Artificial intelligence-based assistance in clinical 123I-FP-CIT SPECT scan classification

Research overview

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Abstract Purpose Dopamine transporter (DAT) imaging with 123I-FP-CIT SPECT is used to support the diagnosis of Parkinson’s disease (PD) in clinically uncertain cases. Previous studies showed that automatic classification of 123IFPCIT SPECT images (marketed as DaTSCAN) is feasible by using machine learning algorithms. However, these studies lacked sizable use of data from routine clinical practice. This study aims to contribute to the discussion whether artificial intelligence (AI) can be applied in clinical practice. Moreover, we investigated the need for hospital specific training data. Methods A convolutional neural network (CNN) named DaTNet-3 was designed and trained to classify DaTSCAN images as either normal or supportive of a dopaminergic deficit. Both a multi-site data set (n = 2412) from the Parkinson’s Progression Marker Initiative (PPMI) and an in-house data set containing clinical images (n = 932) obtained in routine practice at the St Antonius hospital (STA) were used for training and testing. STA images were labeled based on interpretation by nuclear medicine physicians. To investigate whether indeterminate scans effects classification accuracy, a threshold was applied on the output probability. Results DaTNet-3 trained with STA data reached an accuracy of 89.0% in correctly identifying images of the clinical STA test set as either normal or with decreased striatal DAT binding (98.5% on the PPMI test set). When thresholded, accuracy increased to 95.7%. This increase was not observed when trained with PPMI data, indicating the incorrect images were confidently classified as the incorrect class. Conclusion Based on results of DaTNet-3 we conclude that automatic interpretation of DaTSCAN images with AI is feasible and robust. Further, we conclude DaTNet-3 performs slightly better when it is trained with hospital specific data. This difference increased when output probability was thresholded. Therefore we conclude that the usability of a data set increases if it contains indeterminate images.

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