AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.

Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To develop a deep learning model to classify minimally processed brain PET scans as amyloid positive or negative, evaluate its performance on independent data sets and different tracers, and compare it with human visual reads. Materials and Methods This retrospective study used 8476 PET scans (6722 patients) obtained from late 2004 to early 2023 that were analyzed across five different data sets. A deep learning model, AmyloidPETNet, was trained on 1538 scans from 766 patients, validated on 205 scans from 95 patients, and internally tested on 184 scans from 95 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) fluorine 18 (18F) florbetapir (FBP) data set. It was tested on ADNI scans using different tracers and scans from independent data sets. Scan amyloid positivity was based on mean cortical standardized uptake value ratio cutoffs. To compare with model performance, each scan from both the Centiloid Project and a subset of the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study were visually interpreted with a confidence level (low, intermediate, high) of amyloid positivity/negativity. The area under the receiver operating characteristic curve (AUC) and other performance metrics were calculated, and Cohen κ was used to measure physician-model agreement. Results The model achieved an AUC of 0.97 (95% CI: 0.95, 0.99) on test ADNI 18F-FBP scans, which generalized well to 18F-FBP scans from the Open Access Series of Imaging Studies (AUC, 0.95; 95% CI: 0.93, 0.97) and the A4 study (AUC, 0.98; 95% CI: 0.98, 0.98). Model performance was high when applied to data sets with different tracers (AUC ≥ 0.97). Other performance metrics provided converging evidence. Physician-model agreement ranged from fair (Cohen κ = 0.39; 95% CI: 0.16, 0.60) on a sample of mostly equivocal cases from the A4 study to almost perfect (Cohen κ = 0.93; 95% CI: 0.86, 1.0) on the Centiloid Project. Conclusion The developed model was capable of automatically and accurately classifying brain PET scans as amyloid positive or negative without relying on experienced readers or requiring structural MRI. Clinical trial registration no. NCT00106899 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Bryan and Forghani in this issue.

Potential environmental benefits from increased use of bioenergy in China.

Because of its large population and rapidly growing economy, China is confronting a serious energy shortage and daunting environmental problems. An increased use of fuels derived from biomass could relieve some demand for nonrenewable sources of energy while providing environmental benefits in terms of cleaner air and reduced emissions of greenhouse gases. In 2003, China generated about 25.9 x 10(8) metric tons of industrial waste (liquid + solid), 14.7 x 10(8) metric tons/year (t/y) of manure (livestock + human), 7.1 x 10(8) t/y of crop residues and food-processing byproducts, 2 x 10(8) t/y of fuelwood and wood manufacturing residues, and 1.5 x 10(8) t/y of municipal waste. Biofuels derived from these materials could potentially displace the use of about 4.12 x 10(8) t/y of coal and 3.75 x 10(6) t/y of petroleum. An increased bioenergy use of this magnitude would help to reduce the emissions of key air pollutants: SO(2 )by 11.6 x 10(6) t/y, NO(X) by 1.48 x 10(6) t/y, CO2 by 1.07 x 10(9) t/y, and CH4 by 50 x 10(6) t/y. The reduced SO(2) emissions would be equivalent to 54% of the national emissions in 2003, whereas those for CO2 are 30%. It is important to recognize, however, that large increases in the use of biomass fuels also could result in socioeconomic and environmental problems such as less production of food and damage caused to natural habitats.