Artificial intelligence in the diagnosis of Parkinson's disease from ioflupane-123 single-photon emission computed tomography dopamine transporter scans using transfer learning.

OBJECTIVE: The objective of this study was to identify the extent to which artificial intelligence could be used in the diagnosis of Parkinson's disease from ioflupane-123 (¹²³I) single-photon emission computed tomography (SPECT) dopamine transporter scans using transfer learning.

MATERIALS AND METHODS: A data set of 54 normal and 54 abnormal ¹²³I SPECT scans was amplified 44-fold using a process of image augmentation. This resulted in a training set of 2376 normal and 2376 abnormal images. This was used to retrain the top layer of the Inception v3 network. The resulting neural network functioned as a classifier for new ¹²³I SPECT scans as either normal or abnormal. A completely separate set of 45 ¹²³I SPECT scans were used for final testing of the network.

RESULTS: The area under the receiver-operator curve in final testing was 0.87. This corresponded to a test sensitivity of 96.3%, a specificity of 66.7%, a positive predictive value of 81.3% and a negative predictive value of 92.3%, using an optimum diagnostic threshold.

CONCLUSION: This study has provided proof of concept for the use of transfer learning, from convolutional neural networks pretrained on nonmedical images, for the interpretation of ¹²³I SPECT scans. This has been shown to be possible in this study even with a very small sample size. This technique is likely to be applicable to many areas of diagnostic imaging.