Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [18F]FP-CIT Positron Emission Tomography

Abstract

The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [18F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [18F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran’s Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [18F]FP-CIT PET, and its performance was comparable to that of NM physicians.

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

“…R-CNN models use region-based networks, which are capable of detecting an object in an image and holds great potential especially in diagnostic imaging [59].…”

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

“…Neural Network (R-CNN) R-CNN models use region based networks, which are capable to detect an object in an image and holds great potential especially in diagnostic imaging [57].…”

“…The predicted variables adopted were the following: age, sex, chief complaint, symptom onset to arrival time, arrival mode, trauma, and vital signs. A DL-based algorithm was developed using a Multilayer Perceptron (MLP) [57] and derivation data, consisting of patient data from January 2014 to June 2016. Test data was then fed into the algorithm, and a risk score between 0 and 1 was obtained, corresponding to the risk of critical care, involving direct admission to the Pediatric Intensive Care Unit (PICU) from the PED or transfer to other hospitals for PICU admission.…”

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

“…The key concept behind the R-CNN series is region proposals. Region proposals are used to localize objects within an image [46,47].…”