Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson’s disease: a systematic review and meta-analysis

Wang, Jing; Xue, Le; Jiang, Jiehui; Liu, Fengtao; Wu, Ping; Lu, Jiaying; Zhang, Huiwei; Bao, Weiqi; Xu, Qian; Ju, Zizhao; Chen, Li; Jiao, Fangyang; Lin, Huamei; Ge, Jingjie; Zuo, Chuantao; Tian, Mei

doi:10.1038/s41746-024-01012-z

Cited by 8 publications

(1 citation statement)

References 59 publications

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“…Additionally, AI/ML introduced new approaches in computer-aided detection, assisted interpretation, computer-aided diagnosis [ 13 , 17 , 19 ], as well as for reducing administered dose and radiation exposure to the patient [ 15 ]. From the clinical perspective, AI/ML applications span the entire spectrum of nuclear medicine, with the three main areas being: cardiology for myocardial perfusion imaging and detection of coronary artery disease or prediction of major adverse cardiac events [ 16 , 20 ]; neurology for neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and other dementia [ 16 , 21 , 22 ]; and oncology, where it significantly impacted lesion detection and tracking, radiopharmaceutical therapies with precision dosimetry, personalized treatment planning, and therapy response assessment [ 12 , 16 , 19 , 23 ]. AI/ML applications explored new venues in drug discovery, radiopharmacy, and radiochemistry [ 24 ], that greatly enhanced the evolving field of theranostics [ 12 , 23 , 25 ].…”

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confidence: 99%

Gastric Emptying Scintigraphy Protocol Optimization Using Machine Learning for the Detection of Delayed Gastric Emptying

Georgiou,

Sfakianaki,

Diaz-Kanelidis

et al. 2024

Diagnostics

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Purpose: The purpose of this study is to examine the feasibility of a machine learning (ML) system for optimizing a gastric emptying scintigraphy (GES) protocol for the detection of delayed gastric emptying (GE), which is considered a primary indication for the diagnosis of gastroparesis. Methods: An ML model was developed using the JADBio AutoML artificial intelligence (AI) platform. This model employs the percent GE at various imaging time points following the ingestion of a standardized radiolabeled meal to predict normal versus delayed GE at the conclusion of the 4 h GES study. The model was trained and tested on a cohort of 1002 patients who underwent GES using a 70/30 stratified split ratio for training vs. testing. The ML software automated the generation of optimal predictive models by employing a combination of data preprocessing, appropriate feature selection, and predictive modeling analysis algorithms. Results: The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to evaluate the predictive modeling performance. Several models were developed using different combinations of imaging time points as input features and methodologies to achieve optimal output. By using GE values at time points 0.5 h, 1 h, 1.5 h, 2 h, and 2.5 h as input predictors of the 4 h outcome, the analysis produced an AUC of 90.7% and a balanced accuracy (BA) of 80.0% on the test set. This performance was comparable to the training set results (AUC = 91.5%, BA = 84.7%) within the 95% confidence interval (CI), demonstrating a robust predictive capability. Through feature selection, it was discovered that the 2.5 h GE value alone was statistically significant enough to predict the 4 h outcome independently, with a slightly increased test set performance (AUC = 92.4%, BA = 83.3%), thus emphasizing its dominance as the primary predictor for delayed GE. ROC analysis was also performed for single time imaging points at 1 h and 2 h to assess their independent predictiveness of the 4 h outcome. Furthermore, the ML model was tested for its ability to predict “flipping” cases with normal GE at 1 h and 2 h that became abnormal with delayed GE at 4 h. Conclusions: An AI/ML model was designed and trained for predicting delayed GE using a limited number of imaging time points in a 4 h GES clinical protocol. This study demonstrates the feasibility of employing ML for GES optimization in the detection of delayed GE and potentially shortening the protocol’s time length without compromising diagnostic power.

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