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Automatic Hardy and Clapham's classification of hallux sesamoid position on foot radiographs using deep neural
Ryutaro Takeda1, Akihiro Uchio1, Toshiko Iidaka2
1Department of Orthopaedic Surgery, Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Background:
There is currently no deep neural network (DNN) capable of automatically classifying tibial sesamoid position (TSP) on foot radiographs.
Methods:
A DNN was developed to predict TSP according to the Hardy and Clapham's classification. A total of 1554 foot radiographs were used for model development. The validation of the model was conducted using radiographs obtained from 113 consecutive first-visit patients of our foot and ankle clinic. On these 113 radiographs, TSP was independently classified by three foot and ankle surgeons and the DNN, and their results were compared. The weighted kappa value of TSP between the DNN prediction and the median of the three surgeons (KAI) was calculated.
Results:
The KAI was 0.95 (95 %CI: 0.85- 1.00), indicating sufficient consistency between the surgeons and the DNN.
Conclusions:
We have developed a DNN for automated TSP classification that demonstrates sufficient consistency with foot and ankle surgeons.
Levels Of Evidence:
Level 3 - Retrospective Cohort Study.
