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

Two new azaphilone derivatives containing 1,3-dioxolane moiety, penidioxolanes A (1) and B (2), were isolated from marine-derived fungus Penicillium sp. KCB12C078, together with four known compounds (3-6) by chemical investigation. Compounds 1 - 6 were isolated by combination of silica gel, ODS column chromatography and preparative HPLC. Their structures were determined by analysis of spectroscopic data including 1D-, 2D-NMR, and MS techniques. The isolates were evaluated against cancer cell growth inhibition effects and antimicrobial activity.
Chromatography ; Chromatography, High Pressure Liquid ; Fungi ; Penicillium* ; Silica Gel