Application of artificial intelligence system for screening multiple fundus diseases in Chinese primary healthcare settings: a real-world, multicentre and cross-sectional study of 4795 cases.

BACKGROUND/AIMS: This study evaluates the performance of the Airdoc retinal artificial intelligence system (ARAS) for detecting multiple fundus diseases in real-world scenarios in primary healthcare settings and investigates the fundus disease spectrum based on ARAS. METHODS: This real-world, multicentre, cross-sectional study was conducted in Shanghai and Xinjiang, China. Six primary healthcare settings were included in this study. Colour fundus photographs were taken and graded by ARAS and retinal specialists. The performance of ARAS is described by its accuracy, sensitivity, specificity and positive and negative predictive values. The spectrum of fundus diseases in primary healthcare settings has also been investigated. RESULTS: A total of 4795 participants were included. The median age was 57.0 (IQR 39.0-66.0) years, and 3175 (66.2%) participants were female. The accuracy, specificity and negative predictive value of ARAS for detecting normal fundus and 14 retinal abnormalities were high, whereas the sensitivity and positive predictive value varied in detecting different abnormalities. The proportion of retinal drusen, pathological myopia and glaucomatous optic neuropathy was significantly higher in Shanghai than in Xinjiang. Moreover, the percentages of referable diabetic retinopathy, retinal vein occlusion and macular oedema in middle-aged and elderly people in Xinjiang were significantly higher than in Shanghai. CONCLUSION: This study demonstrated the dependability of ARAS for detecting multiple retinal diseases in primary healthcare settings. Implementing the AI-assisted fundus disease screening system in primary healthcare settings might be beneficial in reducing regional disparities in medical resources. However, the ARAS algorithm must be improved to achieve better performance. TRIAL REGISTRATION NUMBER: NCT04592068.

Application of artificial intelligence-based dual-modality analysis combining fundus photography and optical coherence tomography in diabetic retinopathy screening in a community hospital.

BACKGROUND: To assess the feasibility and clinical utility of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) and macular edema (ME) by combining fundus photos and optical coherence tomography (OCT) images in a community hospital. METHODS: Fundus photos and OCT images were taken for 600 diabetic patients in a community hospital. Ophthalmologists graded these fundus photos according to the International Clinical Diabetic Retinopathy (ICDR) Severity Scale as the ground truth. Two existing trained AI models were used to automatically classify the fundus images into DR grades according to ICDR, and to detect concomitant ME from OCT images, respectively. The criteria for referral were DR grades 2-4 and/or the presence of ME. The sensitivity and specificity of AI grading were evaluated. The number of referable DR cases confirmed by ophthalmologists and AI was calculated, respectively. RESULTS: DR was detected in 81 (13.5%) participants by ophthalmologists and in 94 (15.6%) by AI, and 45 (7.5%) and 53 (8.8%) participants were diagnosed with referable DR by ophthalmologists and by AI, respectively. The sensitivity, specificity and area under the curve (AUC) of AI for detecting DR were 91.67%, 96.92% and 0.944, respectively. For detecting referable DR, the sensitivity, specificity and AUC of AI were 97.78%, 98.38% and 0.981, respectively. ME was detected from OCT images in 49 (8.2%) participants by ophthalmologists and in 57 (9.5%) by AI, and the sensitivity, specificity and AUC of AI were 91.30%, 97.46% and 0.944, respectively. When combining fundus photos and OCT images, the number of referrals identified by ophthalmologists increased from 45 to 75 and from 53 to 85 by AI. CONCLUSION: AI-based DR screening has high sensitivity and specificity and may feasibly improve the referral rate of community DR.

Artificial intelligence-based screening for diabetic retinopathy at community hospital.

