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A multi-task learning model for clinically interpretable sesamoiditis grading.

Li Guo¹, Anas M Tahir¹, Michael Hore²

¹Department of Electrical and Computer Engineering, University of British Columbia, Canada.

Computers in Biology and Medicine

|September 26, 2024

Summary

This summary is machine-generated.

This study introduces a new interpretable AI model for grading equine sesamoiditis. The model enhances diagnostic accuracy and transparency by simultaneously grading severity and segmenting vascular channels.

Keywords:

Cross-attention Interpretability Radiograph Sesamoiditis Visual transformer

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Area of Science:

Veterinary Medicine
Artificial Intelligence
Medical Imaging

Background:

Equine sesamoiditis is a prevalent condition impacting horse performance and increasing injury risk.
Accurate grading of sesamoiditis is essential for effective treatment strategies.
Current deep learning models for sesamoiditis lack clinical interpretability.

Purpose of the Study:

To develop a novel, clinically interpretable multi-task learning model for equine sesamoiditis grading.
To integrate clinical knowledge with machine learning for improved diagnostic accuracy.
To enhance the transparency of AI-driven diagnostic decisions in veterinary medicine.

Main Methods:

A dual-branch decoder architecture was employed for simultaneous sesamoiditis grading and vascular channel segmentation.
Feature fusion was utilized to facilitate knowledge transfer between the two tasks.
A diagnostic report and vascular channel mask were generated to explain grading decisions.

Main Results:

The proposed model demonstrated superior performance compared to state-of-the-art methods on two independent datasets.
The model achieved high accuracy in both sesamoiditis grading and vascular channel segmentation.
The generated diagnostic reports provided clear explanations for the model's grading outcomes.

Conclusions:

The developed model offers a clinically interpretable and accurate approach for grading equine sesamoiditis.
This framework provides a foundation for interpretable AI in diagnosing similar veterinary diseases.
The integration of feature fusion and multi-task learning enhances diagnostic capabilities and transparency.