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Table 1 Characteristics of AI-models

From: Deep learning assisted quantitative assessment of histopathological markers of Alzheimer’s disease and cerebral amyloid angiopathy

AI model

Aβ-model

Iron model

Fibrin model

GFAP model

CD68 model

Calcium model

Stain

IHC for Aβ

Perls’ Prussian blue

IHC for Fibrin(ogen)

IHC for GFAP

IHC for CD68

von Kossa

No. of training sections/total no. of sections in dataset

13/146

16/144

13/142

11/144

8/39*

9/44*

Layer 1

Area

Area

Area

Area

Area

Area

 

Leptomeningeal vessels

Tissue

Non-vascular tissue

Tissue

Tissue

Tissue

 

Tissue

 

Vascular fibrin positive tissue

   

Layer 2

Area

Object

Object

Object

Object

Area (Calcium positive)

 

CAA

Iron positive cells

Fibrin positive cells

Reactive astrocytes

CD68 positive cells

Neuronal

 

Amyloid-β plaques

    

Vascular

      

Extracellular

Layer 3

     

Object

      

Calcium positive cells

No. of sections excluded after QC

4

5

3

0

0

0

No. of sections for validation

13

14

14

14

4

4

  1. The table summarizes the six models described in this study, listing the kind of staining for each set of sections, the number of sections on which the model was trained, and the layers by which the model was built. For each layer it is reported whether the convolutional neural network was trained to recognize an area or an object. Finally, the total number of sections on which the model was applied, those excluded after QC, and those on which the model was validated is reported. Key: IHC: immunohistochemistry; AD: Alzheimer’s disease; CAA: cerebral amyloid angiopathy; QC: quality control
  2. *Note that the CD68 and calcium models were trained on and applied to sections derived only from cohorts 2 and 3 (AD cases), whereas the other four models used data from all 3 cohorts (AD and CAA cases)