Back

Ultrasound-led stratification of carpal tunnel syndrome reveals structure-function mismatch

Chen, J.; Shi, D.; Su, J.; Huang, X.; Qian, Y.

2026-05-13 bioengineering
10.64898/2026.05.09.723950 bioRxiv
Show abstract

The severity stratification of carpal tunnel syndrome (CTS) relies on ultrasound morphological markers and electromyography. However, it remains unclear how structural imaging can reliably infer functional impairment. Clarifying the structure-function relationship is critical for efficient diagnostic pathways. A retrospective cohort of 55 patients with symptoms related to CTS was analyzed at the Shanghai Sixth Peoples Hospital. All patients were subjected to ultrasound and EMG. 72.7% cases were diagnosed with CTS with a female predominance and equal left-right involvement. Random-forest classifiers were trained using surrogate splits, and performance was evaluated using predictions outside the bag. A full-feature model (34 candidate variables) was compared against a simplified model (8 core variables) capturing the core morphological and electrophysiological features. A residual-based framework was then used to characterize the structure-function mismatch within severity grades (1a-3c). The simplified model improved discriminative performance compared to the full-feature model (AUC 0.789 to 0.824). The simplified model achieved an overall accuracy of 77.3%. Analysis of predicted probability distributions and 10-bin calibration curves indicated stable and clinically interpretable risk estimation in most probability ranges. Permutation-based importance analysis confirmed that both ultrasound and electrophysiological features contributed substantively to prediction. Residual-based grading further revealed structure- function heterogeneity within each main severity grade. CTS severity can be stratified using a limited set of complementary morphological and electrophysiological features. Structure-function mismatch supports an imaging-led initial screening, with electrophysiology reserved for selected patients.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Annals of Clinical and Translational Neurology
29 papers in training set
Top 0.1%
14.8%
2
Scientific Reports
3102 papers in training set
Top 2%
14.8%
3
European Radiology
14 papers in training set
Top 0.1%
12.4%
4
Frontiers in Neurology
91 papers in training set
Top 0.8%
6.8%
5
Brain
154 papers in training set
Top 0.9%
6.4%
50% of probability mass above
6
PLOS ONE
4510 papers in training set
Top 27%
6.3%
7
npj Digital Medicine
97 papers in training set
Top 0.9%
4.9%
8
eLife
5422 papers in training set
Top 29%
3.1%
9
Journal of Neurotrauma
27 papers in training set
Top 0.2%
2.1%
10
Science Advances
1098 papers in training set
Top 17%
1.7%
11
Science Translational Medicine
111 papers in training set
Top 3%
1.7%
12
Advanced Science
249 papers in training set
Top 13%
1.3%
13
Nature Communications
4913 papers in training set
Top 57%
1.2%
14
Muscle & Nerve
10 papers in training set
Top 0.4%
0.7%
15
PLOS Computational Biology
1633 papers in training set
Top 25%
0.7%
16
Journal of Clinical Investigation
164 papers in training set
Top 7%
0.7%
17
Journal of Neural Engineering
197 papers in training set
Top 2%
0.7%
18
Annals of Biomedical Engineering
34 papers in training set
Top 1%
0.7%
19
Communications Biology
886 papers in training set
Top 29%
0.6%
20
Computers in Biology and Medicine
120 papers in training set
Top 5%
0.6%
21
Frontiers in Neuroscience
223 papers in training set
Top 8%
0.6%
22
The Journal of Headache and Pain
10 papers in training set
Top 0.2%
0.5%
23
Sensors
39 papers in training set
Top 2%
0.5%
24
Human Brain Mapping
295 papers in training set
Top 5%
0.5%