Table 2 Diagnostic performance of the AI model compared with physicians with varying experience (range from 3 to >10 years) in CMR reading

From: Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging

  

No. of subjects (n = 500)

F1 score

AI model

Physician (3–5 years)

Physician (5–10 years)

Physician (>10 years)

1

HCM

100

0.971

0.957

0.938

0.962

2

DCM

100

0.914

0.853

0.911

0.940

3

CAD

80

0.962

0.916

0.949

0.969

4

LVNC

30

0.877

0.667

0.778

0.885

5

RCM

30

0.933

0.578

0.760

0.800

6

CAM

30

0.947

0.667

0.931

0.931

7

HHD

30

0.833

0.615

0.667

0.896

8

Myocarditis

20

0.857

0.553

0.600

0.683

9

ARVC

30

0.897

0.451

0.814

0.983

10

PAH

30

0.983

0.061

0.929

0.931

11

Ebstein’s anomaly

20

0.950

0.519

0.842

0.974

Frequency-weighted F1

0.931

0.734

0.872

0.927

Accuracy

0.932

0.746

0.868

0.928

Time cost (in total)

1.94 min

576 min

329 min

418 min

  1. The physicians are categorized according to their number of years of experience in CMR interpretation.
  2. The bold font emphasizes the superior performance metric among subgroups, including the AI model and physicians with varying levels of experience.
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