Abstract
Aims
The aims of this study were to develop an automatic system capable of calculating four radiological measurements used in the diagnosis and monitoring of cerebral palsy (CP)-related hip disease, and to demonstrate that these measurements are sufficiently accurate to be used in clinical practice.
Methods
We developed a machine-learning system to automatically measure Reimer’s migration percentage (RMP), acetabular index (ACI), head shaft angle (HSA), and neck shaft angle (NSA). The system automatically locates points around the femoral head and acetabulum on pelvic radiographs, and uses these to calculate measurements. The system was evaluated on 1,650 pelvic radiographs of children with CP (682 females and 968 males, mean age 8.3 years (SD 4.5)). Each radiograph was manually measured by five clinical experts. Agreement between the manual clinical measurements and the automatic system was assessed by mean absolute deviation (MAD) from the mean manual measurement, type 1 and type 2 intraclass correlation coefficients (ICCs), and a linear mixed-effects model (LMM) for assessing bias.
Results
The MAD scores were 5.7% (SD 8.5%) for RMP, 4.3° (SD 5.4°) for ACI, 5.0° (SD 5.2°) for NSA, and 5.7° (SD 6.1°) for HSA. Overall ICCs quantifying the agreement between the mean manual measurement and the automatic results were 0.91 for RMP, 0.66 for ACI, 0.85 for NSA, and 0.73 for HSA. The LMM showed no statistically significant bias.
Conclusion
The results showed excellent agreement between the manual and automatic measurements for RMP, good agreement for NSA, and moderate agreement for HSA and ACI. The performance of the system is sufficient for application in clinical practice to support the assessment of hip migration based on RMP. The system has the potential to save clinicians time and to improve patient care by enabling more comprehensive, consistent, and reliable monitoring of hip migration in children with CP.
The aims of this study were to develop an automatic system capable of calculating four radiological measurements used in the diagnosis and monitoring of cerebral palsy (CP)-related hip disease, and to demonstrate that these measurements are sufficiently accurate to be used in clinical practice.
Methods
We developed a machine-learning system to automatically measure Reimer’s migration percentage (RMP), acetabular index (ACI), head shaft angle (HSA), and neck shaft angle (NSA). The system automatically locates points around the femoral head and acetabulum on pelvic radiographs, and uses these to calculate measurements. The system was evaluated on 1,650 pelvic radiographs of children with CP (682 females and 968 males, mean age 8.3 years (SD 4.5)). Each radiograph was manually measured by five clinical experts. Agreement between the manual clinical measurements and the automatic system was assessed by mean absolute deviation (MAD) from the mean manual measurement, type 1 and type 2 intraclass correlation coefficients (ICCs), and a linear mixed-effects model (LMM) for assessing bias.
Results
The MAD scores were 5.7% (SD 8.5%) for RMP, 4.3° (SD 5.4°) for ACI, 5.0° (SD 5.2°) for NSA, and 5.7° (SD 6.1°) for HSA. Overall ICCs quantifying the agreement between the mean manual measurement and the automatic results were 0.91 for RMP, 0.66 for ACI, 0.85 for NSA, and 0.73 for HSA. The LMM showed no statistically significant bias.
Conclusion
The results showed excellent agreement between the manual and automatic measurements for RMP, good agreement for NSA, and moderate agreement for HSA and ACI. The performance of the system is sufficient for application in clinical practice to support the assessment of hip migration based on RMP. The system has the potential to save clinicians time and to improve patient care by enabling more comprehensive, consistent, and reliable monitoring of hip migration in children with CP.
| Original language | English |
|---|---|
| Article number | 124 - 132 |
| Journal | The Bone & Joint Journal |
| Volume | 107-B |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Jan 2025 |
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Lindner, C. (Corresponding participant), Cootes, T. (Participant) & Wallis, G. (Collaborator)
Impact: Health and wellbeing, Economic