J Korean Med Sci.  2018 Oct;33(43):e239. 10.3346/jkms.2018.33.e239.

A Novel Fundus Image Reading Tool for Efficient Generation of a Multi-dimensional Categorical Image Database for Machine Learning Algorithm Training

• Park SJ
• Shin JY
• Kim S
• Son J
• Jung KH
• Park KH

• KMID: 2422699
• DOI: http://doi.org/10.3346/jkms.2018.33.e239

Abstract

BACKGROUND
We described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system.
METHODS
A 5-step retinal fundus image reading tool was developed that rates image quality, presence of abnormality, findings with location information, diagnoses, and clinical significance. Each image was evaluated by 3 different graders. Agreements among graders for each decision were evaluated.
RESULTS
The 234,242 readings of 79,458 images were collected from 55 licensed ophthalmologists during 6 months. The 34,364 images were graded as abnormal by at-least one rater. Of these, all three raters agreed in 46.6% in abnormality, while 69.9% of the images were rated as abnormal by two or more raters. Agreement rate of at-least two raters on a certain finding was 26.7%-65.2%, and complete agreement rate of all-three raters was 5.7%-43.3%. As for diagnoses, agreement of at-least two raters was 35.6%-65.6%, and complete agreement rate was 11.0%-40.0%. Agreement of findings and diagnoses were higher when restricted to images with prior complete agreement on abnormality. Retinal/glaucoma specialists showed higher agreements on findings and diagnoses of their corresponding subspecialties.
CONCLUSION
This novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

Keyword

Retina Fundus Image; Reading Tool; Grader; Machine Learning; Deep Learning

MeSH Terms

Dataset
Decision Support Systems, Clinical
Diagnosis
Machine Learning*
Reading
Retinaldehyde
Specialization
Retinaldehyde