495-P: Comparing Detection of Diabetic Retinopathy by Three Artificial Intelligence Platforms in Subjects Not Known to Have Diabetes
DIABETES（2023）
Bahrain Center for Strategic International and Energy Studies
Abstract
Background: The glycemic threshold for the diagnosis of type 2 diabetes (T2D) was determined based upon the prevalence of moderate retinopathy; however, diabetes related complications may occur below that threshold. This study utilized three companies to analyse retinal images using their commercial software and returned data on absence or presence of features of any diabetic retinopathy (DR) on a per-eye basis. Methods: Ophthalmic retinal images from 11,449 subjects (right and left eyes for each, totalling 22,898 images) not known to have diabetes from the Qatar Biobank were subjected to assessment for detection of features of DR using three Conformitè Europëenne (CE)-marked independent commercial Artificial Intelligence (AI) software systems. Results: From 11,449 patients: •DR was recorded as absent in both eyes in 7,094 (62.0%) patients by all 3 methods (Test Negative at patient level).•DR was detected by 2 or 3 of the AI systems in at least one eye in a total of 2,408 (21.0%) patients (Test Positive at patient level). Of these, in 1,532 (13.4%) cases DR was detected in just one eye of any given patient and in 876 (7.6%) cases DR was detected in both eyes of a patient. •Overall, 1,947 (17.0%) patients had at least one eye reported as unassessable by 2 or 3 of the AI systems; hence, No Overall Result for DR features could be obtained for these patients. Results from the three AI companies differed markedly: 17.7%, 32.9% and 46.6% ranked Test-Positive results at patient level. Only 304 right eyes and 280 left eyes were positive for all 3 companies. Conclusion: This is the first direct comparison of three independent AI companies when simultaneously assessing retinal images from more than 10,000 individuals. It showed markedly differing prevalences of diabetic retinopathy between existing AI systems, indicating inconsistencies if wishing to apply these tools to detect retinopathy in subjects not already known to have diabetes, and may differ in patients with known diabetes. Disclosure A.E.Butler: None. E.S.Kilpatrick: None. S.C.Hunt: None. S.Atkin: None.
MoreTranslated text
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined