ArticleBioengineering (Basel, Switzerland)2025
Precision Imaging for Early Detection of Esophageal Cancer.
Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Advances in the Diagnosis of Barrett's Esophagus.Diagnostics (Basel, Switzerland) · 2026Review
- Machine learning applications in the detection and treatment of esophageal cancer.Discover oncology · 2026Review
- Hyperspectral reconstruction of white light endoscopy images for enhanced segmentation of early esophageal lesions in patients.iScience · 2026Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Liver Cancer: Artificial Intelligence (AI)-Based Integrated Therapeutic Approaches.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Oligometastatic Mixed Neuroendocrine Adenocarcinoma of the Esophago-Gastric Junction: A Case of Successful Multidisciplinary Management, the Lessons Learnt and Review of the Literature.Journal of clinical medicine · 2025Article
- Computed tomography features and clinicopathological characteristics of resectable esophageal sarcomatoid carcinoma: a retrospective study.Frontiers in oncology · 2025Article
- Feasibility study for proton dose calculation of esophageal squamous cell carcinoma based on stopping power ratio directly derived from dual energy CT.Frontiers in oncology · 2025Article
- Optimization strategies for ultrasound zoning of cervical lymph nodes in esophageal cancer: a precision medicine approach.Frontiers in oncology · 2025Review
- Deep learning-based time series prediction in multispectral and hyperspectral imaging for cancer detection.Frontiers in medicine · 2025Article
- Evaluation of Ki-67 expression levels in esophageal squamous cell carcinoma using dual-energy CT quantitative parameters.Frontiers in oncology · 2025Article
- Advancements in real-time oncology diagnosis: harnessing AI and image fusion techniques.Frontiers in oncology · 2025Review
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Authors and funding
8 authors.
Funding
Abstract
Early detection of early-stage esophageal cancer (ECA) is crucial for timely intervention and improved treatment outcomes. Hyperspectral imaging (HSI) and artificial intelligence (AI) technologies offer promising avenues for enhancing diagnostic accuracy in this context. This study utilized a dataset comprising 3984 white light images (WLIs) and 3666 narrow-band images (NBIs). We employed the Yolov5 model, a state-of-the-art object detection algorithm, to predict early ECA based on the provided images. The dataset was divided into two subsets: RGB-WLIs and NBIs, and four distinct models were trained using these datasets. The experimental results revealed that the prediction performance of the training model was notably enhanced when using HSI compared to general NBI training. The HSI training model demonstrated an 8% improvement in accuracy, along with a 5-8% enhancement in precision and recall measures. Notably, the model trained with WLIs exhibited the most significant improvement. Integration of HSI with AI technologies improves the prediction performance for early ECA detection. This study underscores the potential of deep learning identification models to aid in medical detection research. Integrating these models with endoscopic diagnostic systems in healthcare settings could offer faster and more accurate results, thereby improving overall detection performance.
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Registered trials
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