Classification of patients with parkinsonian syndromes using medical imaging and artificial intelligence algorithms

The distinction of Parkinsonian Syndromes (PS) is challenging due to similarities of symptoms and signs at early stages of disease. Thus, the need of accurate methods for differential diagnosis at those early stages has emerged. To improve the evaluation of medical images, artificial intelligence turns out to be a useful tool. Parkinson’s Disease, the commonest PS, is characterized by the degeneration of dopamine neurons in the substantia nigra which is detected by the dopamine transporter scan (DaTscanTM), a single photon-emission tomography (SPECT) exam that uses of a radiotracer that binds dopamine receptors. In fact, by using such exam it was possible to identify a sub-group of PD patients known as “Scans without evidence of dopaminergic deficit” (SWEDD) that present a normal exam, unlike PD patients. In this study, an approach based on Convolutional Neural Networks (CNNs) was proposed for classifying PD patients, SWEDD patients and healthy subjects using SPECT and Magnetic Resonance Imaging (MRI) images. Then, these images were divided into subsets of slices in the axial view that contains particular regions of interest since 2D images are the norm in clinical practice. The classifier evaluation was performed with Cohen’s Kappa and Receiver Operating Characteristic (ROC) curve. The results obtained allow to conclude that the CNN using imaging information of the Basal Ganglia and the mesencephalon was able to distinguish PD patients from healthy subjects since achieved 97.4% accuracy using MRI and 92.4% accuracy using SPECT, and PD from SWEDD with 97.3% accuracy using MRI and 93.3% accuracy using SPECT. Nonetheless, using the same approach, it was not possible to discriminate SWEDD patients from healthy subjects (60% accuracy) using DaTscanTM and MRI. These results allow to conclude that this approach may be a useful tool to aid in PD diagnosis in the future

(I123)FP-CIT reporting: Machine Learning, Effectiveness and Clinical Integration

(I123)FP-CIT imaging is used for differential diagnosis of clinically uncertain Parkinsonian Syndromes. Conventional reporting relies on visual interpretation of images and analysis of semi-quantification results. However, this form of reporting is associated with variable diagnostic accuracy results. The first half of this thesis clarifies whether machine learning classification algorithms, used as computer aided diagnosis (CADx) tool, can offer improved performance. Candidate machine learning classification algorithms were developed and compared to a range of semi-quantitative methods, which showed the superiority of machine learning tools in terms of binary classification performance. The best of the machine learning algorithms, based on 5 principal components and a linear Support Vector Machine classifier, was then integrated into clinical software for a reporting exercise (pilot and main study). Results demonstrated that the CADx software had a consistently high standalone accuracy. In general, CADx caused reporters to give more consistent decisions and resulted in improved diagnostic accuracy when viewing images with unfamiliar appearances. However, although these results were undoubtedly impressive, it was also clear that a number of additional, significant hurdles remained, that needed to be overcome before widespread clinical adoption could be achieved. Consequently, the second half of this thesis focuses on addressing one particular aspect of the remaining translation gap for (I123)FP-CIT classification software, namely heterogeneity of the clinical environment. Introduction of new technology, such as machine learning, may require new metrics, which in this work were informed through novel methods (such as the use of innovative phantoms) and strategies, enabling sensitivity testing to be developed, applied and evaluated. The pathway to acceptance of novel and progressive technology in the clinic is a tortuous one, and this thesis emphasises the importance of many factors in addition to the core technology that need to be addressed if such tools are ever to achieve clinical adoption