ArticleBiomedical optics express2019
Early diagnosis of gastric cancer based on deep learning combined with the spectral-spatial classification method.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04589884 (Intraoperative EXamination Using MAChine-learning-based HYperspectral for diagNosis & Autonomous Anatomy Assessment), which is not on this map. Cited by 15 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Intraoperative EXamination Using MAChine-learning-based HYperspectral for diagNosis & Autonomous Anatomy Assessment
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Clinical applications of hyperspectral imaging in gastroenterology and hepatology: A systematic review.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2026Pooled it
- A review on computer-aided diagnostic system to classify the disorders of the gastrointestinal tract.European journal of medical research · 2025Review
- Hyperspectral imaging and its applications: A review.Heliyon · 2024Review
- Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology.Sensors (Basel, Switzerland) · 2024Article
- A microfluidic approach for label-free identification of small-sized microplastics in seawater.Scientific reports · 2023Article
- Multiplex immunofluorescence and single-cell transcriptomic profiling reveal the spatial cell interaction networks in the non-small cell lung cancer microenvironment.Clinical and translational medicine · 2023Article
- Deep Learning in Medical Hyperspectral Images: A Review.Sensors (Basel, Switzerland) · 2022Review
- IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing.Sensors (Basel, Switzerland) · 2022Article
- New Intraoperative Imaging Tools and Image-Guided Surgery in Gastric Cancer Surgery.Diagnostics (Basel, Switzerland) · 2022Review
- Identification of gastric cancer with convolutional neural networks: a systematic review.Multimedia tools and applications · 2022Article
- Primary Investigation of Deep Learning Models for Japanese "Group Classification" of Whole-Slide Images of Gastric Endoscopic Biopsy.Computational and mathematical methods in medicine · 2022Article
- Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm.Multimedia tools and applications · 2022Article
- Deep Learning Analysis of In Vivo Hyperspectral Images for Automated Intraoperative Nerve Detection.Diagnostics (Basel, Switzerland) · 2021Article
- Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of an objective and rapid method that can be used for the early diagnosis of gastric cancer has important clinical application value. In this study, the fluorescence hyperspectral imaging technique was used to acquire fluorescence spectral images. Deep learning combined with spectral-spatial classification methods based on 120 fresh tissues samples that had a confirmed diagnosis by histopathological examinations was used to automatically identify and extract the "spectral + spatial" features to construct an early diagnosis model of gastric cancer. The model results showed that the overall accuracy for the nonprecancerous lesion, precancerous lesion, and gastric cancer groups was 96.5% with specificities of 96.0%, 97.3%, and 96.7% and sensitivities of 97.0%, 96.3%, and 96.6%, respectively. Therefore, the proposed method can increase the diagnostic accuracy and is expected to be a new method for the early diagnosis of gastric cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.