Skip to main content
. 2023 Nov 15;2023(11):CD014911. doi: 10.1002/14651858.CD014911.pub2

Subramaniam 2022.

Study characteristics
Patient Sampling The SyntEye KTC model (Rozema et al.) has been used to generate the data set that is to be used for the training of the convolutional neural network. The data set consists of topography images of healthy normal eyes, developing keratoconus eyes, and keratoconus eyes.
Patient characteristics and setting Data set consists of subclinical keratoconus and keratoconus eyes.
Index tests Convolutional neural network. It analyses topography images and classifies them into 3 categories: normal, subclinical and keratoconus.
The article provides a clear explanation of the model and training procedure.
Target condition and reference standard(s) The topography images were artificially synthesized by a program called Synteye; it made 300 images of each classification: normal, subclinical keratoconus, and keratoconus. No human observation is mentioned.
Flow and timing All cases were included in the reference standard and index test. All data were presented in a 2 × 2 table.
Comparative Not applicable
Notes No funding source mentioned.
Methodological quality
Item Authors' judgement Risk of bias Applicability concerns
DOMAIN 1: Patient selection
Was a consecutive or random sample of patients enrolled? No    
Was a case‐control design avoided? No    
Did the study avoid inappropriate exclusions? No    
Could the selection of patients have introduced bias?   High risk  
Are there concerns that the included patients and setting do not match the review question?     High
DOMAIN 2: Index test (All tests)
Were the index test results interpreted without knowledge of the results of the reference standard? Unclear    
If a threshold was used, was it pre‐specified? Unclear    
Was the model designed in an appropriate manner? Yes    
Could the conduct or interpretation of the index test have introduced bias?   Unclear risk  
Are there concerns that the index test, its conduct, or interpretation differ from the review question?     Low concern
DOMAIN 3: Reference standard
Is the reference standard likely to correctly classify the target condition? No    
Were the reference standard results interpreted without knowledge of the results of the index tests? Unclear    
Could the reference standard, its conduct, or its interpretation have introduced bias?   Unclear risk  
Are there concerns that the target condition as defined by the reference standard does not match the question?     Low concern
DOMAIN 4: Flow and timing
Did all patients receive the same reference standard? Yes    
Were all patients included in the analysis? Yes    
Could the patient flow have introduced bias?   Low risk  
DOMAIN 5: Comparative
Were different AI tests were developed and interpreted without knowledge of each other.      
Are the proportions and reasons for missing data similar for all index tests?