Application of Recurrence Quantification Analysis in the detection of osteoarthritis of the knee with the use of vibroarthrography
Artykuł w czasopiśmie
MNiSW
0
brak dyscyplin
| Status: | |
| Autorzy: | Karpiński Robert, Machrowska Anna, Maciejewski Marcin, Jonak Józef, Krakowski Przemysław, Syta Arkadiusz |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2024 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 5 |
| Wolumen/Tom: | 18 |
| Strony: | 19 - 31 |
| Impact Factor: | 1,0 |
| Web of Science® Times Cited: | 1 |
| Scopus® Cytowania: | 1 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 1 sierpnia 2024 |
| Abstrakty: | angielski |
| Nowadays, the world is struggling with the problems of an aging society. With the increasing share of older people in the population, degenerative joint diseases are a growing problem. The result of progressive degenerative changes in joints is primarily the deterioration of the patients' quality of life and their gradual exclusion from activity and social life. The ability to effectively, non-invasively and quickly detect cases of chondromalacia of the knee joints is a challenge for modern medicine. The possibility of early detection of progressive degenerative changes allows for the appropriate selection of treatment protocols and significantly increases the chances of inhibiting the development of degenerative diseases of the musculoskeletal system. The article presents a non-invasive method for detecting degenerative changes in the knee joints based on recurrence analysis and classification using neural networks. The result of the analyzes was a classification accuracy of 91.07% in the case of MLP neural networks and 80.36% for RBF networks. |
