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Article

Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence

by
Wadi’ Othmani
1,
Arthur Coste
1,
Dimitri Papathanassiou
1,2,3 and
David Morland
1,2,3,*
1
Médecine Nucléaire, Institut Godinot, 51100 Reims, France
2
CReSTIC, UR 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
3
Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
*
Author to whom correspondence should be addressed.
Diagnostics 2025, 15(11), 1407; https://doi.org/10.3390/diagnostics15111407
Submission received: 17 March 2025 / Revised: 20 May 2025 / Accepted: 29 May 2025 / Published: 31 May 2025
(This article belongs to the Special Issue Application of Neural Networks in Medical Diagnosis)

Abstract

Background/Objectives: 123I-FP-CIT dopamine transporter imaging is commonly used for the diagnosis of Parkinsonian syndromes in patients whose clinical presentation is atypical. Prolonged immobility, which can be difficult to maintain in this population, is required to perform SPECT acquisition. In this study we aimed to develop a Convolutional Neural Network (CNN) able to predict the outcome of the full examination based on the first acquired projection, and reliably detect normal patients. Methods: All 123I-FP-CIT SPECT performed in our center between June 2017 and February 2024 were included and split between a training and a validation set (70%/30%). An additional 100 SPECT were used as an independent test set. Examinations were labeled by two independent physicians. A VGG16-like CNN model was trained to assess the probability of examination abnormality from the first acquired projection (anterior and posterior view at 0°), taking age into consideration. A threshold maximizing sensitivity while maintaining good diagnostic accuracy was then determined. The model was validated in the independent testing set. Saliency maps were generated to visualize the most impactful areas in the classification. Results: A total of 982 123I-FP-CIT SPECT were retrieved and labelled (training set: 618; validation set: 264; independent testing set: 100). The trained model achieved a sensibility of 98.0% and a negative predictive value of 96.3% (one false negative) while maintaining an accuracy of 75.0%. The saliency maps confirmed that the regions with the greatest impact on the final classification corresponded to clinically relevant areas (basal ganglia and background noise). Conclusions: Our results suggest that this trained CNN could be used to exclude presynaptic dopaminergic loss with high reliability from the first acquired projection. It could be particularly useful in patients with compliance issues. Confirmation with images from other centers will be necessary.
Keywords: Parkinsonian disorders; dopamine plasma membrane transport proteins; tomography; emission-computed; single-photon; basal ganglia; iodine-123; artificial intelligence; neural networks; computer Parkinsonian disorders; dopamine plasma membrane transport proteins; tomography; emission-computed; single-photon; basal ganglia; iodine-123; artificial intelligence; neural networks; computer

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MDPI and ACS Style

Othmani, W.; Coste, A.; Papathanassiou, D.; Morland, D. Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence. Diagnostics 2025, 15, 1407. https://doi.org/10.3390/diagnostics15111407

AMA Style

Othmani W, Coste A, Papathanassiou D, Morland D. Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence. Diagnostics. 2025; 15(11):1407. https://doi.org/10.3390/diagnostics15111407

Chicago/Turabian Style

Othmani, Wadi’, Arthur Coste, Dimitri Papathanassiou, and David Morland. 2025. "Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence" Diagnostics 15, no. 11: 1407. https://doi.org/10.3390/diagnostics15111407

APA Style

Othmani, W., Coste, A., Papathanassiou, D., & Morland, D. (2025). Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence. Diagnostics, 15(11), 1407. https://doi.org/10.3390/diagnostics15111407

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