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Enhancing wrist arthroscopy: artificial intelligence applications for bone structure recognition using machine

Antoni Orgiu¹, Bihes Karkazan¹, Stuart Cannell¹

¹Orthopaedic, Traumatological Surgery Department, Avicenne University Hospital, Paris, France.

Hand Surgery & Rehabilitation

|May 26, 2024

Summary

This summary is machine-generated.

Artificial intelligence can now accurately identify carpal bones during wrist arthroscopy. This AI algorithm shows promise for improving surgical training and performance in this complex procedure.

Keywords:

Artificial intelligence Bone structure Machine learning Wrist arthroscopy

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Area of Science:

Orthopedic Surgery
Medical Imaging
Artificial Intelligence

Background:

Wrist arthroscopy is a complex surgical procedure with a steep learning curve.
Accurate identification of anatomical structures is crucial but challenging during arthroscopy.
Artificial intelligence (AI) offers untapped potential for enhancing surgical training.

Purpose of the Study:

To develop and evaluate an AI algorithm for recognizing carpal bone anatomy during wrist arthroscopy.
To create a robust dataset of labeled wrist arthroscopy images for algorithm training and validation.

Main Methods:

A Deeplabv3+ classification algorithm with a U-Net architecture was developed.
A database of 511 labeled wrist arthroscopy images (4,088 augmented) was created from 20 procedures (10 patient, 10 cadaver).
Algorithm performance was assessed using a Dice loss score, aiming for >80% accuracy.

Main Results:

The AI algorithm achieved an average Dice loss score of 89% for carpal bone recognition.
The system demonstrated reliable detection of various carpal bones.
Performance varied slightly between different carpal bones.

Conclusions:

AI can reliably detect carpal bones in wrist arthroscopy, aiding surgical visualization.
Further algorithm refinement could improve accuracy for all carpal bones.
Real-world application may enhance arthroscopic wrist surgery training and outcomes.