Papers for

orthopedic clinicians

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Transformer model predicts spine curve progression in adolescents

Predicting the Progression of Adolescent Idiopathic Scoliosis

Abstract: Adolescent Idiopathic Scoliosis is defined as a lateral curvature of the spine that develops during adolescence, without known cause. The condition can result in significant pain and disability, and often progresses rapidly during adolescence. The objective of this paper is to predict the progression of the condition in a temporal sequence from ages 9 to 24, as measured from a sequence of Dual X-ray Absorptiometry (DXA) scans. To this end, we train a transformer model that takes in the curve of the spine to predict curve progression. The model is trained using a large-scale synthetic dataset of spine curves and their time series, covering different curve types and different progression patterns. We show that the model is able to generalise from synthetic to real data by evaluating it on a dataset of real DXA scans covering multiple time points. We find that fine-tuning the model on real data gives a significant boost to performance. The model is able to accurately predict spine curve progression in both scoliosis and normal cases.

Wed 23 SeptComputer Vision and Pattern Recognition
The gist
Adolescent Idiopathic Scoliosis is a condition where the spine curves sideways during adolescence for unknown reasons. This can cause pain and worsen quickly as teens grow. The authors created a computer model that looks at spine shapes from special scans to predict how the curve will change from age 9 to 24. They trained the model on many made-up spine curves, then showed it works well on real scan data, especially after some extra training with that real data. This system can predict how scoliosis develops or if a spine remains normal over time.
Open → 2609.28434v1

Automatic continuous profiling improves knee joint shape assessment

SA-Profile: Automated Sulcus Angle Profiling from Super-Resolution MRI

Abstract: Trochlear dysplasia (TD) is an abnormality of the femoral trochlea associated with anterior knee pain and patellar instability. The sulcus angle (SA) is used to assess trochlear morphology, but it is typically measured on a single axial MR slice with no clear guidance on which to select, making it sensitive to slice selection and landmark placement. We propose an automatic framework for continuous SA profiling from super-resolved MR volumes. Clinically acquired axial, coronal, and sagittal MR scans are combined using implicit neural representations to reconstruct a high-resolution volume. SA measurements are computed across the trochlear region using two landmark detection U-Net models. The approach was evaluated on the public fastMRI dataset and a small in-house cohort of patients with TD. Compared with conventional manual single-slice SA measurements, the proposed automated method yielded a mean absolute error of 11.6$^\circ$ while providing continuous characterization of trochlear morphology. Population-level analysis demonstrated distinct mean SA profiles between the public cohort and the in-house TD cohort, highlighting the potential of profile-based assessment to characterize TD. By reducing reliance on a single manually selected axial slice, the proposed framework extends conventional SA assessment to a continuous profile-based description of trochlear morphology without additional imaging, while remaining conceptually linked to current clinical assessment. Further validation is required. The code is available: https://github.com/wehrlimi/SA_Profile.

Wed 9 SeptComputer Vision and Pattern RecognitionArtificial Intelligence
The gist
Problems with knee pain and instability are linked to a bony shape called the trochlea. Doctors measure an angle in images to see if the shape is abnormal, but this measurement depends a lot on which image slice they pick. The authors created a computer method that builds a sharper 3D image from several scans and then measures this angle all along the area, giving a fuller and more reliable picture. Their method matched well with manual checks and showed clear shape differences in patients with knee problems.
Open → 2609.10125v1