OBJECTIVES: The purpose of this study is to assess the accuracy of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) and to explore the feasibility of applying AI-based technique to community hospital for DR screening. METHODS: Nonmydriatic fundus photos were taken for 889 diabetic patients who were screened in community hospital clinic. According to DR international classification standards, ophthalmologists and AI identified and classified these fundus photos. The sensitivity and specificity of AI automatic grading were evaluated according to ophthalmologists' grading. RESULTS: DR was detected by ophthalmologists in 143 (16.1%) participants and by AI in 145 (16.3%) participants. Among them, there were 101 (11.4%) participants diagnosed with referable diabetic retinopathy (RDR) by ophthalmologists and 103 (11.6%) by AI. The sensitivity, specificity and area under the curve (AUC) of AI for detecting DR were 90.79% (95% CI 86.4-94.1), 98.5% (95% CI 97.8-99.0) and 0.946 (95% CI 0.935-0.956), respectively. For detecting RDR, the sensitivity, specificity and AUC of AI were 91.18% (95% CI 86.4-94.7), 98.79% (95% CI 98.1-99.3) and 0.950 (95% CI 0.939-0.960), respectively. CONCLUSION: AI has high sensitivity and specificity in detecting DR and RDR, so it is feasible to carry out AI-based DR screening in outpatient clinic of community hospital.

[Detection of macular foveal by optical coherence tomography and its influence on results of measurement of central retina thickness].

OBJECTIVE: To detect macular foveal by Cirrus(TM) HD-OCT using the post acquisition fovea detection and to study its influence on the results of measurement of central retina thickness (CRT). METHODS: In this retrospectively studies, the 512×128 cube scans from 50 eyes of normal subjects and 50 eyes of macular edema patients were analyzed. Three methods for the foveal detection were used, the scan center (SC), the automated detected foveal (AF) and the manually identified foveal (MF). The foveal detected by MF was set as the reference. Radial distances from SC and AF to MF were calculated. Subfields retinal thickness acquired from different groups using different foveal detection methods were compared by paired Student's t test for normal data or Wilcoxon-Mann-Whitney for non-normal data. RESULTS: For normal subjects, it is reliable determining the foveal by special tomography features. The means of the radial distance from the MF was (35.0 ± 23.0) µm in scan center group and was (6.0 ± 9.0) µm in automated center (t = -3.732, P < 0.05). The average CRT difference between the SC and MF groups was (7.4 ± 10.4) µm. The median difference between the AF and MF groups was 0 µm (0 to 3), which is significantly less than that between the SC and MF groups (Z = -2.956, P < 0.05). In subjects with macular edema, the means of the radial offset was (59.0 ± 26.0) µm in SC group and was (34.0 ± 23.0) µm in AF group (t = -4.555, P < 0.05). The average CRT difference between the SC and MF groups was (28.0 ± 36.0) µm, which was not significantly greater than the difference between the AF and MF groups (23.0 ± 31.0) µm in macular edema eyes. CONCLUSIONS: Post acquisition detection of foveal reduces the radial offset to the real fovea, and may improve the reproducibility and reliability of the central retinal thickness quantification using spectral domain OCT in normal subjects.

Functional recovery after intravitreal bevacizumab treatments for idiopathic choroidal neovascularization in young adults.

PURPOSE: To evaluate the morphology and functional recovery of the retina after treatment of idiopathic choroidal neovascularization using intravitreal injections of bevacizumab in young adults. METHODS: For this interventional case series, 20 eyes of 19 patients with idiopathic choroidal neovascularization were treated with multiple intravitreal injections of bevacizumab. Changes in best-corrected visual acuity before the treatment and at follow-up visits were recorded. Structural changes were evaluated using optical coherence tomography and functional changes were assessed using microperimetry. RESULTS: Twenty eyes were followed for 12 months after their first injection. The eyes underwent an average of 3.95 injections. All eyes had a stable or an improved vision. The mean logarithm of the minimum angle of resolution best-corrected visual acuity improved from 0.43 to 0.06 (Wilcoxon signed-ranks test, P < 0.005). Improvement in macular function was detected as early as 1 month after the treatment and lasted for at least 6 months. Microperimetry demonstrated that mean retinal sensitivities within the central 10° field (10.29 ± 5.12 dB) at baseline improved to 13.98 ± 3.96 dB at the last visit. Dense scotomas were found in 13 of the 20 eyes at baseline and 5 of the 20 eyes at the last visit. Stable fixation (6 of 20 at baseline) was found in 17 of the 20 eyes at the last visit. Twelve of the 14 eyes with either a predominantly eccentric or poor central fixation at baseline established central fixation at 12 months. No serious local or systemic complications were encountered. CONCLUSION: In young adults with idiopathic choroidal neovascularization, an improvement in visual acuity and macular function was detected after intravitreal injections of bevacizumab.