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Deep-learning-based cardiac amyloidosis classification from early acquired pet images.

Maria Filomena Santarelli¹, Dario Genovesi², Vincenzo Positano²

¹CNR Institute of Clinical Physiology, CNR Research Area-Via Moruzzi, 1, 56124, Pisa, Italy. mariafilomena.santarelli@cnr.it.

The International Journal of Cardiovascular Imaging

|February 16, 2021

Summary

Deep learning accurately identifies cardiac amyloidosis using early PET scans. The CAclassNet model shows high sensitivity and specificity for diagnosing AL-CA and ATTR-CA subtypes.

Keywords:

Amyloid light chain (AL)Amyloid transthyretin (ATTR)Cardiac amyloidosis Convolutional neural network Deep learning [18F]-florbetaben

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Area of Science:

Nuclear Medicine
Artificial Intelligence
Cardiology

Background:

Cardiac amyloidosis (CA) diagnosis can be challenging.
Early detection is crucial for effective management.
Novel imaging analysis techniques are needed.

Purpose of the Study:

To evaluate deep learning for characterizing cardiac amyloidosis.
To assess the potential of early [18F]-Florbetaben PET images.
To develop a deep convolutional neural network (CAclassNet) for CA diagnosis.

Main Methods:

Utilized early (15 min post-injection) [18F]-Florbetaben PET/CT images from 47 subjects (13 ATTR-CA, 15 AL-CA, 19 controls).
Developed CAclassNet, a deep convolutional neural network, to classify images into AL-CA, ATTR-CA, and control groups.
Trained, validated, and tested the network on 1107 2D images.

Main Results:

CAclassNet achieved high cross-validation accuracy (95.49%) and good test set performance.
Sensitivity, specificity, and accuracy for AL-CA were 1, 0.912, and 0.936, respectively.
Sensitivity, specificity, and accuracy for ATTR-CA were 0.935, 0.897, and 0.972, respectively.

Conclusions:

Deep learning models like CAclassNet show significant promise for diagnosing cardiac amyloidosis.
Early [18F]-Florbetaben PET imaging can be effectively analyzed by AI for CA detection.
CAclassNet can serve as a valuable clinical aid for CA diagnosis.