Summary of Deep Learning-based Noninvasive Screening Of Type 2 Diabetes with Chest X-ray Images and Electronic Health Records, by Sanjana Gundapaneni et al.
Deep Learning-Based Noninvasive Screening of Type 2 Diabetes with Chest X-ray Images and Electronic Health Records
by Sanjana Gundapaneni, Zhuo Zhi, Miguel Rodrigues
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to detecting type 2 diabetes mellitus (T2DM) using chest X-ray (CXR) images and other noninvasive data sources. By integrating CXR images with electronic health records (EHRs) and electrocardiography signals, the authors aim to create a more holistic understanding of patients’ health conditions. The study uses deep learning models, specifically two fusion paradigms: an early fusion-based multimodal transformer and a modular joint fusion ResNet-LSTM architecture. The results show that the end-to-end trained ResNet-LSTM model achieves an AUROC of 0.86, surpassing the CXR-only baseline by 2.3% with just 9863 training samples. This demonstrates the diagnostic value of CXRs within multimodal frameworks for identifying at-risk individuals early. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to find a way to detect type 2 diabetes earlier and better using X-ray images from the chest, electronic health records, and heartbeat signals. They test two ways to combine this information: one that looks at all the data together and another that uses separate parts of the model for different types of data. The best approach is able to identify people who might get type 2 diabetes with an accuracy of about 86%, which is better than just using X-ray images alone. |
Keywords
» Artificial intelligence » Deep learning » Lstm » Resnet » Transformer
