The role of the deep convolutional neural network as an aid to interpreting brain [18F]DOPA PET/CT in the diagnosis of Parkinson's disease

Objectives To test the performance of a 3D convolutional neural network (CNN) in analysing brain [F-18]DOPA PET/CT in order to identify patients with nigro-striatal neurodegeneration. We evaluated the robustness of the 3D CNN by testing it against a manual regional analysis of the striata by using a striatal-to-occipital ratio (SOR). Methods We analyzed patients who had undergone [F-18]DOPA PET/CT from 2016 to 2018. Two examiners interpreted PET/CT images as positive or negative. Only patients with at least 2 years of follow-up and an ascertained neurological diagnosis were included. A 3D CNN was developed to evaluate [F-18]DOPA PET/CT and refine the diagnosis of movement disorder. This system required training and testing, which were carried out on 2/3 and 1/3 of patients, respectively. A regional analysis was also conducted by drawing region of interest on T1-weighted 3D MRI scans, on which the [F-18]DOPA PET images were first co-registered. Results Ninety-eight patients were enrolled: 43 presented nigro-striatal degeneration and 55 negative cases used as controls. After training on 69 patients, the diagnostic performance of the 3D CNN was then calculated in 29 patients. Sensitivity, specificity, negative predictive value, positive predictive value and accuracy were 100%, 89%, 100%, 85% and 93%, respectively. When we compared the 3D CNN results with the SOR analysis, we found that the two patients falsely classified as positive by the 3D CNN procedure showed SOR values <= 5(th) percentile of the negative cases' distribution. Conclusions 3D CNNs are able to interpret [F-18]DOPA PET/CT properly, revealing patients affected by Parkinson's disease.

Automated summary

The study evaluated the performance of a 3D convolutional neural network in analyzing [F-18]DOPA PET/CT to identify patients with nigro-striatal neurodegeneration. After training and testing on a group of patients, the 3D CNN showed high sensitivity and accuracy in diagnosing movement disorders.