An automated explainable decision-support system for multi-criteria assessment of functionalized implant coatings
Artykuł w czasopiśmie
MNiSW
20
Lista 2024
| Status: | |
| Autorzy: | Alibekkyzy Karlygash, Batalova Madina, Komada Paweł, Belginova Saule, Bazarova Madina, Keribayeva Talshyn, Karymsakova Indira, Zheksenov Yergazy |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 8 |
| Strony: | 1 - 25 |
| Impact Factor: | 3,4 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus | DOAJ | EBSCO | COPE | CLOCKSS | OpenAIRE | Inspec | ProQuest |
| Efekt badań statutowych | NIE |
| Finansowanie: | The author(s) declared that financial support was not received for this work and/or its publication. |
| 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: | 13 sierpnia 2026 |
| Abstrakty: | angielski |
| Introduction: The selection of functionalized implant coatings is a complex multi-criteria decision-making problem because biological, engineering, technological, and implementation-readiness indicators are heterogeneous and are frequently evaluated manually. This study proposes an automated and explainable computational decision-support framework for coating assessment and ranking. Methods: The framework integrates data preprocessing, indicator normalization, weighted scoring, implementation-readiness assessment, sensitivity analysis, explainable classification, and visualization. Biological effectiveness, engineering and operational reliability, and manufacturability and scalability were used as the three evaluation domains. Published experimental data from 2015 to 2025 on titanium oxide nanostructures, hydrogel coatings, hybrid nanocomposites, and bioactive surface modifications were used for validation. Results: The framework generated reproducible domain scores, integral performance scores, implementation classes, and explainable rankings. TiO₂-Sr, Grad-Ti, and TiO₂-NT achieved the highest scores because of their balanced biological performance, engineering reliability, sterilization compatibility, and technological scalability. The ranking remained stable under variations in weighting factors. Discussion: The proposed framework transforms heterogeneous biomedical data into a transparent and reproducible decision-support workflow. It can serve as the computational core of future biomedical material-selection platforms and support preliminary coating screening, technology-readiness evaluation, and data-driven engineering decisions. |
