Objective <p>To provide fully automatic scanner-independent 5-level categorization of the [¹²³I]FP-CIT uptake in striatal subregions in dopamine transporter SPECT.</p> Methods <p>A total of 3500 [¹²³I]FP-CIT SPECT scans from two in house (<i>n</i> = 1740, <i>n</i> = 640) and two external (<i>n</i> = 645, <i>n</i> = 475) datasets were used for this study. A convolutional neural network (CNN) was trained for the categorization of the [¹²³I]FP-CIT uptake in unilateral caudate and putamen in both hemispheres according to 5 levels: normal, borderline, moderate reduction, strong reduction, almost missing. Reference standard labels for the network training were created automatically by fitting a Gaussian mixture model to histograms of the specific [¹²³I]FP-CIT binding ratio, separately for caudate and putamen and separately for each dataset. The CNN was trained on a mixed-scanner subsample (<i>n</i> = 1957) and tested on one independent identically distributed (IID, <i>n</i> = 1068) and one out-of-distribution (OOD, <i>n</i> = 475) test dataset.</p> Results <p>The accuracy of the CNN for the 5-level prediction of the [¹²³I]FP-CIT uptake in caudate/putamen was 80.1/78.0% in the IID test dataset and 78.1/76.5% in the OOD test dataset. All 4 regional 5-level predictions were correct in 54.3/52.6% of the cases in the IID/OOD test dataset. A global binary score automatically derived from the regional 5-scores achieved 97.4/96.2% accuracy for automatic classification of the scans as normal or reduced relative to visual expert read as reference standard.</p> Conclusions <p>Automatic scanner-independent 5-level categorization of the [¹²³I]FP-CIT uptake in striatal subregions by a CNN model is feasible with clinically useful accuracy.</p>