title: Predicting AD conversion: comparison between prodromal AD guidelines and computer assisted PredictAD tool.
abstract: OBJECTIVE
  To compare the accuracies of predicting AD conversion by using a decision support system (PredictAD tool) and current research criteria of prodromal AD as identified by combinations of episodic memory impairment of hippocampal type and visual assessment of medial temporal lobe atrophy (MTA) on MRI and CSF biomarkers.
  
  
  METHODS
  Altogether 391 MCI cases (158 AD converters) were selected from the ADNI cohort. All the cases had baseline cognitive tests, MRI and/or CSF levels of Aβ1-42 and Tau. Using baseline data, the status of MCI patients (AD or MCI) three years later was predicted using current diagnostic research guidelines and the PredictAD software tool designed for supporting clinical diagnostics. The data used were 1) clinical criteria for episodic memory loss of the hippocampal type, 2) visual MTA, 3) positive CSF markers, 4) their combinations, and 5) when the PredictAD tool was applied, automatically computed MRI measures were used instead of the visual MTA results. The accuracies of diagnosis were evaluated with the diagnosis made 3 years later.
  
  
  RESULTS
  The PredictAD tool achieved the overall accuracy of 72% (sensitivity 73%, specificity 71%) in predicting the AD diagnosis. The corresponding number for a clinician's prediction with the assistance of the PredictAD tool was 71% (sensitivity 75%, specificity 68%). Diagnosis with the PredictAD tool was significantly better than diagnosis by biomarkers alone or the combinations of clinical diagnosis of hippocampal pattern for the memory loss and biomarkers (p≤0.037).
  
  
  CONCLUSIONS
  With the assistance of PredictAD tool, the clinician can predict AD conversion more accurately than the current diagnostic criteria.
authors: Department of Neurology, University of Eastern Finland, Kuopio University Hospital, Kuopio, Finland.
authors: Yawu
authors: Y
authors: Liu
authors: Jussi
authors: J
authors: Mattila
authors: Miguel Ángel Muñoz
authors: MÁ
authors: Ruiz
authors: Teemu
authors: T
authors: Paajanen
authors: Juha
authors: J
authors: Koikkalainen
authors: Mark
authors: M
authors: van Gils
authors: Sanna-Kaisa
authors: SK
authors: Herukka
authors: Gunhild
authors: G
authors: Waldemar
authors: Jyrki
authors: J
authors: Lötjönen
authors: Hilkka
authors: H
authors: Soininen
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000175:diagnosis*; D060825:Cognitive Dysfunction / Q000175:diagnosis; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D017410:Practice Guidelines as Topic; D062706:Prodromal Symptoms; D012680:Sensitivity and Specificity; D012984:Software