| Title |
Application of artificial intelligence methods in flatfoot assessment |
| Authors |
Sinkutė, Birutė ; Šeštokė, Justina ; Butkevičiūtė, Eglė |
| DOI |
10.71467/ILK.2024.1.62 |
| ISBN |
9786099630250 |
| Full Text |
|
| Is Part of |
Conference Proceedings of the 1st International Conference on Regenerative Futures and Personalised Education REGENT 2025.. St. Ignatius Loyola College, 2025. p. 103-114.. ISBN 9786099630250 |
| Keywords [eng] |
orthopaedics ; flat feet ; artificial intelligence ; 2D images |
| Abstract [eng] |
This study examined the potential of artificial intelligence tools for detecting flatfoot pathology. We want to emphasise that there is very little research in this area and to point out that this is a relevant and very important topic in medicine. First, the base flow used a pretrained “backbone” on the ImageNet dataset. In this study, this term refers to the feature extraction part of a convolutional network. A standardised pre-processing with pruning and augmentation was performed, and a three-stage training schedule (stages 1, 2, and 3), average and maximum aggregation at the subject level, and the addition of light test time were proposed. Nine different model architectures were used. From stage 2 onwards, all models were trained on feet. Three-dimensional photographs with real flatfoot shapes, from flatfoot stages I to IV, were used. The most validated model was displayed in accurate AUROC plots, with estimated average and maximum aggregation values and their standard deviations. The research and calculations demonstrate the feasibility of applying artificial intelligence in orthopaedics. This work aimed to apply artificial intelligence methods and to detect flat feet. |
| Published |
St. Ignatius Loyola College, 2025 |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2025 |
