Summary of Detecting Underdiagnosed Medical Conditions with Deep Learning-based Opportunistic Ct Imaging, by Asad Aali et al.
Detecting Underdiagnosed Medical Conditions with Deep Learning-Based Opportunistic CT Imaging
by Asad Aali, Andrew Johnston, Louis Blankemeier, Dave Van Veen, Laura T Derry, David Svec, Jason Hom, Robert D. Boutin, Akshay S. Chaudhari
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study leverages deep learning techniques to improve diagnosis and clinical documentation in abdominal computed tomography (CT) scans. Specifically, it repurposes routine CT images for detecting underdiagnosed conditions like sarcopenia, hepatic steatosis, and ascites through opportunistic CT analysis. The researchers analyze 2,674 inpatient CT scans to identify discrepancies between imaging phenotypes and radiology reports. They find that only a small percentage of scans diagnosed with these conditions were accurately ICD-coded. By demonstrating the potential of opportunistic CT for enhancing diagnostic precision and accuracy, this study offers advancements in precision medicine. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses computers to look at CT scan images to help doctors diagnose certain medical conditions better. They took 2,674 CT scans and looked at what the images showed compared to what the doctors wrote down about each patient. What they found is that many times, the doctors didn’t correctly write down what the CT scans were showing. This matters because it can help doctors be more accurate when diagnosing patients and making treatment plans. |
Keywords
» Artificial intelligence » Deep learning » Precision
