s outcomes. Results concerned a) the initial distinction between “meniscus injury” and “other disease” and b) discrimination between acute and degenerative injury for patients positive to meniscal tear from the first level. Both levels provided us with very satisfactory results in total agreement with MRI outcomes.
Conclusion: The outcome of a close collaboration between engineers and medical doctors was a platform for real time diagnosing of knee injuries, which is simple, user friendly, real-time, easily accessible, fast, reliable, and low-cost. Furthermore, it could also be used as a patient storage database. The obtained results have been evaluated by orthopedic surgeons who found them very satisfactory.
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