Name
Abstract | ||
|---|---|---|
Automated amyloid-PET image classification can support clinical assessment and increase diagnostic confidence. Three automated approaches using global cut-points derived from Receiver Operating Characteristic (ROC) analysis, machine learning (ML) algorithms with regional SUVr values, and deep learning (DL) network with 3D image input were compared under various conditions: number of training data, radiotracers, and cohorts. 276 [C-11]PiB and 209 [F-18]AV45 PET images from ADNI database and our local cohort were used. Global mean and maximum SUVr cut-points were derived using ROC analysis. 68 ML models were built using regional SUVr values and one DL network was trained with classifications of two visual assessments - manufacturer's recommendations (gray-scale) and with visually guided reference region scaling (rainbow-scale). ML-based classification achieved similarly high accuracy as ROC classification, but had better convergence between training and unseen data, with a smaller number of training data. Naive Bayes performed the best overall among the 68 ML algorithms. Classification with maximum SUVr cut-points yielded higher accuracy than with mean SUVr cut-points, particularly for cohorts showing more focal uptake. DL networks can support the classification of definite cases accurately but performed poorly for equivocal cases. Rainbow-scale standardized image intensity scaling and improved inter-rater agreement. Gray-scale detects focal accumulation better, thus classifying more amyloid-positive scans. All three approaches generally achieved higher accuracy when trained with rainbow-scale classification. ML yielded similarly high accuracy as ROC, but with better convergence between training and unseen data, and further work may lead to even more accurate ML methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s12021-022-09587-2 | NEUROINFORMATICS |
Keywords | DocType | Volume |
Alzheimer's disease, Positron emission tomography (PET), Visual interpretation, Equivocal, Machine Learning, Deep Learning | Journal | 20 |
Issue | ISSN | Citations |
4 | 1539-2791 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying-Hwey Nai | 1 | 0 | 0.34 |
| Yee-Hsin Tay | 2 | 0 | 0.34 |
| Tomotaka Tanaka | 3 | 0 | 0.34 |
| Christopher P Chen | 4 | 0 | 0.34 |
| Edward G Robins | 5 | 0 | 0.34 |
| Anthonin Reilhac | 6 | 50 | 8.20 |