title: Automated assessment of diabetic retinopathy severity using content-based image retrieval in multimodal fundus photographs.
abstract: OBJECTIVE
  Recent studies on diabetic retinopathy (DR) screening in fundus photographs suggest that disagreements between algorithms and clinicians are now comparable to disagreements among clinicians. The purpose of this study is to (1) determine whether this observation also holds for automated DR severity assessment algorithms, and (2) show the interest of such algorithms in clinical practice.
  
  
  METHODS
  A dataset of 85 consecutive DR examinations (168 eyes, 1176 multimodal eye fundus photographs) was collected at Brest University Hospital (Brest, France). Two clinicians with different experience levels determined DR severity in each eye, according to the International Clinical Diabetic Retinopathy Disease Severity (ICDRS) scale. Based on Cohen's kappa (κ) measurements, the performance of clinicians at assessing DR severity was compared to the performance of state-of-the-art content-based image retrieval (CBIR) algorithms from our group.
  
  
  RESULTS
  At assessing DR severity in each patient, intraobserver agreement was κ = 0.769 for the most experienced clinician. Interobserver agreement between clinicians was κ = 0.526. Interobserver agreement between the most experienced clinicians and the most advanced algorithm was κ = 0.592. Besides, the most advanced algorithm was often able to predict agreements and disagreements between clinicians.
  
  
  CONCLUSIONS
  Automated DR severity assessment algorithms, trained to imitate experienced clinicians, can be used to predict when young clinicians would agree or disagree with their more experienced fellow members. Such algorithms may thus be used in clinical practice to help validate or invalidate their diagnoses. CBIR algorithms, in particular, may also be used for pooling diagnostic knowledge among peers, with applications in training and coordination of clinicians' prescriptions.
authors: Telecom Bretagne, Laboratoire Traitement de l’Information Me´dicale, Brest, France. gwenole.quellec@telecom-bretagne.eu
authors: Gwénolé
authors: G
authors: Quellec
authors: Mathieu
authors: M
authors: Lamard
authors: Guy
authors: G
authors: Cazuguel
authors: Lynda
authors: L
authors: Bekri
authors: Wissam
authors: W
authors: Daccache
authors: Christian
authors: C
authors: Roux
authors: Béatrice
authors: B
authors: Cochener
mesh_terms: D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D003922:Diabetes Mellitus, Type 1 / Q000150:complications*; D003924:Diabetes Mellitus, Type 2 / Q000150:complications*; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D003941:Diagnostic Techniques, Ophthalmological / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D010781:Photography / Q000379:methods* / Q000706:statistics & numerical data; D012720:Severity of Illness Index