Real-World Testing of a Machine Learning-Derived Visual Scale for Tc99m TRODAT-1 for Diagnosing Lewy Body Disease: Comparison with a Traditional Approach Using Semiquantification

Pai-Yi Chiu; Po-Nien Hou; Guang-Uei Hung; Te-Chun Hsieh; Pak-Ki Chan; Chia-Hung Kao

doi:10.3390/jpm12091369

Real-World Testing of a Machine Learning-Derived Visual Scale for Tc99m TRODAT-1 for Diagnosing Lewy Body Disease: Comparison with a Traditional Approach Using Semiquantification

J Pers Med. 2022 Aug 25;12(9):1369. doi: 10.3390/jpm12091369.

Authors

Pai-Yi Chiu^{1

2}, Po-Nien Hou³, Guang-Uei Hung³, Te-Chun Hsieh^{4

5}, Pak-Ki Chan⁶, Chia-Hung Kao^{4

6

7

8}

Affiliations

¹ Department of Neurology, Show Chwan Memorial Hospital, Changhua 50008, Taiwan.
² Department of Applied Mathematics, Tunghai University, Taichung 40704, Taiwan.
³ Department of Nuclear Medicine, Chang Bing Show Chwan Memorial Hospital, Changhua 50544, Taiwan.
⁴ Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 40402, Taiwan.
⁵ Department of Biomedical Imaging and Radiological Science, Elite Campus, China Medical University, Taichung 40402, Taiwan.
⁶ Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung 40402, Taiwan.
⁷ Graduate Institute of Biomedical Sciences, Elite Campus, School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan.
⁸ Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.

Abstract

Objectives: Abnormal dopamine transporter (DAT) uptake is an important biomarker for diagnosing Lewy body disease (LBD), including Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). We evaluated a machine learning-derived visual scale (ML-VS) for Tc99m TRODAT-1 from one center and compared it with the striatal/background ratio (SBR) using semiquantification for diagnosing LBD in two other centers. Patients and Methods: This was a retrospective analysis of data from a history-based computerized dementia diagnostic system. MT-VS and SBR among normal controls (NCs) and patients with PD, PD with dementia (PDD), DLB, or Alzheimer’s disease (AD) were compared. Results: We included 715 individuals, including 122 NCs, 286 patients with PD, 40 with AD, 179 with DLB, and 88 with PDD. Compared with NCs, patients with PD exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. Compared with the AD group, PDD and DLB groups exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. The distribution of ML-VS was significantly different between PD and NC, DLB and AD, and PDD and AD groups (all p < 0.001). The correlation coefficient of ML-VS/SBR in all participants was 0.679. Conclusions: The ML-VS designed in one center is useful for differentiating PD from NC, DLB from AD, and PDD from AD in other centers. Its correlation with traditional approaches using different scanning machines is also acceptable. Future studies should develop models using data pools from multiple centers for increasing diagnostic accuracy.

Keywords: Alzheimer’s disease; Lewy body disease; Parkinson’s disease; Tc99m TRODAT-1; dementia with Lewy bodies; machine learning.

Grants and funding

This study is supported in part by China Medical University Hospital (DMR-110-089, DMR-111-090, DMR-111-091). The funders had no role in the study design, data collection and analysis, the decision to publish, or preparation of the manuscript. No additional external funding was received for this study.