Instant automatic diagnosis of diabetic retinopathy
- URL: http://arxiv.org/abs/1906.11875v2
- Date: Mon, 26 Aug 2024 14:13:29 GMT
- Title: Instant automatic diagnosis of diabetic retinopathy
- Authors: Gwenolé Quellec, Mathieu Lamard, Bruno Lay, Alexandre Le Guilcher, Ali Erginay, Béatrice Cochener, Pascale Massin,
- Abstract summary: OphtAI relies on ensembles of convolutional neural networks trained to recognize eye laterality, detect referable DR and assess DR severity.
System was developed and validated using a dataset of 763,848 images from 164,660 screening procedures.
OphtAI is safer, faster and more comprehensive than the only AI system authorized by the FDA so far.
- Score: 36.2143325805188
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The purpose of this study is to evaluate the performance of the OphtAI system for the automatic detection of referable diabetic retinopathy (DR) and the automatic assessment of DR severity using color fundus photography. OphtAI relies on ensembles of convolutional neural networks trained to recognize eye laterality, detect referable DR and assess DR severity. The system can either process single images or full examination records. To document the automatic diagnoses, accurate heatmaps are generated. The system was developed and validated using a dataset of 763,848 images from 164,660 screening procedures from the OPHDIAT screening program. For comparison purposes, it was also evaluated in the public Messidor-2 dataset. Referable DR can be detected with an area under the ROC curve of AUC = 0.989 in the Messidor-2 dataset, using the University of Iowa's reference standard (95% CI: 0.984-0.994). This is better than the only AI system authorized by the FDA, evaluated in the exact same conditions (AUC = 0.980). OphtAI can also detect vision-threatening DR with an AUC of 0.997 (95% CI: 0.996-0.998) and proliferative DR with an AUC of 0.997 (95% CI: 0.995-0.999). The system runs in 0.3 seconds using a graphics processing unit and less than 2 seconds without. OphtAI is safer, faster and more comprehensive than the only AI system authorized by the FDA so far. Instant DR diagnosis is now possible, which is expected to streamline DR screening and to give easy access to DR screening to more diabetic patients.
Related papers
- Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction [47.7045293755736]
Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life.
This study demonstrates the potential of retinal optical coherence tomography ( OCT) imaging combined with fundus photographs for identifying future adverse cardiac events.
We propose a novel binary classification network based on a Multi-channel Variational Autoencoder (MCVAE), which learns a latent embedding of patients' fundus and OCT images to classify individuals into two groups: those likely to develop CVD in the future and those who are not.
arXiv Detail & Related papers (2024-10-18T12:37:51Z) - Improving Image Classification of Knee Radiographs: An Automated Image
Labeling Approach [0.3258500021481664]
The purpose of our study was to develop an automated labeling approach that improves the image classification to distinguish normal knee images with abnormalities or prior.
The automated labeler was trained on a small set of labeled data to automatically label a much larger set of unlabeled data.
Our findings demonstrated that the proposed automated labeling approach significantly improves the performance of image classification for knee diagnosis.
arXiv Detail & Related papers (2023-09-06T03:26:24Z) - Learning to diagnose cirrhosis from radiological and histological labels
with joint self and weakly-supervised pretraining strategies [62.840338941861134]
We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset.
We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis.
This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
arXiv Detail & Related papers (2023-02-16T17:06:23Z) - An Ensemble Method to Automatically Grade Diabetic Retinopathy with
Optical Coherence Tomography Angiography Images [4.640835690336653]
We propose an ensemble method to automatically grade Diabetic retinopathy (DR) images available from Diabetic Retinopathy Analysis Challenge (DRAC) 2022.
First, we adopt the state-of-the-art classification networks, and train them to grade UW- OCTA images with different splits of the available dataset.
Ultimately, we obtain 25 models, of which, the top 16 models are selected and ensembled to generate the final predictions.
arXiv Detail & Related papers (2022-12-12T22:06:47Z) - minoHealth.ai: A Clinical Evaluation Of Deep Learning Systems For the
Diagnosis of Pleural Effusion and Cardiomegaly In Ghana, Vietnam and the
United States of America [0.0]
We evaluate how well minoHealth.ai systems, developed my minoHealth AI Labs, will perform at diagnosing cardiomegaly and pleural effusion.
chest x-rays from Ghana, Vietnam and the USA, and how well AI systems will perform when compared with radiologists working in Ghana.
For cardiomegaly, minoHealth.ai systems scored Area under the Receiver operating characteristic Curve (AUC-ROC) of 0.9 and 0.97 while the AUC-ROC of individual radiologists ranged from 0.77 to 0.86.
arXiv Detail & Related papers (2022-10-31T20:12:41Z) - Deep Learning for Segmentation-based Hepatic Steatosis Detection on Open
Data: A Multicenter International Validation Study [5.117364766785943]
This three-step AI workflow consists of 3D liver segmentation, liver attenuation measurements, and hepatic steatosis detection.
The deep-learning segmentation achieved a mean coefficient of 0.957.
If adopted for universal detection, this deep learning system could potentially allow early non-invasive, non-pharmacological preventative interventions.
arXiv Detail & Related papers (2022-10-27T03:24:52Z) - Performance of a deep learning system for detection of referable
diabetic retinopathy in real clinical settings [0.0]
RetCAD v.1.3.1 was developed to automatically detect referable diabetic retinopathy (DR)
Analysed the reduction of workload that can be released incorporating this artificial intelligence-based technology.
arXiv Detail & Related papers (2022-05-11T14:59:10Z) - Building Brains: Subvolume Recombination for Data Augmentation in Large
Vessel Occlusion Detection [56.67577446132946]
A large training data set is required for a standard deep learning-based model to learn this strategy from data.
We propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres from different patients.
In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres.
arXiv Detail & Related papers (2022-05-05T10:31:57Z) - An Interpretable Multiple-Instance Approach for the Detection of
referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images.
By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy.
We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
arXiv Detail & Related papers (2021-03-02T13:14:15Z) - A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading,
and Transferability [76.64661091980531]
People with diabetes are at risk of developing diabetic retinopathy (DR)
Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading.
This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists.
arXiv Detail & Related papers (2020-08-22T07:48:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.