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AIMS: BoneFinder is a machine-learning tool that can automatically calculate Reimers migration percentage (RMP) and head-shaft angle (HSA) from paediatric cerebral palsy (CP) pelvic radiographs. This study's primary aim was to compare BoneFinder's fully automated measurements to manual measurements made by clinicians and HipScreen-assisted measurements made by clinicians. METHODS: Using the radiological database within Cerebral Palsy Integrated Care Pathway Scotland (CPIPS), BoneFinder's automatic RMP and HSA measurements were compared across the same set of radiographs to: routine manual measurements performed by clinical experts from the CPIPS database; additional manual measurements performed by two clinicians; and measurements performed by the same two clinicians using the smartphone application HipScreen. RESULTS: A total of 509 anteroposterior pelvic radiographs (1,018 hips; mean age 7.4 years (1 to 17)) were selected at random from the CPIPS database. Gross Motor Function Classification System levels were I (n = 69), II (n = 37), III (n = 97), IV (n = 120), and V (n = 186). The mean absolute difference (MAD) in RMP between BoneFinder and CPIPS measurements, manual measurements, and HipScreen was 7.6% (SD 10.0%), 5.5% (SD 9.1%), and 5.8% (SD 9.2%), respectively. Interobserver reliability of RMP measurement across all methods was excellent (intraclass correlation coefficient (ICC) 0.89 (95% CI 0.87 to 0.91); p < 0.001). Good ICC was found between BoneFinder and CPIPS measurements (ICC 0.80 (95% CI 0.65 to 0.87); p < 0.001). The area under the receiver operating characteristic curve for BoneFinder's ability to detect a hip with a RMP ≥ 30%/40%/50% was 0.95/0.97/0.98, respectively. ICC of HSA measurement across all raters was moderate (ICC 0.72 (95% CI 0.67 to 0.76); p < 0.001). Image artefact was present in 138 of 1,018 hips (14%). In these images, MAD increased and ICC decreased for both RMP and HSA measurement between BoneFinder and CPIPS, indicating a decline in agreement. CONCLUSION: Fully automated RMP and HSA measurements using BoneFinder were highly reliable with clinically acceptable measurement error. Further refinement of BoneFinder is required for analysis of radiographs with artefact.

Original publication

DOI

10.1302/0301-620X.107B7.BJJ-2024-1575.R1

Type

Journal article

Journal

Bone joint j

Publication Date

01/07/2025

Volume

107-B

Pages

752 - 760

Keywords

Humans, Cerebral Palsy, Child, Adolescent, Child, Preschool, Male, Infant, Female, Databases, Factual, Pelvic Bones, Radiography, Machine Learning, Reproducibility of Results