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Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.
Chuter, Benton; Huynh, Justin; Bowd, Christopher; Walker, Evan; Rezapour, Jasmin; Brye, Nicole; Belghith, Akram; Fazio, Massimo A; Girkin, Christopher A; De Moraes, Gustavo; Liebmann, Jeffrey M; Weinreb, Robert N; Zangwill, Linda M; Christopher, Mark.
Afiliación
  • Chuter B; Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States.
  • Huynh J; Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States.
  • Bowd C; Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States.
  • Walker E; Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States.
  • Rezapour J; Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States.
  • Brye N; Department of Ophthalmology, University Medical Center Mainz, Germany.
  • Belghith A; Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States.
  • Fazio MA; Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States.
  • Girkin CA; School of Medicine, Callahan Eye Hospital, University of Alabama-Birmingham, Birmingham, Alabama, United States.
  • De Moraes G; School of Medicine, Callahan Eye Hospital, University of Alabama-Birmingham, Birmingham, Alabama, United States.
  • Liebmann JM; Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Medical Center, New York, New York, United States.
  • Weinreb RN; Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Medical Center, New York, New York, United States.
  • Zangwill LM; Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States.
  • Christopher M; Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States.
Transl Vis Sci Technol ; 13(1): 23, 2024 01 02.
Article en En | MEDLINE | ID: mdl-38285462
ABSTRACT

Purpose:

To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations.

Methods:

Image quality ground truth was determined by manual review of 2815 fundus photographs of healthy and POAG eyes from the Diagnostic Innovations in Glaucoma Study and African Descent and Glaucoma Evaluation Study (DIGS/ADAGES), as well as 11,350 from the Ocular Hypertension Treatment Study (OHTS). Human experts assessed a photograph as high quality if of sufficient quality to determine POAG status and poor quality if not. A DL quality model was trained on photographs from DIGS/ADAGES and tested on OHTS. The effect of DL quality assessment on DL POAG detection was measured using area under the receiver operating characteristic (AUROC).

Results:

The DL quality model yielded an AUROC of 0.97 for differentiating between high- and low-quality photographs; qualitative human review affirmed high model performance. Diagnostic accuracy of the DL POAG model was significantly greater (P < 0.001) in good (AUROC, 0.87; 95% CI, 0.80-0.92) compared with poor quality photographs (AUROC, 0.77; 95% CI, 0.67-0.88).

Conclusions:

The DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model. Translational Relevance Incorporating DL quality assessment into automated review of fundus photographs can help to decrease the burden of manual review and improve accuracy for automated DL POAG detection.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Glaucoma / Glaucoma de Ángulo Abierto / Hipertensión Ocular / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Guideline / Prognostic_studies / Qualitative_research Límite: Humans Idioma: En Revista: Transl Vis Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: EEUU / ESTADOS UNIDOS / ESTADOS UNIDOS DA AMERICA / EUA / UNITED STATES / UNITED STATES OF AMERICA / US / USA

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Glaucoma / Glaucoma de Ángulo Abierto / Hipertensión Ocular / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Guideline / Prognostic_studies / Qualitative_research Límite: Humans Idioma: En Revista: Transl Vis Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: EEUU / ESTADOS UNIDOS / ESTADOS UNIDOS DA AMERICA / EUA / UNITED STATES / UNITED STATES OF AMERICA / US / USA