Classification of Parkinson’s disease by deep learning on midbrain MRI.
Purpose: Susceptibility map weighted imaging (SMWI), based on quantitative susceptibility mapping (QSM), allows accurate nigrosome-1 (N1) evaluation and has been used to develop Parkinson’s disease (PD) deep learning (DL) classification algorithms. Neuromelanin-sensitive (NMS) MRI could improve auto...
| Publicado en: | Frontiers in Aging Neuroscience pp. 1 - 13 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179424800&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179424800 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 2024 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 179424800 179424800 179424800 10.3389/fnagi.2024.1425095 179424800 ppf: 1 ppct: 12 formats: tig: atl: Classification of Parkinson’s disease by deep learning on midbrain MRI. aug: au: Welton, Thomas Hartono, Septian Weiling Lee Peik Yen Teh Wenlu Hou Chun Chen, Robert Chen, Celeste Ee Wei Lim Prakash, Kumar M. Tan, Louis C. S. Eng King Tan Ling Ling Chan affil: National Neuroscience Institute (NNI), Singapore, Singapore. sug: subj: Parkinson Disease Diagnosis Deep Learning Algorithms Diagnosis, Computer Assisted Magnetic Resonance Imaging Methods Brain Biological Markers Funding Source Human Male Female Middle Age Aged Case Control Studies Spearman's Rank Correlation Coefficient Severity of Illness Indices Scales Data Analysis Software Descriptive Statistics ROC Curve Post Hoc Analysis Sensitivity and Specificity Mann-Whitney U Test False Positive Results False Negative Results Comparative Studies Levodopa Administration and Dosage Unpaired T-Tests Chi Square Test Kendall's tau Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Purpose: Susceptibility map weighted imaging (SMWI), based on quantitative susceptibility mapping (QSM), allows accurate nigrosome-1 (N1) evaluation and has been used to develop Parkinson’s disease (PD) deep learning (DL) classification algorithms. Neuromelanin-sensitive (NMS) MRI could improve automated quantitative N1 analysis by revealing neuromelanin content. This study aimed to compare classification performance of four approaches to PD diagnosis: (1) N1 quantitative “QSM-NMS” composite marker, (2) DL model for N1 morphological abnormality using SMWI (“Heuron IPD”), (3) DL model for N1 volume using SMWI (“Heuron NI”), and (4) N1 SMWI neuroradiological evaluation. Method: PD patients (n = 82; aged 65 ± 9 years; 68% male) and healthy-controls (n = 107; 66 ± 7 years; 48% male) underwent 3 T midbrain MRI with T2*-SWI multi-echo-GRE (for QSM and SMWI), and NMS-MRI. AUC was used to compare diagnostic performance. We tested for correlation of each imaging measure with clinical parameters (severity, duration and levodopa dosing) by Spearman-Rho or Kendall-Tao-Beta correlation. Results: Classification performance was excellent for the QSM-NMS composite marker (AUC = 0.94), N1 SMWI abnormality (AUC = 0.92), N1 SMWI volume (AUC = 0.90), and neuroradiologist (AUC = 0.98). Reasons for misclassification were right–left asymmetry, through-plane re-slicing, pulsation artefacts, and thin N1. In the two DL models, all 18/189 (9.5%) cases misclassified by Heuron IPD were controls with normal N1 volumes. We found significant correlation of the SN QSM-NMS composite measure with levodopa dosing (rho = −0.303, p = 0.006). Conclusion: Our data demonstrate excellent performance of a quantitative QSM-NMS marker and automated DL PD classification algorithms based on midbrain MRI, while suggesting potential further improvements. Clinical utility is supported but requires validation in earlier stage PD cohorts. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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