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Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification
- Source :
- International journal of medical informatics. 132
- Publication Year :
- 2019
-
Abstract
- Background Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, which stays in the top causes of vision impairment and blindness. Therefore, precisely inspecting its progression enables the ophthalmologists to set up appropriate next-visit schedule and cost-effective treatment plans. In the literature, existing work only makes use of numerical attributes in Electronic Medical Records (EMR) for acquiring such kind of DR-oriented knowledge through conventional machine learning techniques, which require an exhaustive job of engineering most impactful risk factors. Objective In this paper, an approach of deep bimodal learning is introduced to leverage the performance of DR risk progression identification. Methods In particular, we further involve valuable clinical information of fundus photography in addition to the aforementioned systemic attributes. Accordingly, a Trilogy of Skip-connection Deep Networks, namely Tri-SDN, is proposed to exhaustively exploit underlying relationships between the baseline and follow-up information of the fundus images and EMR-based attributes. Besides that, we adopt Skip-Connection Blocks as basis components of the Tri-SDN for making the end-to-end flow of signals more efficient during feedforward and backpropagation processes. Results Through a 10-fold cross validation strategy on a private dataset of 96 diabetic mellitus patients, the proposed method attains superior performance over the conventional EMR-modality learning approach in terms of Accuracy (90.6%), Sensitivity (96.5%), Precision (88.7%), Specificity (82.1%), and Area Under Receiver Operating Characteristics (88.8%). Conclusions The experimental results show that the proposed Tri-SDN can combine features of different modalities (i.e., fundus images and EMR-based numerical risk factors) smoothly and effectively during training and testing processes, respectively. As a consequence, with impressive performance of DR risk progression recognition, the proposed approach is able to help the ophthalmologists properly decide follow-up schedule and subsequent treatment plans.
- Subjects :
- Schedule
020205 medical informatics
Computer science
Fundus Oculi
Health Informatics
02 engineering and technology
Machine learning
computer.software_genre
Machine Learning
03 medical and health sciences
0302 clinical medicine
Risk Factors
0202 electrical engineering, electronic engineering, information engineering
medicine
Image Processing, Computer-Assisted
Photography
Leverage (statistics)
Electronic Health Records
Humans
030212 general & internal medicine
Set (psychology)
Modalities
Diabetic Retinopathy
medicine.diagnostic_test
Receiver operating characteristic
business.industry
Fundus photography
Backpropagation
Identification (information)
ROC Curve
Artificial intelligence
Neural Networks, Computer
business
computer
Algorithms
Subjects
Details
- ISSN :
- 18728243
- Volume :
- 132
- Database :
- OpenAIRE
- Journal :
- International journal of medical informatics
- Accession number :
- edsair.doi.dedup.....6df0b9ec215942f07e485d61ae508b69