Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson’s disease: a systematic review and meta-analysis
Abstract Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson’s disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databas...
| Main Authors: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
|
| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01012-z |
| _version_ | 1827328957959110656 |
|---|---|
| author | Jing Wang Le Xue Jiehui Jiang Fengtao Liu Ping Wu Jiaying Lu Huiwei Zhang Weiqi Bao Qian Xu Zizhao Ju Li Chen Fangyang Jiao Huamei Lin Jingjie Ge Chuantao Zuo Mei Tian |
| author_facet | Jing Wang Le Xue Jiehui Jiang Fengtao Liu Ping Wu Jiaying Lu Huiwei Zhang Weiqi Bao Qian Xu Zizhao Ju Li Chen Fangyang Jiao Huamei Lin Jingjie Ge Chuantao Zuo Mei Tian |
| author_sort | Jing Wang |
| collection | DOAJ |
| description | Abstract Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson’s disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94–0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87–0.93) for glucose metabolism (18F-FDG). 13 studies were identified that distinguished PD from the atypical parkinsonism (AP), with a pooled AUC of 0.93 (95% CI: 0.91 − 0.95) for presynaptic DA, 0.79 (95% CI: 0.75–0.82) for postsynaptic DA, and 0.97 (95% CI: 0.96–0.99) for 18F-FDG. Acceptable diagnostic performance of PD with AI algorithms-assisted PET imaging was highlighted across the subgroups. More rigorous reporting standards that take into account the unique challenges of AI research could improve future studies. |
| first_indexed | 2024-03-07T15:25:57Z |
| format | Article |
| id | doaj.art-99122795377f40a89656f9086e6c2b13 |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-07T15:25:57Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-99122795377f40a89656f9086e6c2b132024-03-05T17:06:38ZengNature Portfolionpj Digital Medicine2398-63522024-01-017111110.1038/s41746-024-01012-zDiagnostic performance of artificial intelligence-assisted PET imaging for Parkinson’s disease: a systematic review and meta-analysisJing Wang0Le Xue1Jiehui Jiang2Fengtao Liu3Ping Wu4Jiaying Lu5Huiwei Zhang6Weiqi Bao7Qian Xu8Zizhao Ju9Li Chen10Fangyang Jiao11Huamei Lin12Jingjie Ge13Chuantao Zuo14Mei Tian15Huashan Hospital & Human Phenome Institute, Fudan UniversityDepartment of Nuclear Medicine, the Second Hospital of Zhejiang University School of MedicineInstitute of Biomedical Engineering, School of Life Science, Shanghai UniversityDepartment of Neurology, Huashan Hospital, Fudan UniversityDepartment of Nuclear Medicine/PET Center, Huashan Hospital, Fudan UniversityDepartment of Nuclear Medicine/PET Center, Huashan Hospital, Fudan UniversityDepartment of Nuclear Medicine/PET Center, Huashan Hospital, Fudan UniversityDepartment of Nuclear Medicine/PET Center, Huashan Hospital, Fudan UniversityDepartment of Nuclear Medicine/PET Center, Huashan Hospital, Fudan UniversityDepartment of Nuclear Medicine/PET Center, Huashan Hospital, Fudan UniversityDepartment of Ultrasound Medicine, Huashan Hospital, Fudan UniversityDepartment of Nuclear Medicine/PET Center, Huashan Hospital, Fudan UniversityDepartment of Nuclear Medicine/PET Center, Huashan Hospital, Fudan UniversityDepartment of Nuclear Medicine/PET Center, Huashan Hospital, Fudan UniversityHuashan Hospital & Human Phenome Institute, Fudan UniversityHuashan Hospital & Human Phenome Institute, Fudan UniversityAbstract Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson’s disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94–0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87–0.93) for glucose metabolism (18F-FDG). 13 studies were identified that distinguished PD from the atypical parkinsonism (AP), with a pooled AUC of 0.93 (95% CI: 0.91 − 0.95) for presynaptic DA, 0.79 (95% CI: 0.75–0.82) for postsynaptic DA, and 0.97 (95% CI: 0.96–0.99) for 18F-FDG. Acceptable diagnostic performance of PD with AI algorithms-assisted PET imaging was highlighted across the subgroups. More rigorous reporting standards that take into account the unique challenges of AI research could improve future studies.https://doi.org/10.1038/s41746-024-01012-z |
| spellingShingle | Jing Wang Le Xue Jiehui Jiang Fengtao Liu Ping Wu Jiaying Lu Huiwei Zhang Weiqi Bao Qian Xu Zizhao Ju Li Chen Fangyang Jiao Huamei Lin Jingjie Ge Chuantao Zuo Mei Tian Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson’s disease: a systematic review and meta-analysis npj Digital Medicine |
| title | Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson’s disease: a systematic review and meta-analysis |
| title_full | Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson’s disease: a systematic review and meta-analysis |
| title_fullStr | Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson’s disease: a systematic review and meta-analysis |
| title_full_unstemmed | Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson’s disease: a systematic review and meta-analysis |
| title_short | Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson’s disease: a systematic review and meta-analysis |
| title_sort | diagnostic performance of artificial intelligence assisted pet imaging for parkinson s disease a systematic review and meta analysis |
| url | https://doi.org/10.1038/s41746-024-01012-z |
| work_keys_str_mv | AT jingwang diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT lexue diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT jiehuijiang diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT fengtaoliu diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT pingwu diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT jiayinglu diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT huiweizhang diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT weiqibao diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT qianxu diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT zizhaoju diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT lichen diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT fangyangjiao diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT huameilin diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT jingjiege diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT chuantaozuo diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis AT meitian diagnosticperformanceofartificialintelligenceassistedpetimagingforparkinsonsdiseaseasystematicreviewandmetaanalysis |