To explore the effectiveness of artificial intelligence (AI) deep learning technology in the screening and detecting glaucomatous optic neuropathy (GON).

Using diagnostic test research method, 200 fundus images (200 eyes) were collected from the glaucoma clinic of Beijing Tongren Eye Center from August 2013 to July 2019. According to the different ways of image reading, they were divided into two groups: AI system group and doctor group. Among of them, the doctor group included senior ophthalmologists (with more than 10 years′ experience in fundus image diagnosis), junior ophthalmologists (more than 5 years and less than 10 years) and general practitioners. All fundus images were read and labeled by AI system group and doctor group. The data of age and sex were normal distribution by S-W test, expressed as (±s). As the Levene test showed that the real variance was the same, the average image reading time of AI system was compared with that of doctors of different levels by one-way ANOVA, and the LSD-t test was used for pairwise comparison. Sensitivity, specificity and area under curve (AUC) were used to predict and evaluate the performance of AI model. The diagnostic coincidence rate was expressed as a percentage. Chi square test was used to compare the diagnostic coincidence rate between AI system and senior ophthalmologists, between AI system and doctors at different levels.

In the doctor group, 6 ophthalmologists (senior ophthalmologists, junior ophthalmologists and general practitioners) read images twice. The difference of diagnostic accuracy was statistically significant (χ²=4.324, 3.562, 4.213, 5.786, 10.546, 11.431; P<0.05). The sensitivity, specificity, diagnostic coincidence rate and AUC of AI image reading test were 100.0%, 88.6%, 90.5% and 0.934, respectively. With the help of AI system, the accuracy of labeling was improved significantly in 2 general practitioners, which reached 87.3% and 82.5%, respectively. With the aid of AI system, the average reading time of senior ophthalmologists, junior ophthalmologists and general practitioners was significantly shorter than that of independent film reading (t=4.175, 3.189, 6.160; P<0.05).

The application of standardized intelligent AI labeling assistant system is helpful for improving the accurate rate of diagnosis and effectiveness of doctors with different qualifications. In particular, it can quickly improve the accuracy of labeling by general practitioners and junior ophthalmologists.