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Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

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Optimizing Wrist Splint Fitting Parameters Through Artificial Intelligence Analysis.

Ashkan Sedigh¹, Meysam Fathi², Peter K Beredjiklian²

¹Orthopedics, Department of Orthopedic Surgery, Division of Hand Surgery, Rothman Orthopedic Institute, Jefferson Medical College, Philadelphia, USA.

|October 18, 2024

Summary

This summary is machine-generated.

Wrist width, measured using 3D scanning and artificial intelligence, accurately predicts wrist splint size. This AI-driven approach offers a more precise fit than traditional circumference measurements for prefabricated splints.

Keywords:

3d printing 3d scanning artificial intelligence brace machine learning wrist

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Area of Science:

Orthopedics
Biomedical Engineering
Artificial Intelligence

Background:

Current wrist splint fitting relies on wrist circumference, often leading to suboptimal fit.
3D scanning and AI offer potential for improved accuracy in assessing hand anatomy.

Purpose of the Study:

To evaluate the efficacy of 3D scanned hand features and AI in predicting optimal wrist splint size.
To compare the predictive accuracy of wrist width and other hand measurements against wrist circumference.

Main Methods:

Collected 3D scan data from 54 healthy volunteers using an infrared-based scanner.
Analyzed 14 distinct hand features and developed a classification algorithm using machine learning models (XGBoost, RandomForest, SVC).

Main Results:

Wrist width achieved the highest classification accuracy (91%) with XGBoost and RandomForest classifiers.
Combined measurements including wrist width, mid-forearm width, and hand crease line width showed 90% accuracy with XGBoost.
Support Vector Classifier demonstrated consistent performance with up to 81% accuracy.

Conclusions:

Wrist width is the most significant predictor of wrist splint size.
AI and 3D scanning enable accurate, contactless, and personalized splint fitting, potentially improving patient outcomes.