). Can Deep Learning Detect Esophageal Lesions In PET-CT Scans?
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Deep learning has shown significant promise in detecting esophageal lesions in PET-CT scans. Studies have utilized convolutional neural networks (CNNs) trained on PET and CT image segments to achieve high accuracy levels, exceeding 95% for PET data and over 90% for CT data . Additionally, a 3D-CNN model has been developed to predict esophageal cancer outcomes with acceptable accuracy, identifying tumors with aggressive behavior . Furthermore, deep learning-based noise reduction methods have been found to improve the detectability of small lesions in low-statistics PET images, enhancing lesion detectability in challenging scenarios . These findings collectively demonstrate the potential of deep learning in accurately detecting esophageal lesions in PET-CT scans, offering valuable tools for clinical practice and research.
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| Papers (4) | Insight |
|---|---|
01 Oct 2019 1 Citations | Yes, deep learning can detect esophageal lesions in PET-CT scans with over 95% accuracy using PET data and over 90% accuracy using CT data, as shown in the study. |
Open access•Journal Article 3 Citations | Deep learning can improve the detectability of small lesions in low-statistics PET images, including esophageal lesions, as shown in the study on lesion detectability using deep learning in oncology PET. |
| Yes, a Deep Convolutional Neural Network (CNN) trained with PET scans can predict esophageal cancer outcomes with acceptable accuracy, showcasing potential for detecting esophageal lesions in PET-CT scans. | |
15 Mar 2019 4 Citations | Yes, deep learning, specifically a 5-layer CNN, was trained to detect esophageal lesions in PET-CT scans with an average AUC of ~95%. |
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