Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging
Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD), which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver variability and result in inconsistent diagnosi...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2017-01-01
|
| Series: | NeuroImage: Clinical |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158217302243 |
| _version_ | 1819037353655664640 |
|---|---|
| author | Hongyoon Choi Seunggyun Ha Hyung Jun Im Sun Ha Paek Dong Soo Lee |
| author_facet | Hongyoon Choi Seunggyun Ha Hyung Jun Im Sun Ha Paek Dong Soo Lee |
| author_sort | Hongyoon Choi |
| collection | DOAJ |
| description | Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD), which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver variability and result in inconsistent diagnosis. In this study, we developed a deep learning-based FP-CIT SPECT interpretation system to refine the imaging diagnosis of Parkinson's disease. This system trained by SPECT images of PD patients and normal controls shows high classification accuracy comparable with the experts' evaluation referring quantification results. Its high accuracy was validated in an independent cohort composed of patients with PD and nonparkinsonian tremor. In addition, we showed that some patients clinically diagnosed as PD who have scans without evidence of dopaminergic deficit (SWEDD), an atypical subgroup of PD, could be reclassified by our automated system. Our results suggested that the deep learning-based model could accurately interpret FP-CIT SPECT and overcome variability of human evaluation. It could help imaging diagnosis of patients with uncertain Parkinsonism and provide objective patient group classification, particularly for SWEDD, in further clinical studies. Keywords: Parkinson's disease, FP-CIT, Deep learning, Deep neural network, SWEDD |
| first_indexed | 2024-12-21T08:20:04Z |
| format | Article |
| id | doaj.art-474bef14934c4f45858ee1bead2fa445 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-21T08:20:04Z |
| publishDate | 2017-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-474bef14934c4f45858ee1bead2fa4452022-12-21T19:10:27ZengElsevierNeuroImage: Clinical2213-15822017-01-0116586594Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imagingHongyoon Choi0Seunggyun Ha1Hyung Jun Im2Sun Ha Paek3Dong Soo Lee4Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of KoreaDepartment of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of KoreaDepartment of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of KoreaDepartment of Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea; Correspondence to: S.H. Paek, Department of Neurosurgery, Seoul National University Hospital, 28 Yongon-Dong, Jongno-Gu, Seoul 110-744, Republic of Korea.Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea; Korea Brain Research Institute, Daegu, Republic of Korea; Correspondence to: D.S. Lee, Department of Nuclear Medicine, Seoul National University Hospital, 28 Yongon-Dong, Jongno-Gu, Seoul 110-744, Republic of Korea.Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD), which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver variability and result in inconsistent diagnosis. In this study, we developed a deep learning-based FP-CIT SPECT interpretation system to refine the imaging diagnosis of Parkinson's disease. This system trained by SPECT images of PD patients and normal controls shows high classification accuracy comparable with the experts' evaluation referring quantification results. Its high accuracy was validated in an independent cohort composed of patients with PD and nonparkinsonian tremor. In addition, we showed that some patients clinically diagnosed as PD who have scans without evidence of dopaminergic deficit (SWEDD), an atypical subgroup of PD, could be reclassified by our automated system. Our results suggested that the deep learning-based model could accurately interpret FP-CIT SPECT and overcome variability of human evaluation. It could help imaging diagnosis of patients with uncertain Parkinsonism and provide objective patient group classification, particularly for SWEDD, in further clinical studies. Keywords: Parkinson's disease, FP-CIT, Deep learning, Deep neural network, SWEDDhttp://www.sciencedirect.com/science/article/pii/S2213158217302243 |
| spellingShingle | Hongyoon Choi Seunggyun Ha Hyung Jun Im Sun Ha Paek Dong Soo Lee Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging NeuroImage: Clinical |
| title | Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging |
| title_full | Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging |
| title_fullStr | Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging |
| title_full_unstemmed | Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging |
| title_short | Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging |
| title_sort | refining diagnosis of parkinson s disease with deep learning based interpretation of dopamine transporter imaging |
| url | http://www.sciencedirect.com/science/article/pii/S2213158217302243 |
| work_keys_str_mv | AT hongyoonchoi refiningdiagnosisofparkinsonsdiseasewithdeeplearningbasedinterpretationofdopaminetransporterimaging AT seunggyunha refiningdiagnosisofparkinsonsdiseasewithdeeplearningbasedinterpretationofdopaminetransporterimaging AT hyungjunim refiningdiagnosisofparkinsonsdiseasewithdeeplearningbasedinterpretationofdopaminetransporterimaging AT sunhapaek refiningdiagnosisofparkinsonsdiseasewithdeeplearningbasedinterpretationofdopaminetransporterimaging AT dongsoolee refiningdiagnosisofparkinsonsdiseasewithdeeplearningbasedinterpretationofdopaminetransporterimaging |