Fully Automated Measurement of the Insall-Salvati Ratio with Artificial Intelligence.
AI bias
Artificial intelligence
Data science
Insall-Salvati, Patella, Patellar height
Journal
Journal of imaging informatics in medicine
ISSN: 2948-2933
Titre abrégé: J Imaging Inform Med
Pays: Switzerland
ID NLM: 9918663679206676
Informations de publication
Date de publication:
10 Jan 2024
10 Jan 2024
Historique:
received:
23
08
2023
accepted:
19
09
2023
revised:
17
09
2023
medline:
12
2
2024
pubmed:
12
2
2024
entrez:
12
2
2024
Statut:
aheadofprint
Résumé
Patella alta (PA) and patella baja (PB) affect 1-2% of the world population, but are often underreported, leading to potential complications like osteoarthritis. The Insall-Salvati ratio (ISR) is commonly used to diagnose patellar height abnormalities. Artificial intelligence (AI) keypoint models show promising accuracy in measuring and detecting these abnormalities.An AI keypoint model is developed and validated to study the Insall-Salvati ratio on a random population sample of lateral knee radiographs. A keypoint model was trained and internally validated with 689 lateral knee radiographs from five sites in a multi-hospital urban healthcare system after IRB approval. A total of 116 lateral knee radiographs from a sixth site were used for external validation. Distance error (mm), Pearson correlation, and Bland-Altman plots were used to evaluate model performance. On a random sample of 2647 different lateral knee radiographs, mean and standard deviation were used to calculate the normal distribution of ISR. A keypoint detection model had mean distance error of 2.57 ± 2.44 mm on internal validation data and 2.73 ± 2.86 mm on external validation data. Pearson correlation between labeled and predicted Insall-Salvati ratios was 0.82 [95% CI 0.76-0.86] on internal validation and 0.75 [0.66-0.82] on external validation. For the population sample of 2647 patients, there was mean ISR of 1.11 ± 0.21. Patellar height abnormalities were underreported in radiology reports from the population sample. AI keypoint models consistently measure ISR on knee radiographs. Future models can enable radiologists to study musculoskeletal measurements on larger population samples and enhance our understanding of normal and abnormal ranges.
Identifiants
pubmed: 38343226
doi: 10.1007/s10278-023-00955-1
pii: 10.1007/s10278-023-00955-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.
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