Heart failure & cardiomyopathies
Deep Learning on Bone Scintigraphy to Detect Abnormal Cardiac Uptake at Risk of Cardiac Amyloidosis
Authors' conclusion
The authors' detection model is effective at identifying patients with cardiac uptake Perugini grade ≥2 on WBS and may help in the diagnosis of patients with cardiac amyloidosis.
Study context
- Population
- Technetium-99m whole-body scintigraphies (whole-body scintigraphy (WBS)) from hospital databases; detection of significant cardiac uptake (Perugini grade ≥2) to retrieve patients at risk of cardiac amyloidosisimages
- Participants
- 3,048 vs 1,633
- Comparison
- 5-fold cross-validation vs External validation
- 5-fold cross-validationn = 3,048
- External validationn = 1,633
- Outcome
- Performance (C-statistics: sensitivity, specificity, AUC) of a deep learning model (convolutional neural network) detecting cardiac uptake Perugini grade ≥2 on WBS
Methodological details5-fold cross-validation, external validation
- 5-fold cross-validation
- External validation
Performance
As reported in the abstract
Detection of cardiac uptake Perugini grade ≥2
| Test or model | AUC | Sensitivity | Specificity |
|---|---|---|---|
| 5-fold cross-validation | 0.999 (SD = 0.000) | 98.9% (± 1.0) | 99.5% (± 0.4) |
| External validation | 0.999 | 96.1% | 99.5% |
AUC
5-fold cross-validation0.999 (SD = 0.000)
External validation0.999
Sensitivity
5-fold cross-validation98.9% (± 1.0)
External validation96.1%
Specificity
5-fold cross-validation99.5% (± 0.4)
External validation99.5%
Further findings
Performance by sex, age <90 years, BMI, injection-acquisition delay, radionuclides and WBS indication
- Only slightly affected performances
Original publication
Deep Learning on Bone Scintigraphy to Detect Abnormal Cardiac Uptake at Risk of Cardiac Amyloidosis. JACC Cardiovasc Imaging. Published 12 April 2023. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →