2024
Deep learning analysis of fMRI data for predicting Alzheimer’s Disease: A focus on convolutional neural networks and model interpretability
Zhou X, Kedia S, Meng R, Gerstein M. Deep learning analysis of fMRI data for predicting Alzheimer’s Disease: A focus on convolutional neural networks and model interpretability. PLOS ONE 2024, 19: e0312848. PMID: 39630834, PMCID: PMC11616848, DOI: 10.1371/journal.pone.0312848.Peer-Reviewed Original ResearchConceptsConvolutional neural networkNeural networkAlzheimer's diseaseConvolutional neural network modelMultimodal medical datasetsDeep learning methodsPotential of deep learningGenetic risk factorsMedical datasetsAlzheimer's Disease Neuroimaging InitiativeAD predictionDeep learningDeep learning analysisLearning methodsMedical imagesPredicting Alzheimer's diseaseDetection of Alzheimer's diseaseModel interpretationEarly detection of Alzheimer's diseaseAccuracy levelGenetic factorsDatasetEarly detection of ADNetworkDetection of AD
2022
Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science
Knoedler L, Baecher H, Kauke-Navarro M, Prantl L, Machens HG, Scheuermann P, Palm C, Baumann R, Kehrer A, Panayi AC, Knoedler S. Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science. Journal Of Clinical Medicine 2022, 11: 4998. PMID: 36078928, PMCID: PMC9457271, DOI: 10.3390/jcm11174998.Peer-Reviewed Original ResearchAutomated grading systemEarly fusion techniqueHigh-level accuracyImage datasetsLate fusionComputer scienceNeural networkFacial poseFusion techniqueSurgeon workflowAccuracy levelAlgorithmGrading processGrading systemWorkflowInsufficient accuracyFP patientsAccuracyAccuracy outcomesClassification formDepartment of PlasticSequential methodUniversity Hospital RegensburgFacial palsy patientsHardware
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